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Abstract Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.
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Robust Optimization-based Motion Planning for high-DOF Robots under Sensing Uncertainty
Motion planning for high degree-of-freedom (DOF) robots is challenging, especially when acting in complex environments under sensing uncertainty. While there is significant work on how to plan under state uncertainty for low-DOF robots, existing methods cannot be easily translated into the high-DOF case, due to the complex geometry of the robot’s body and its environment. In this paper, we present a method that enhances optimization-based motion planners to produce robust trajectories for high-DOF robots for convex obstacles. Our approach introduces robustness into planners that are based on sequential convex programming: We reformulate each convex subproblem as a robust optimization problem that “protects” the solution against deviations due to sensing uncertainty. The parameters of the robust problem are estimated by sampling from the distribution of noisy obstacles, and performing a first-order approximation of the signed distance function. The original merit function is updated to account for the new costs of the robust formulation at every step. The effectiveness of our approach is demonstrated on two simulated experiments that involve a full body square robot, that moves in randomly generated scenes, and a 7-DOF Fetch robot, performing tabletop operations. The results show nearly zero probability of collision for a reasonable range of the noise parameters for Gaussian and Uniform uncertainty.
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- Award ID(s):
- 2008720
- PAR ID:
- 10301749
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 9724 to 9730
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA48506.2021.9560917
