| 1. |
- Krug, Robert, 1981-, et al.
(författare)
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Analytic Grasp Success Prediction with Tactile Feedback
- 2016
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - New York, USA : IEEE. - 1050-4729. ; , s. 165-171
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Konferensbidrag (refereegranskat)abstract
- Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.
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| 2. |
- Bouguerra, Abdelbaki, 1974-, et al.
(författare)
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An autonomous robotic system for load transportation
- 2009
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Ingår i: 2009 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2009). - New York : IEEE conference proceedings. - 9781424427277 - 9781424427284 ; , s. 1563-1566
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Konferensbidrag (refereegranskat)abstract
- This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems.
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| 3. |
- Bouguerra, Abdelbaki, et al.
(författare)
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MALTA : a system of multiple autonomous trucks for load transportation
- 2009
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Ingår i: Proceedings of the 4th European conference on mobile robots (ECMR). - Zagreb : KoREMA. - 9789536037544 ; , s. 93-98
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Konferensbidrag (refereegranskat)abstract
- This paper presents an overview of an autonomousrobotic material handling system. The goal of the system is toextend the functionalities of traditional AGVs to operate in highlydynamic environments. Traditionally, the reliable functioning ofAGVs relies on the availability of adequate infrastructure tosupport navigation. In the target environments of our system,such infrastructure is difficult to setup in an efficient way.Additionally, the location of objects to handle are unknown,which requires that the system be able to detect and track objectpositions at runtime. Another requirement of the system is to beable to generate trajectories dynamically, which is uncommon inindustrial AGV systems.
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| 4. |
- Gupta, Himanshu, 1993-, et al.
(författare)
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Robust scan registration for navigation in forest environment using low-resolution LiDAR sensors
- 2023
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:10
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Tidskriftsartikel (refereegranskat)abstract
- Automated forest machines are becoming important due to human operators’ complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.
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| 5. |
- Reggente, Matteo, et al.
(författare)
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Using local wind information for gas distribution mapping in outdoor environments with a mobile robot
- 2009
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Ingår i: IEEE sensors, vols 1-3. - New York : IEEE conference proceedings. - 9781424445486 ; , s. 1637-1642
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Konferensbidrag (refereegranskat)abstract
- In this paper we introduce a statistical method tobuild two-dimensional gas distribution maps (Kernel DM+V/Walgorithm). In addition to gas sensor measurements, the proposedmethod also takes into account wind information by modelingthe information content of the gas sensor measurements as abivariate Gaussian kernel whose shape depends on the measuredwind vector. We evaluate the method based on real measurementsin an outdoor environment obtained with a mobile robot thatwas equipped with gas sensors and an ultrasonic anemometerfor wind measurements. As a measure of the model quality wecompute how well unseen measurements are predicted in termsof the data likelihood. The initial results are encouraging andshow a clear improvement of the proposed method compared tothe case where wind is not considered.
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| 6. |
- Schreiter, Tim, 1997-, et al.
(författare)
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The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction
- 2022
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Ingår i: Trust, Acceptance and Social Cues in Human-Robot Interaction - SCRITA Workshop. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, andefficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robotsin industrial environments are usually not anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates driving an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here.
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| 7. |
- Winkler, Nicolas P., 1991-, et al.
(författare)
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Using Redundancy in a Sensor Network to Compensate Sensor Failures
- 2021
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Ingår i: 2021 IEEE SENSORS. - : IEEE. - 9781728195018 - 9781728195025
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Konferensbidrag (refereegranskat)abstract
- Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
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| 8. |
- Åstrand, Björn, 1969-, et al.
(författare)
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An Autonomous Robotic System for Load Transportation
- 2009
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Ingår i: Proceedings of the 4th Swedish Workshop on Autonomous Robotics (SWAR). - Västerås : Mälardalen University. ; , s. 56-57
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Konferensbidrag (refereegranskat)
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| 9. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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A Submap per Perspective : Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
- 2019
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Ingår i: 2019 European Conference on Mobile Robots (ECMR). - : IEEE. - 9781728136059
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Konferensbidrag (refereegranskat)abstract
- This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.
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| 10. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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CorAl : Introspection for robust radar and lidar perception in diverse environments using differential entropy
- 2022
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Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.
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