通过自适应知情采样加速基于采样的最优路径规划

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Autonomous Robots Pub Date : 2024-04-20 DOI:10.1007/s10514-024-10157-5

Marco Faroni, Nicola Pedrocchi, Manuel Beschi

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引用次数: 0

摘要

本文通过结合可允许的知情采样和局部采样（即对当前解的邻域进行采样），提高了类似采样的路径规划器的性能。一种自适应策略会根据之前采样的在线回报来调节探索（允许的知情采样）和利用（局部采样）之间的权衡。论文证明，在多个模拟和实际场景中，所产生的算法是渐进最优的，而且收敛速度优于最先进的路径规划器（例如，Informed-RRT/(^*\)）。该算法的开源、兼容 ROS 的实现已公开发布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Adaptive hybrid local–global sampling for fast informed sampling-based optimal path planning

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Adaptive hybrid local–global sampling for fast informed sampling-based optimal path planning

This paper improves the performance of RRT\(^*\)-like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online rewards from previous samples. The paper demonstrates that the algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT\(^*\)) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.

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来源期刊

Autonomous Robots 工程技术-机器人学

CiteScore

7.90

自引率

5.70%

发文量

审稿时长

3 months

期刊介绍： Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.