桥点指导神经运动规划在具有狭窄通道的复杂环境中.
Songyi Dian1,2, Juntong Liu1, Guofei Xiang1
1Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
本研究介绍了一种混合路径规划框架,可以有效地在具有狭窄通道的复杂环境中导航. 它结合了先进的采样,结构抽象和神经预测,以实现更快,更可靠的机器人导航.
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
- 运动规划 运动规划
背景情况:
- 智能机器人系统需要强大的运动和路径规划,以便在复杂的环境中进行导航.
- 传统的以采样为基础的规划者在狭窄的通道上扎,而基于学习的方法可以产生不可行的路径.
- 一个重大挑战在于有效地生成无碰撞的轨迹,特别是在充满障碍的配置空间 (C空间) 中.
研究的目的:
- 开发一种混合路径规划框架,以克服狭窄通道场景中现有方法的局限性.
- 提高成功率,减少机器人导航在具有挑战性的环境中的规划时间.
- 结合基于采样和基于学习的方法的优势,以实现更可靠和更有效的路径生成.
主要方法:
- 一个混合框架,整合了改进的采样,结构抽象和神经预测.
- 一个经过修改的桥测试采样器,具有方向扰动和走廊检查,用于狭窄通道采样.
- 将样本分组成代表性的桥梁点,形成一个全局图,使用贪的启发式搜索和神经局部部分生成器进行查询.
主要成果:
- 提出的方法在成功率和规划时间方面明显优于经典和基于学习的基线.
- 通过对2D迷宫地图,3D voxel环境和复杂的12-DOF操纵任务进行验证,证明了卓越的性能.
- 该框架保留了概率学完整性的概括形式,并附有可选修复模块.
结论:
- 混合框架在运动规划方面提供了显著的进步,特别是在由狭窄通道主导的环境中.
- 这种方法有效地平衡了规划效率和路径可靠性,解决了以前方法的关键局限性.
- 该方法为复杂,混乱的C空间中的智能机器人导航提供了强大的解决方案.
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