期刊
IEEE TRANSACTIONS ON ROBOTICS
卷 38, 期 2, 页码 939-957出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2021.3106827
关键词
Vehicle dynamics; Collision avoidance; Tools; Safety; Probabilistic logic; Reliability; Mathematical models; Collision avoidance; motion control; optimization; predictive control; probability distribution; robustness
类别
资金
- Seoul National University
- Information and Communications Technology Planning and Evaluation (IITP) - Ministry of Science and ICT (MSIT) [2020-0-00857]
- National Research Foundation of Korea - MSIT [2021R1A4A2001824]
- Samsung Electronics
- National Research Foundation of Korea [2021R1A4A2001824] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This article introduces a novel model-predictive control method for mobile robots to avoid randomly moving obstacles, even without knowing the true probability distribution of uncertainty. By utilizing a statistical ball as the ambiguity set, it achieves a probabilistic guarantee of out-of-sample risk and resolves the infinite-dimensionality issue in the distributionally robust MPC problem. The proposed method demonstrates successful avoidance of randomly moving obstacles and guarantees out-of-sample risk even with a small sample size, outperforming its sample average approximation counterpart.
In this article, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown. We propose a novel model-predictive control (MPC) method for limiting the risk of unsafety even when the true distribution of the obstacles' movements deviates, within an ambiguity set, from the empirical distribution obtained using a limited amount of sample data. By choosing the ambiguity set as a statistical ball with its radius measured by the Wasserstein metric, we achieve a probabilistic guarantee of the out-of-sample risk, evaluated using new sample data generated independently of the training data. To resolve the infinite-dimensionality issue inherent in the distributionally robust MPC problem, we reformulate it as a finite-dimensional nonlinear program using modern distributionally robust optimization techniques based on the Kantorovich duality principle. To find a globally optimal solution in the case of affine dynamics and output equations, a spatial branch-and-bound algorithm is designed using McCormick relaxation. The performance of the proposed method is demonstrated and analyzed through simulation studies using nonlinear dynamic and kinematic vehicle models and a linearized quadrotor model. The simulation results indicate that, even when the sample size is small, the proposed method can successfully avoid randomly moving obstacles with a guarantee of out-of-sample risk, while its sample average approximation counterpart fails to do so.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据