| 1. |
- Ahtiainen, Juhana, et al.
(författare)
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Normal Distributions Transform Traversability Maps : LIDAR-Only Approach for Traversability Mapping in Outdoor Environments
- 2017
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Ingår i: Journal of Field Robotics. - : John Wiley & Sons. - 1556-4959 .- 1556-4967. ; 34:3, s. 600-621
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Tidskriftsartikel (refereegranskat)abstract
- Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.
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| 2. |
- Almqvist, Håkan, 1984-, et al.
(författare)
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Improving Point-Cloud Accuracy from a Moving Platform in Field Operations
- 2013
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Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE conference proceedings. - 9781467356411 - 9781467356435 ; , s. 733-738
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.
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| 3. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Autonomous transport vehicles : where we are and what is missing
- 2015
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Ingår i: IEEE robotics & automation magazine. - 1070-9932 .- 1558-223X. ; 22:1, s. 64-75
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.
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| 4. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Drive the Drive : From Discrete Motion Plans to Smooth Drivable Trajectories
- 2014
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Ingår i: Robotics. - Basel, Switzerland : M D P I AG. - 2218-6581. ; 3:4, s. 400-416
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Tidskriftsartikel (refereegranskat)abstract
- Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation).
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| 5. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Fast, continuous state path smoothing to improve navigation accuracy
- 2015
-
Ingår i: IEEE International Conference on Robotics and Automation (ICRA), 2015. - : IEEE Computer Society. - 9781479969234 ; , s. 662-669
-
Konferensbidrag (refereegranskat)abstract
- Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.
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| 6. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
- 2017
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Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 - 9781538626832 ; , s. 1389-1395
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Konferensbidrag (refereegranskat)abstract
- Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.
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| 7. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Real time registration of RGB-D data using local visual features and 3D-NDT registration
- 2012
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Ingår i: Proc. of International Conference on Robotics and Automation (ICRA) Workshop on Semantic Perception, Mapping and Exploration (SPME). - : IEEE. - 9781467314039
-
Konferensbidrag (refereegranskat)abstract
- Recent increased popularity of RGB-D capable sensors in robotics has resulted in a surge of related RGBD registration methods. This paper presents several RGB-D registration algorithms based on combinations between local visual feature and geometric registration. Fast and accurate transformation refinement is obtained by using a recently proposed geometric registration algorithm, based on the Three-Dimensional Normal Distributions Transform (3D-NDT). Results obtained on standard data sets have demonstrated mean translational errors on the order of 1 cm and rotational errors bellow 1 degree, at frame processing rates of about 15 Hz.
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| 8. |
- Bennetts, Victor Hernandez, 1980-, et al.
(författare)
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Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments
- 2014
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Ingår i: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE conference proceedings. - 9781479936854 ; , s. 6362-6367
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Konferensbidrag (refereegranskat)abstract
- In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.
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| 9. |
- Birk, Andreas, et al.
(författare)
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Planetary Exploration in USARSim : A Case Study including Real World Data from Mars
- 2009
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Ingår i: RoboCup 2008. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642029202 - 3642029205 ; , s. 463-472
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Konferensbidrag (refereegranskat)abstract
- Intelligent Mobile Robots are increasingly used in unstructured domains; one particularly challenging example for this is, planetary exploration. The preparation of according missions is highly non-trivial, especially as it is difficult to carry out realistic experiments without, very sophisticated infrastructures. In this paper, we argue that, the, Unified System for Automation and Robot Simulation (USARSim) offers interesting opportunities for research on planetary exploration by mobile robots. With the example of work on terrain classification, it, is shown how synthetic as well as real world data, from Mars call be used to test an algorithm's performance in USARSim. Concretely, experiments with an algorithm for the detection of negotiable ground oil a, planetary surface are presented. It is shown that the approach performs fast; and robust on planetary surfaces.
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| 10. |
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| 11. |
- Canelhas, Daniel Ricão, et al.
(författare)
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A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry
- 2018
-
Ingår i: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). - : IEEE Computer Society. ; , s. 6337-6343
-
Konferensbidrag (refereegranskat)abstract
- Voxel volumes are simple to implement and lend themselves to many of the tools and algorithms available for 2D images. However, the additional dimension of voxels may be costly to manage in memory when mapping large spaces at high resolutions. While lowering the resolution and using interpolation is common work-around, in the literature we often find that authors either use trilinear interpolation or nearest neighbors and rarely any of the intermediate options. This paper presents a survey of geometric interpolation methods for voxel-based map representations. In particular we study the truncated signed distance field (TSDF) and the impact of using fewer than 8 samples to perform interpolation within a depth-camera pose tracking and mapping scenario. We find that lowering the number of samples fetched to perform the interpolation results in performance similar to the commonly used trilinear interpolation method, but leads to higher framerates. We also report that lower bit-depth generally leads to performance degradation, though not as much as may be expected, with voxels containing as few as 3 bits sometimes resulting in adequate estimation of camera trajectories.
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| 12. |
- Canelhas, Daniel R., 1983-, et al.
(författare)
-
Compressed Voxel-Based Mapping Using Unsupervised Learning
- 2017
-
Ingår i: Robotics. - Basel, Switzerland : MDPI AG. - 2218-6581. ; 6:3
-
Tidskriftsartikel (refereegranskat)abstract
- In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.
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| 13. |
- Canelhas, Daniel R., 1983-, et al.
(författare)
-
From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs
- 2016
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Ingår i: IEEE Robotics and Automation Letters. - Piscataway, USA : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 1:2, s. 1148-1155
-
Tidskriftsartikel (refereegranskat)abstract
- With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies.
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| 14. |
- Canelhas, Daniel R., 1983-, et al.
(författare)
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Improved local shape feature stability through dense model tracking
- 2013
-
Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. - 9781467363587 ; , s. 3203-3209
-
Konferensbidrag (refereegranskat)abstract
- In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability
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| 15. |
- Canelhas, Daniel R., 1983-, et al.
(författare)
-
SDF tracker : a parallel algorithm for on-line pose estimation and scene reconstruction from depth images
- 2013
-
Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. - 9781467363587 ; , s. 3671-3676
-
Konferensbidrag (refereegranskat)abstract
- Ego-motion estimation and environment mapping are two recurring problems in the field of robotics. In this work we propose a simple on-line method for tracking the pose of a depth camera in six degrees of freedom and simultaneously maintaining an updated 3D map, represented as a truncated signed distance function. The distance function representation implicitly encodes surfaces in 3D-space and is used directly to define a cost function for accurate registration of new data. The proposed algorithm is highly parallel and achieves good accuracy compared to state of the art methods. It is suitable for reconstructing single household items, workspace environments and small rooms at near real-time rates, making it practical for use on modern CPU hardware
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| 16. |
- Canelhas, Daniel Ricão, 1983-
(författare)
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Truncated Signed Distance Fields Applied To Robotics
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is concerned with topics related to dense mapping of large scale three-dimensional spaces. In particular, the motivating scenario of this work is one in which a mobile robot with limited computational resources explores an unknown environment using a depth-camera. To this end, low-level topics such as sensor noise, map representation, interpolation, bit-rates, compression are investigated, and their impacts on more complex tasks, such as feature detection and description, camera-tracking, and mapping are evaluated thoroughly. A central idea of this thesis is the use of truncated signed distance fields (TSDF) as a map representation and a comprehensive yet accessible treatise on this subject is the first major contribution of this dissertation. The TSDF is a voxel-based representation of 3D space that enables dense mapping with high surface quality and robustness to sensor noise, making it a good candidate for use in grasping, manipulation and collision avoidance scenarios.The second main contribution of this thesis deals with the way in which information can be efficiently encoded in TSDF maps. The redundant way in which voxels represent continuous surfaces and empty space is one of the main impediments to applying TSDF representations to large-scale mapping. This thesis proposes two algorithms for enabling large-scale 3D tracking and mapping: a fast on-the-fly compression method based on unsupervised learning, and a parallel algorithm for lifting a sparse scene-graph representation from the dense 3D map.The third major contribution of this work consists of thorough evaluations of the impacts of low-level choices on higher-level tasks. Examples of these are the relationships between gradient estimation methods and feature detector repeatability, voxel bit-rate, interpolation strategy and compression ratio on camera tracking performance. Each evaluation thus leads to a better understanding of the trade-offs involved, which translate to direct recommendations for future applications, depending on their particular resource constraints.
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| 17. |
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| 18. |
- Charusta, Krzysztof, 1983-, et al.
(författare)
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Generation of independent contact regions on objects reconstructed from noisy real-world range data
- 2012
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Ingår i: 2012 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE conference proceedings. - 9781467314053 - 9781467314039 ; , s. 1338-1344
-
Konferensbidrag (refereegranskat)abstract
- The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.
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| 19. |
- Della Corte, Bartolomeo, et al.
(författare)
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Unified Motion-Based Calibration of Mobile Multi-Sensor Platforms With Time Delay Estimation
- 2019
-
Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 4:2, s. 902-909
-
Tidskriftsartikel (refereegranskat)abstract
- The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it, and their time delays. In this letter, we present a unified pipeline for motion-based calibration of mobile platforms equipped with multiple heterogeneous sensors. We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters, and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value. We release an open-source version of our framework to the community.
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| 20. |
- Dominguez, David Caceres, 1993-, et al.
(författare)
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A Stack-of-Tasks Approach Combined With Behavior Trees : A New Framework for Robot Control
- 2022
-
Ingår i: IEEE Robotics and Automation Letters. - : IEEE Press. - 2377-3766. ; 7:4, s. 12110-12117
-
Tidskriftsartikel (refereegranskat)abstract
- Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs.
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| 21. |
- Ferri, Gabriele, et al.
(författare)
-
DustCart, a Mobile Robot for Urban Environments : Experiments of Pollution Monitoring and Mapping during Autonomous Navigation in Urban Scenarios
- 2010
-
Ingår i: Proceedings of ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments.
-
Konferensbidrag (refereegranskat)abstract
- In the framework of DustBot European project, aimed at developing a new multi-robot system for urban hygiene management, we have developed a twowheeled robot: DustCart. DustCart aims at providing a solution to door-to-door garbage collection: the robot, called by a user, navigates autonomously to his/her house; collects the garbage from the user and discharges it in an apposite area. An additional feature of DustCart is the capability to monitor the air pollution by means of an on board Air Monitoring Module (AMM). The AMM integrates sensors to monitor several atmospheric pollutants, such as carbon monoxide (CO), particular matter (PM10), nitrogen dioxide (NO2), ozone (O3) plus temperature (T) and relative humidity (rHu). An Ambient Intelligence platform (AmI) manages the robots’ operations through a wireless connection. AmI is able to collect measurements taken by different robots and to process them to create a pollution distribution map. In this paper we describe the DustCart robot system, focusing on the AMM and on the process of creating the pollutant distribution maps. We report results of experiments of one DustCart robot moving in urban scenarios and producing gas distribution maps using the Kernel DM+V algorithm. These experiments can be considered as one of the first attempts to use robots as mobile monitoring devices that can complement the traditional fixed stations.
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| 22. |
- Gabellieri, Chiara, et al.
(författare)
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Towards an Autonomous Unwrapping System for Intralogistics
- 2019
-
Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 4:4, s. 4603-4610
-
Tidskriftsartikel (refereegranskat)abstract
- Warehouse logistics is a rapidly growing market for robots. However, one key procedure that has not received much attention is the unwrapping of pallets to prepare them for objects picking. In fact, to prevent the goods from falling and to protect them, pallets are normally wrapped in plastic when they enter the warehouse. Currently, unwrapping is mainly performed by human operators, due to the complexity of its planning and control phases. Autonomous solutions exist, but usually they are designed for specific situations, require a large footprint and are characterized by low flexibility. In this work, we propose a novel integrated robotic solution for autonomous plastic film removal relying on an impedance-controlled robot. The main contribution is twofold: on one side, a strategy to plan Cartesian impedance and trajectory to execute the cut without damaging the goods is discussed; on the other side, we present a cutting device that we designed for this purpose. The proposed solution presents the characteristics of high versatility and the need for a reduced footprint, due to the adopted technologies and the integration with a mobile base. Experimental results are shown to validate the proposed approach.
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| 23. |
- Gugliermo, Simona, 1995-, et al.
(författare)
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Evaluating behavior trees
- 2024
-
Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 178
-
Tidskriftsartikel (refereegranskat)abstract
- Behavior trees (BTs) are increasingly popular in the robotics community. Yet in the growing body of published work on this topic, there is a lack of consensus on what to measure and how to quantify BTs when reporting results. This is not only due to the lack of standardized measures, but due to the sometimes ambiguous use of definitions to describe BT properties. This work provides a comprehensive overview of BT properties the community is interested in, how they relate to each other, the metrics currently used to measure BTs, and whether the metrics appropriately quantify those properties of interest. Finally, we provide the practitioner with a set of metrics to measure, as well as insights into the properties that can be derived from those metrics. By providing this holistic view of properties and their corresponding evaluation metrics, we hope to improve clarity when using BTs in robotics. This more systematic approach will make reported results more consistent and comparable when evaluating BTs.
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| 24. |
- Güler, Püren, et al.
(författare)
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Visual state estimation in unseen environments through domain adaptation and metric learning
- 2022
-
Ingår i: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- In robotics, deep learning models are used in many visual perception applications, including the tracking, detection and pose estimation of robotic manipulators. The state of the art methods however are conditioned on the availability of annotated training data, which may in practice be costly or even impossible to collect. Domain augmentation is one popular method to improve generalization to out-of-domain data by extending the training data set with predefined sources of variation, unrelated to the primary task. While this typically results in better performance on the target domain, it is not always clear that the trained models are capable to accurately separate the signals relevant to solving the task (e.g., appearance of an object of interest) from those associated with differences between the domains (e.g., lighting conditions). In this work we propose to improve the generalization capabilities of models trained with domain augmentation by formulating a secondary structured metric-space learning objective. We concentrate on one particularly challenging domain transfer task-visual state estimation for an articulated underground mining machine-and demonstrate the benefits of imposing structure on the encoding space. Our results indicate that the proposed method has the potential to transfer feature embeddings learned on the source domain, through a suitably designed augmentation procedure, and on to an unseen target domain.
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| 25. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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Robot assisted gas tomography : an alternative approach for the detection of fugitive methane emissions
- 2014
-
Ingår i: Workshop on Robot Monitoring.
-
Konferensbidrag (refereegranskat)abstract
- Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements.
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| 26. |
- Hoang, Dinh-Cuong, 1991-, et al.
(författare)
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Context-Aware Grasp Generation in Cluttered Scenes
- 2022
-
Ingår i: 2022 International Conference on Robotics and Automation (ICRA). - : IEEE. - 9781728196824 - 9781728196817 ; , s. 1492-1498
-
Konferensbidrag (refereegranskat)abstract
- Conventional methods to autonomous grasping rely on a pre-computed database with known objects to synthesize grasps, which is not possible for novel objects. On the other hand, recently proposed deep learning-based approaches have demonstrated the ability to generalize grasp for unknown objects. However, grasp generation still remains a challenging problem, especially in cluttered environments under partial occlusion. In this work, we propose an end-to-end deep learning approach for generating 6-DOF collision-free grasps given a 3D scene point cloud. To build robustness to occlusion, the proposed model generates candidates by casting votes and accumulating evidence for feasible grasp configurations. We exploit contextual information by encoding the dependency of objects in the scene into features to boost the performance of grasp generation. The contextual information enables our model to increase the likelihood that the generated grasps are collision-free. Our experimental results confirm that the proposed system performs favorably in terms of predicting object grasps in cluttered environments in comparison to the current state of the art methods.
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| 27. |
- Hoang, Dinh-Cuong, 1991-, et al.
(författare)
-
Object-RPE : Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks for Warehouse Robots
- 2019
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Ingår i: 2019 European Conference on Mobile Robots, ECMR 2019. - : IEEE. - 9781728136059
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Konferensbidrag (refereegranskat)abstract
- We present a system for accurate 3D instance-aware semantic reconstruction and 6D pose estimation, using an RGB-D camera. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. The method presented in this paper extends a high-quality instance-aware semantic 3D Mapping system from previous work [1] by adding a 6D object pose estimator. While the main trend in CNN-based 6D pose estimation has been to infer object's position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality object-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through experimental validation on the YCB-Video dataset and a newly collected warehouse object dataset. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe.
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| 28. |
- Hoang, Dinh-Cuong, 1991-, et al.
(författare)
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Object-RPE : Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks
- 2020
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Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 133
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Tidskriftsartikel (refereegranskat)abstract
- We present an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D instance-aware semantic reconstruction. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping(SLAM) system, ElasticFusion [1], to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. We leverage the pipeline of ElasticFusion as a back-bone and propose a joint geometric and photometric error function with per-pixel adaptive weights. While the main trend in CNN-based 6D pose estimation has been to infer an object’s position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality instance-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through extensive experiments on different datasets. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe.
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| 29. |
- Hoang, Dinh-Cuong, 1991-, et al.
(författare)
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Panoptic 3D Mapping and Object Pose Estimation Using Adaptively Weighted Semantic Information
- 2020
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 5:2, s. 1962-1969
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Tidskriftsartikel (refereegranskat)abstract
- We present a system capable of reconstructing highly detailed object-level models and estimating the 6D pose of objects by means of an RGB-D camera. In this work, we integrate deep-learning-based semantic segmentation, instance segmentation, and 6D object pose estimation into a state of the art RGB-D mapping system. We leverage the pipeline of ElasticFusion as a backbone and propose modifications of the registration cost function to make full use of the semantic class labels in the process. The proposed objective function features tunable weights for the depth, appearance, and semantic information channels, which are learned from data. A fast semantic segmentation and registration weight prediction convolutional neural network (Fast-RGBD-SSWP) suited to efficient computation is introduced. In addition, our approach explores performing 6D object pose estimation from multiple viewpoints supported by the high-quality reconstruction system. The developed method has been verified through experimental validation on the YCB-Video dataset and a dataset of warehouse objects. Our results confirm that the proposed system performs favorably in terms of surface reconstruction, segmentation quality, and accurate object pose estimation in comparison to other state-of-the-art systems. Our code and video are available at https://sites.google.com/view/panoptic-mope.
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| 30. |
- Hoang, Dinh-Cuong, 1991-
(författare)
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Vision-based Perception For Autonomous Robotic Manipulation
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In order to safely and effectively operate in real-world unstructured environments where a priori knowledge of the surroundings is not available, robots must have adequate perceptual capabilities. This thesis is concerned with several important aspects of vision-based perception for autonomous robotic manipulation. With a focus on topics related to scene reconstruction, object pose estimation and grasp configuration generation, we aim at helping robots to better understand their surroundings, to avoid undesirable contacts with the environment and to accurately grasp selected objects.With the wide availability of affordable RGB-D cameras, research on visual SLAM (Simultaneous Localization and Mapping) or scene reconstruction has made giant strides in development. As a key element of an RGB-D reconstruction system, a large number of registration algorithms have been proposed in the context of RGB-D Tracking and Mapping (TAM). The state-of-the-art methods rely on color and depth information to track camera poses. Besides depth and color images, semantic information is now often available due to the advancement of image segmentation driven by deep learning. We are interested to explore to what extent the use of semantic cues can increase the robustness of camera pose tracking. This leads to the first contribution of this dissertation. A method for reliable camera tracking using an objective function that combines geometric, appearance, and semantic cues with adaptive weights.Beyond the purely geometric model of the environment produced by classical reconstruction systems, the inclusion of rich semantic information and 6D poses of object instances within a dense map is useful for robots to effectively operate and interact with objects. Therefore, the second contribution of this thesis is an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D semantic reconstruction. Our framework deploys simultaneously a 3D mapping algorithm to reconstruct a semantic model of the environment, and an incremental 6D object pose recovery algorithm that carries out predictions using the reconstructed model. We demonstrate that we can exploit multiple viewpoints around the same object to achieve robust and stable 6D pose estimation in the presence of heavy clutter and occlusion.The methods taking RGB-D images as input have achieved state-of-the-art performance on the object pose estimation task. However, in a number of cases, color information may not be available — for example, when the input is point cloud data from laser range finders or industrial high-resolution 3D sensors. Therefore, besides methods using RGB-D images, studies on recovering the 6D pose of rigid objects from 3D point clouds containing only geometric information are necessary. The third contribution of this dissertation is a novel deep learning architecture to address the problem of estimating the 6D pose of multiple rigid objects in a cluttered scene, using only a 3D point cloud of the scene as an input. The proposed architecture pools geometric features together using a self-attention mechanism and adopts a deep Hough voting scheme for pose proposal generation. We show that by exploiting the correlation between poses of object instances and object parts we can improve the performance of object pose estimation.By applying a 6D object pose estimation algorithm, robots can perform grasping known objects where the 3D model of objects is available and a grasp database is pre-defined. What if we want to grasp novel objects? The fourth contribution of this thesis is a method for robust manipulation of novel objects in cluttered environments. we develop an end-to-end deep learning approach for generating grasp configurations for a two-finger parallel jaw gripper, based on 3D point cloud observations of the scene. The proposed model generates candidates by casting votes to accumulate evidence for feasible grasp configurations. We exploit contextual information by encoding the dependency of objects in the scene into features to boost the performance of grasp generation.
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| 31. |
- Hoang, Dinh-Cuong, et al.
(författare)
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Voting and Attention-Based Pose Relation Learning for Object Pose Estimation From 3D Point Clouds
- 2022
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 7:4, s. 8980-8987
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Tidskriftsartikel (refereegranskat)abstract
- Estimating the 6DOF pose of objects is an important function in many applications, such as robot manipulation or augmented reality. However, accurate and fast pose estimation from 3D point clouds is challenging, because of the complexity of object shapes, measurement noise, and presence of occlusions. We address this challenging task using an end-to-end learning approach for object pose estimation given a raw point cloud input. Our architecture pools geometric features together using a self-attention mechanism and adopts a deep Hough voting scheme for pose proposal generation. To build robustness to occlusion, the proposed network generates candidates by casting votes and accumulating evidence for object locations. Specifically, our model learns higher-level features by leveraging the dependency of object parts and object instances, thereby boosting the performance of object pose estimation. Our experiments show that our method outperforms state-of-the-art approaches in public benchmarks including the Sileane dataset 135 and the Fraunhofer IPA dataset [36]. We also deploy our proposed method to a real robot pick-and-place based on the estimated pose.
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| 32. |
- Iannotta, Marco, 1993-, et al.
(författare)
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Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees
- 2022
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Ingår i: IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence (MOMA): Challenges and Opportunities.
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Konferensbidrag (refereegranskat)abstract
- Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this work we extend our framework based on Behavior Trees for the control of a redundant mechanical system to the problem of commanding more complex systems that involve multiple low-level controllers. This allows the integrated systems to achieve non-trivial goals that require coordination among the sub-systems.
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| 33. |
- Ivan, Jean-Paul A., 1995-, et al.
(författare)
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Online Distance Field Priors for Gaussian Process Implicit Surfaces
- 2022
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 7:4, s. 8996-9003
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Tidskriftsartikel (refereegranskat)abstract
- Gaussian process (GP) implicit surface models provide environment and object representations which elegantly address noise and uncertainty while remaining sufficiently flexible to capture complex geometry. However, GP models quickly become intractable as the size of the observation set grows-a trait which is difficult to reconcile with the rate at which modern range sensors produce data. Furthermore, naive applications of GPs to implicit surface models allocate model resources uniformly, thus using precious resources to capture simple geometry. In contrast to prior work addressing these challenges though model sparsification, spatial partitioning, or ad-hoc filtering, we propose introducing model bias online through the GP's mean function. We achieve more accurate distance fields using smaller models by creating a distance field prior from features which are easy to extract and have analytic distance fields. In particular, we demonstrate this approach using linear features. We show the proposed distance field halves model size in a 2D mapping task using data from a SICK S300 sensor. When applied to a single 3D scene from the TUM RGB-D SLAM dataset, we achieve a fivefold reduction in model size. Our proposed prior results in more accurate GP implicit surfaces, while allowing existing models to function in larger environments or with larger spatial partitions due to reduced model size.
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| 34. |
- Krug, Robert, 1981-, et al.
(författare)
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Grasp Envelopes for Constraint-based Robot Motion Planning and Control
- 2015
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Ingår i: Robotics: Science and Systems Conference.
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Konferensbidrag (refereegranskat)abstract
- We suggest a grasp represen-tation in form of a set of enveloping spatial constraints. Our representation transforms the grasp synthesisproblem (i. e., the question of where to position the graspingdevice) from finding a suitable discrete manipulator wrist pose to finding a suitable pose manifold. Also the correspondingmotion planning and execution problem is relaxed – insteadof transitioning the wrist to a discrete pose, it is enough tomove it anywhere within the grasp envelope which allows toexploit kinematic redundancy.
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| 35. |
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| 36. |
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| 37. |
- Krug, Robert, 1981-, et al.
(författare)
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The Next Step in Robot Commissioning : Autonomous Picking and Palletizing
- 2016
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Ingår i: IEEE Robotics and Automation Letters. - Piscataway, USA : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 1:1, s. 546-553
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Tidskriftsartikel (refereegranskat)abstract
- So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.
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| 38. |
- Krug, Robert, 1981-, et al.
(författare)
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Velvet fingers : grasp planning and execution for an underactuated gripper with active surfaces
- 2014
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Ingår i: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). - : IEEE conference proceedings. - 9781479936854 ; , s. 3669-3675
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Konferensbidrag (refereegranskat)abstract
- In this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore,we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is ‘pulled-in’ towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario.
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| 39. |
- Lundell, Jens, et al.
(författare)
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Safe-To-Explore State Spaces : Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
- 2018
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Ingår i: IEEE-RAS Conference on Humanoid Robots. - : IEEE. ; , s. 132-138, s. 132-138
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Konferensbidrag (refereegranskat)abstract
- Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.
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| 40. |
- Magnusson, Martin, 1977-, et al.
(författare)
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Beyond points : Evaluating recent 3D scan-matching algorithms
- 2015
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Ingår i: 2015 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE conference proceedings. - 9781479969234 ; , s. 3631-3637
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Konferensbidrag (refereegranskat)abstract
- Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations.
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| 41. |
- Mojtahedzadeh, Rasoul, 1977-, et al.
(författare)
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Application Based 3D Sensor Evaluation : A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers
- 2013
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Ingår i: Proceedings of the European Conference on Mobile Robots (ECMR). - : IEEE conference proceedings. ; , s. 313-318
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A fundamental task in the design process of a complex system that requires 3D visual perception is the choice of suitable 3D range sensors. Identifying the utility of 3D range sensors in an industrial application solely based on an evaluation of their distance accuracy and the noise level may lead to an inappropriate selection. To assess the actual effect on the performance of the system as a whole requires a more involved analysis. In this paper, we examine the problem of selecting a set of 3D range sensors when designing autonomous systems for specific industrial applications in a holistic manner. As an instance of this problem we present a case study with an experimental evaluation of the utility of four 3D range sensors for object pose estimation in the process of automation of unloading containers.
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| 42. |
- Mojtahedzadeh, Rasoul, 1977-
(författare)
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Safe Robotic Manipulation to Extract Objects from Piles : From 3D Perception to Object Selection
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is concerned with the task of autonomous selection of objects to remove (unload) them from a pile in robotic manipulation systems. Applications such as the automation of logistics processes and service robots require an ability to autonomously manipulate objects in the environment. A collapse of a pile of objects due to an inappropriate choice of the object to be removed from the pile cannot be afforded for an autonomous robotic manipulation system. This dissertation presents an indepth analysis of the problem and proposes methods and algorithms to empower robotic manipulation systems to select a safe object from a pile elaborately and autonomously.The contributions presented in this thesis are three-fold. First, a set of algorithms is proposed for extracting a minimal set of high level symbolic relations, namely, gravitational act and support relations, of physical interactions between objects composing a pile. The symbolic relations, extracted by a geometrical reasoning method and a static equilibrium analysis can be readily used by AI paradigms to analyze the stability of a pile and reason about the safest set of objects to be removed. Considering the problem of undetected objects and the uncertainty in the estimated poses as they exist in realistic perception systems, a probabilistic approach is proposed to extract the support relations and to make a probabilistic decision about the set of safest objects using notions from machine learning and decision theory. Second, an efficient search based algorithm is proposed in an internal representation to automatically resolve the inter-penetrations between the shapes of objects due to errors in the poses estimated by an existing object detection module. Refining the poses by resolving the inter-penetrations results in a geometrically consistent model of the environment, and was found to reduce the overall pose error of the objects. This dissertation presents the concept of minimum translation search for object pose refinement and discusses a discrete search paradigm based on the concept of depth of penetration between two polyhedrons. Third, an application centric evaluation of ranging sensors for selecting a set of appropriate sensors for the task of object detection in the design process of a real-world robotics manipulation system is presented. The performance of the proposed algorithms are tested on data sets generated in simulation and from real-world scenarios.
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| 43. |
- Nevatia, Yashodhan, et al.
(författare)
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Augmented Autonomy : Improving human-robot team performance in Urban Search and Rescue
- 2008
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Ingår i: 2008 IEEE/RSJ International Conference on Robots and Intelligent Systems, vols 1-3, conference proceedings. - New York : IEEE Robotics and Automation Society. - 9781424420575 - 9781424420582 ; , s. 2103-2108
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Konferensbidrag (refereegranskat)abstract
- Exploration of unknown environments remains one of the fundamental problems of mobile robotics. It is also a prime example for a task that can benefit significantly from multi-robot teams. We present an integrated system for semi-autonomous cooperative exploration, augmented by an intuitive user interface for efficient human supervision and control. In this preliminary study we demonstrate the effectiveness of the system as a whole and the intuitive interface in particular. Congruent with previous findings, results confirm that having a human in the loop improves task performance, especially with larger numbers of robots. Specific to our interface, we find that even untrained operators can efficiently manage a decently sized team of robots.
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| 44. |
- Pfingsthorn, Max, et al.
(författare)
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Towards Cooperative and Decentralized Mapping in the Jacobs Virtual Rescue Team
- 2009
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Ingår i: RoboCup 2008. - Berlin, Heidelberg : Springer Berlin / Heidelberg. ; , s. 225-234
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The task of mapping and exploring an unknown environment remains one of the fundamental problems of mobile robotics. It is a task that can intuitively benefit significantly from a multi-robot approach. In this paper, we describe the design of the multi-robot mapping system used in the Jacobs Virtual Rescue team. The team competed in the World Cup 2007 and won the second place. It is shown how the recently proposed pose graph map representation facilitates not only map merging but also allows transmitting map updates efficiently
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| 45. |
- Rietz, Finn, 1995-, et al.
(författare)
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Hierarchical goals contextualize local reward decomposition explanations
- 2023
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Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:23, s. 16693-16704
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Tidskriftsartikel (refereegranskat)abstract
- One-step reinforcement learning explanation methods account for individual actions but fail to consider the agent's future behavior, which can make their interpretation ambiguous. We propose to address this limitation by providing hierarchical goals as context for one-step explanations. By considering the current hierarchical goal as a context, one-step explanations can be interpreted with higher certainty, as the agent's future behavior is more predictable. We combine reward decomposition with hierarchical reinforcement learning into a novel explainable reinforcement learning framework, which yields more interpretable, goal-contextualized one-step explanations. With a qualitative analysis of one-step reward decomposition explanations, we first show that their interpretability is indeed limited in scenarios with multiple, different optimal policies-a characteristic shared by other one-step explanation methods. Then, we show that our framework retains high interpretability in such cases, as the hierarchical goal can be considered as context for the explanation. To the best of our knowledge, our work is the first to investigate hierarchical goals not as an explanation directly but as additional context for one-step reinforcement learning explanations.
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| 46. |
- Rietz, Finn, 1995-, et al.
(författare)
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Towards Task-Prioritized Policy Composition
- 2022
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Konferensbidrag (refereegranskat)abstract
- Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer. In control theory, prioritized composition is realized by null-space control, where low-priority control actions are projected into the null-space of high-priority control actions. Such a method is currently unavailable for Reinforcement Learning. We propose a novel, task-prioritized composition framework for Reinforcement Learning, which involves a novel concept: The indifferent-space of Reinforcement Learning policies. Our framework has the potential to facilitate knowledge transfer and modular design while greatly increasing data efficiency and data reuse for Reinforcement Learning agents. Further, our approach can ensure high-priority constraint satisfaction, which makes it promising for learning in safety-critical domains like robotics. Unlike null-space control, our approach allows learning globally optimal policies for the compound task by online learning in the indifference-space of higher-level policies after initial compound policy construction.
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| 47. |
- Saarinen, Jari, et al.
(författare)
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3D normal distributions transform occupancy maps : an efficient representation for mapping in dynamic environments
- 2013
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Ingår i: The international journal of robotics research. - : SAGE Publications. - 0278-3649 .- 1741-3176. ; 32:14, s. 1627-1644
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Tidskriftsartikel (refereegranskat)abstract
- In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups.
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| 48. |
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| 49. |
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| 50. |
- Saarinen, Jari, 1977-, et al.
(författare)
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Normal distributions transform occupancy maps : application to large-scale online 3D mapping
- 2013
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Ingår i: IEEE International Conference on Robotics and Automation. - New York : IEEE conference proceedings. ; , s. 2233-2238
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Konferensbidrag (refereegranskat)abstract
- Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach — the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic nvironments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map.
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