Wasserstein Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor

Fuente: arXiv
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Main Authors: Xu, Shaohang, Ruan, Haolin, Zhang, Wentao, Wang, Yian, Zhu, Lijun, Ho, Chin Pang
Format: Preprint
Published: 2023
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_version_ 1866910675595427840
author Xu, Shaohang
Ruan, Haolin
Zhang, Wentao
Wang, Yian
Zhu, Lijun
Ho, Chin Pang
author_facet Xu, Shaohang
Ruan, Haolin
Zhang, Wentao
Wang, Yian
Zhu, Lijun
Ho, Chin Pang
contents Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, offering global optimality through its convex formulation. The safe corridor is constructed based on the perceived map, however, the non-ideal perception induces uncertainty, which is rarely considered in trajectory generation. In this paper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) to consider the uncertainty of the safe corridor. Then, we integrate DRSCCs into the trajectory optimization framework using Bernstein basis polynomials. Theoretically, we rigorously prove that the trajectory optimization problem incorporating DRSCCs is equivalent to a computationally efficient, convex quadratic program. Compared to the nominal TO, our method enhances navigation safety by significantly reducing the infeasible motions in presence of uncertainty. Moreover, the proposed approach is validated through two robotic applications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robot Unitree A1.
format Preprint
id arxiv_https___arxiv_org_abs_2308_16381
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Wasserstein Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor
Xu, Shaohang
Ruan, Haolin
Zhang, Wentao
Wang, Yian
Zhu, Lijun
Ho, Chin Pang
Robotics
Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, offering global optimality through its convex formulation. The safe corridor is constructed based on the perceived map, however, the non-ideal perception induces uncertainty, which is rarely considered in trajectory generation. In this paper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) to consider the uncertainty of the safe corridor. Then, we integrate DRSCCs into the trajectory optimization framework using Bernstein basis polynomials. Theoretically, we rigorously prove that the trajectory optimization problem incorporating DRSCCs is equivalent to a computationally efficient, convex quadratic program. Compared to the nominal TO, our method enhances navigation safety by significantly reducing the infeasible motions in presence of uncertainty. Moreover, the proposed approach is validated through two robotic applications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robot Unitree A1.
title Wasserstein Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor
topic Robotics
url https://arxiv.org/abs/2308.16381