SOAR-RL:安全和开放空间意识强化学习,用于移动机器人在狭窄空间中的导航
Minkyung Jun1, Piljae Park2, Hoeryong Jung1
1Department of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究引入了深度强化学习 (DRL) 框架,用于在狭窄空间中自主导航机器人. 该系统通过使机器人能够主动避免行人和动态风险来提高安全性,从而改善导航成功.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在狭窄空间环境 (NSEs) 中的自主导航由于动态的行人互动和空间限制而具有挑战性.
- 现有的方法往往依赖于反应性避免碰撞,这对于在共享的人机环境中进行主动风险评估是不够的.
研究的目的:
- 开发基于深度强化学习 (DRL) 的导航框架,用于在人机器人共享环境中运行的移动机器人.
- 使机器人能够主动识别和导航安全路径,同时与NSE中的行人互动.
主要方法:
- 一个DRL框架融合3D LiDAR和RGB摄像头数据,用于实时行人检测,位置和速度估计.
- 构建一个人意识占用地图 (HAOM),将静态障碍物和动态风险区作为DRL输入.
- 设计一种新的状态表示和奖励结构,以促进安全和主动的导航行为.
主要成果:
- 拟议的框架在模拟各种NSE布局 (直线,L形,十字形) 中显著提高了导航成功率.
- 与传统的导航计划器相比,碰撞事件大幅减少.
- 该系统在各种动态障碍场景中展示了有效的导航.
结论:
- 基于DRL的导航框架为复杂,动态的狭窄空间环境中的自主机器人导航提供了强大的解决方案.
- 整合多传感器数据和人类意识的映射提高了机器人安全性和在人机交互中的效率.
- 该方法克服了传统方法的局限性,允许主动风险评估和更安全的路线选择.
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