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同学类指导快速探索随机树的信仰空间规划

Ran Hao¹, M Cenk Çavuşoğlu¹

¹RH and MCC are with the Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH.

Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems

|May 22, 2024

概括

这项研究引入了一种新的同质导向规划方法,用于在杂乱的空间中导航的机器人. 它通过考虑不同的拓特征,有效地找到最佳路径,减少复杂环境中的不确定性.

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
计算几何学的计算几何学

背景情况:

在障碍物拥挤的环境中路径规划是机器人技术的一个重大挑战.
现有的方法经常与复杂的拓空间和狭窄的通道作斗争.
信念空间规划对于处理机器人定位和状态估计中的不确定性至关重要.

研究的目的:

为障碍物杂乱的环境开发一种高效的同质性引导的信念空间规划方法.
通过纳入拓信息 (同质类) 来增强轨迹规划.
在已识别的同类类中,尽量减少局部最佳轨迹的不确定性.

主要方法:

这是一个两步的方法,结合了基于树的探索和信念空间优化.
引入一个以h签名为指导的快速探索随机树 (HRRT) 算法,用于并行构建同质感知子树.
扩展一个以h签名为指导的RTT*算法,并使用同类类间的重新布线程序来改善狭窄通道的发现.
基于iLQG的信念空间规划算法的应用,用于局部轨迹优化和不确定性最小化.

主要成果:

拟议的HRRT算法成功地在不同的同类类别中生成名义轨迹.
扩展的RRT*算法表明发现同类类的概率增加,特别是在受约束的空间.
基于iLQG的规划器有效地找到本地最佳轨迹,同时最大限度地减少每个同类类内的状态不确定性.
综合方法在复杂,充满障碍的环境中提供了高效和稳健的路径规划.

结论:

开发的同质导向信念空间规划方法为复杂的机器人导航任务提供了有效的解决方案.
整合拓信息显著提高了规划者处理具有挑战性的环境和不确定性的能力.
该方法为更复杂的自主导航系统在混乱和不确定的领域提供了基础.

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