| 1. |
- 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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| 2. |
- Arslan Waltersson, Gabriel, 1996, et al.
(författare)
-
Planning and Control for Cable-routing with Dual-arm Robot
- 2022
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2022-May, s. 1046-1052
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a new framework for solving cable-routing problems with a dual-arm robot, where the objective is to clip a Deformable Linear Object (DLO) into several arbitrarily placed fixtures. The core of the framework is a task-space planner, which builds a roadmap from predefined tasks and employs a replanning strategy based on a genetic algorithm, if problems occur. The manipulation tasks are executed with either individual or coordinated control of the arms. Moreover, hierarchical quadratic programming is used to solve the inverse differential kinematics together with extra feasibility objectives. A vision system first identifies the desired fixture route and structure preserved registration estimates the state of the DLO in real-time. The framework is tested on real-world experiments with a YuMi robot, demonstrating a 90% success rate for 3 fixture problems.
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| 3. |
- Laezza, Rita, 1995, et al.
(författare)
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Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
- 2024
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. While shape-servoing methods have been shown successful in contexts with approximately linear behavior, they can fail in tasks with more complex dynamics. We investigate an alternative approach, using offline RL to solve a planar shape control problem of a Deformable Linear Object (DLO). To evaluate the effect of material properties, two DLOs are tested namely a soft rope and an elastic cord. We frame this task as a goal-conditioned offline RL problem, and aim to learn to generalize to unseen goal shapes. Data collection and augmentation procedures are proposed to limit the amount of experimental data which needs to be collected with the real robot. We evaluate the amount of augmentation needed to achieve the best results, and test the effect of regularization through behavior cloning on the TD3+BC algorithm. Finally, we show that the proposed approach is able to outperform a shape-servoing baseline in a curvature inversion experiment.
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