人群
强化学习
机器人
计算机科学
行人
人工智能
运动(物理)
社会力量模型
避碰
人机交互
钢筋
模拟
碰撞
工程类
计算机安全
运输工程
结构工程
作者
Zi-Cai Feng,Bingxin Xue,Chaoqun Wang,Fengyu Zhou
出处
期刊:Robotica
[Cambridge University Press]
日期:2024-02-26
卷期号:42 (4): 1212-1230
标识
DOI:10.1017/s0263574724000183
摘要
Abstract Safe and socially compliant navigation in a crowded environment is essential for social robots. Numerous research efforts have shown the advantages of deep reinforcement learning techniques in training efficient policies, while most of them ignore fast-moving pedestrians in the crowd. In this paper, we present a novel design of safety measure, named Risk-Area, considering collision theory and motion characteristics of different robots and humans. The geometry of Risk-Area is formed based on the real-time relative positions and velocities of the agents in the environment. Our approach perceives risk in the environment and encourages the robot to take safe and socially compliant navigation behaviors. The proposed method is verified with three existing well-known deep reinforcement learning models in densely populated environments. Experiment results demonstrate that our approach combined with the reinforcement learning techniques can efficiently perceive risk in the environment and navigate the robot with high safety in the crowds with fast-moving pedestrians.
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