Safety-Oriented Motion Planning for a Wheeled Humanoid Robot Operating in Environments with Stochastically Moving Humans
Year: 2026
Type: article
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: Applied Sciences
DOI: 10.3390/app16031500
Authors: Jian Mi, Xianbo Zhang, Zhongjie Long, Jun Wang, Wei Xu
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7127112915
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