自律移動ロボットのための人間リスク認識型安全経路計画に関する強化学習

This paper addresses the challenge of safe path planning for mobile robots operating in human-shared environments, where human movements are inherently stochastic. To this end, we propose a reinforcement-learning-based path planning algorithm that accounts for human-related uncertainties at the planning level. The algorithm first employs a Markov decision process learner to explore the environment and generate multiple candidate paths. Second, to reduce computational redundancy, a path eliminator module filters out similar paths based on a proposed diversity metric, ensuring path diversity with minimal overhead. Simultaneously, a Monte Carlo-simulated human risk predictor is integrated into the decision-making unit to select the safest path among the candidates. This integrated algorithm enables robots to generate safe and efficient trajectories without the need for frequent re-planning, even in environments with stochastic human behavior. Simulation results demonstrate the effectiveness of the proposed method. In high-density settings, a 40×40 grid map with 10 humans, the proposed method reduces the average number of conflicts by -69.8%, -54.8%, and -73.4% compared with A*, MDP, and RRT methods, respectively. Meanwhile, it improves task success rates by 94.4%, 70.7%, and 118.75% relative to the same baseline methods.