Safety-Oriented Motion Planning for a Wheeled Humanoid Robot Operating in Environments with Stochastically Moving Humans
Source abstract
Abstract
With the advancement of humanoid robotics, human–robot collaboration has emerged as a prominent research focus. Ensuring the safety of both humanoid robots and humans remains a critical challenge. In this paper, we address conflict resolutions at the planning level and propose a safety-oriented motion planning (SOMP) algorithm for a wheeled humanoid robot operating in environments with unknown human motions. In the proposed SOMP algorithm, we employ Monte Carlo simulations to predict trajectories of stochastically moving humans and formulate both hard and soft constraints. A dynamic-quadrant stochastic sampling policy, integrated with a rapidly exploring random tree method, is proposed to generate diverse initial paths. Building upon this, we develop a constraint-fusion mechanism that combines hard constraints for safety guarantees and soft constraints for path optimization, thereby effectively resolving potential conflicts between wheeled humanoid robots and stochastically moving humans. We evaluate the proposed algorithm under different configurations of conflict numbers, task success rates, and path rewards. The proposed method outperforms A*, RRT, and MDP in terms of conflict numbers (−77.8%, −76.6%, and −71.4%) and task success rates (+168.0%, +109.4%, and +91.4%). Our simulation results prove the efficiency and robustness of our algorithm in safe motion planning with stochastically moving humans.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Locomotion and Control · Robotic Path Planning Algorithms · Zebrafish Biomedical Research Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Transport Engineering, School of Civil Engineering and Transportation, Yangzhou University, Yangzhou 225127, China
Key Laboratory of the Ministry of Education for Modern Measurement & Control Technology, College of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 102206, China
College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
Smart Lab of Innovation, Sinotrans Innovation & Technology Co., Ltd., Beijing 100029, China
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