| 1. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
- 2023
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Ingår i: IEEE Transactions on robotics. - : IEEE. - 1552-3098 .- 1941-0468. ; 39:2, s. 1476-1495
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Tidskriftsartikel (refereegranskat)abstract
- This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.
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| 2. |
- Aragues, Rosario, et al.
(författare)
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Intermittent Connectivity Maintenance With Heterogeneous Robots
- 2021
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Ingår i: IEEE Transactions on robotics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1552-3098 .- 1941-0468. ; 37:1, s. 225-245
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we consider a scenario of cooperative task servicing, with a team of heterogeneous robots with different maximum speeds and communication radii, in charge of keeping the network intermittently connected. We abstract the task locations into a one-dimensional cycle graph that is traversed by the communicating robots, and we discuss intermittent communication strategies so that each task location is periodically visited, with a worst-case revisiting time. Robots move forward and backward along the cycle graph, exchanging data with their previous and next neighbors when they meet, and updating their region boundaries. Asymptotically, each robot is in charge of a region of the cycle graph, depending on its capabilities. The method is distributed, and robots only exchange data when they meet.
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| 3. |
- Arslan Waltersson, Gabriel, 1996, et al.
(författare)
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Planar Friction Modelling with LuGre Dynamics and Limit Surfaces
- 2024
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Ingår i: IEEE Transactions on Robotics. - 1552-3098 .- 1941-0468. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- During planar motion, contact surfaces exhibit a coupling between tangential and rotational friction forces. This paper proposes planar friction models grounded in the LuGre model and limit surface theory. First, distributed planar extended state models are proposed and the Elasto-Plastic model is extended for multi-dimensional friction. Subsequently, we derive a reduced planar friction model coupled with a pre-calculated limit surface, that offers reduced computational cost. The limit surface approximation through an ellipsoid is discussed. The properties of the planar friction models are assessed in various simulations, demonstrating that the reduced planar friction model achieves comparable performance to the distributed model while exhibiting $\sim 80$ times lower computational cost.
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| 4. |
- Aydemir, Alper, et al.
(författare)
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Active Visual Object Search in Unknown Environments Using Uncertain Semantics
- 2013
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Ingår i: IEEE Transactions on robotics. - 1552-3098 .- 1941-0468. ; 29:4, s. 986-1002
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can devise efficient search strategies that can locate everyday objects at the scale of an entire building floor, which is previously unknown to the robot. To realize this, we present a probabilistic model of the search environment, which allows for prioritizing the search effort to those parts of the environment that are most promising for a specific object type. Further, we describe a method for reasoning about the unexplored part of the environment for goal-directed exploration with the purpose of object search. We demonstrate the validity of our approach by comparing it with two other search systems in terms of search trajectory length and time. First, we implement a greedy coverage-based search strategy that is found in previous work. Second, we let human participants search for objects as an alternative comparison for our method. Our results show that AVS strategies that exploit uncertain semantics of the environment are a very promising idea, and our method pushes the state-of-the-art forward in AVS.
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| 5. |
- Baumann, Dominik, Ph.D. 1991-, et al.
(författare)
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Safe Reinforcement Learning in Uncertain Contexts
- 2024
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Ingår i: IEEE Transactions on robotics. - : IEEE. - 1552-3098 .- 1941-0468. ; 40, s. 1828-1841
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Tidskriftsartikel (refereegranskat)abstract
- When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables, i.e., regression tasks. However, in practice, robotic systems are also subject to discrete, external environmental changes, e.g., having to carry objects of certain weights or operating on frozen, wet, or dry surfaces. Such influences can be modeled as discrete context variables. In the existing literature, such contexts are, if considered, mostly assumed to be known. In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables. To achieve this, we derive frequentist guarantees for multiclass classification, allowing us to estimate the current context from measurements. Furthermore, we propose an approach for identifying contexts through experiments. We discuss under which conditions we can retain theoretical guarantees and demonstrate the applicability of our algorithm on a Furuta pendulum with camera measurements of different weights that serve as contexts.
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| 6. |
- Bechlioulis, Charalampos P., et al.
(författare)
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Robust Image-Based Visual Servoing With Prescribed Performance Under Field of View Constraints
- 2019
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Ingår i: IEEE Transactions on robotics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1552-3098 .- 1941-0468. ; 35:4, s. 1063-1070
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a visual servoing scheme that imposes predefined performance specifications on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the camera's limited field of view, despite the inevitable calibration and depth measurement errors. Its efficiency is demonstrated via comparative experimental and simulation studies.
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| 7. |
- Bekiroglu, Yasemin, et al.
(författare)
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Assessing Grasp Stability Based on Learning and Haptic Data
- 2011
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Ingår i: IEEE Transactions on robotics. - : IEEE Robotics and Automation Society. - 1552-3098 .- 1941-0468. ; 27:3, s. 616-629
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Tidskriftsartikel (refereegranskat)abstract
- An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machinelearning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements fromfingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research.
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| 8. |
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| 9. |
- Bohg, Jeannette, et al.
(författare)
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Data-Driven Grasp Synthesis-A Survey
- 2014
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Ingår i: IEEE Transactions on robotics. - 1552-3098 .- 1941-0468. ; 30:2, s. 289-309
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Tidskriftsartikel (refereegranskat)abstract
- We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.
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| 10. |
- Bohg, Jeannette, et al.
(författare)
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Interactive Perception : Leveraging Action in Perception and Perception in Action
- 2017
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Ingår i: IEEE Transactions on robotics. - : IEEE. - 1552-3098 .- 1941-0468. ; 33:6, s. 1273-1291
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Tidskriftsartikel (refereegranskat)abstract
- Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.
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