广义多速Dubins运动模型

IF 9.4 1区 计算机科学 Q1 ROBOTICS
James P. Wilson;Shalabh Gupta;Thomas A. Wettergren
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引用次数: 0

摘要

本文提出了一种新的运动模型,称为广义多速杜宾运动模型(GMDM),它在杜宾模型的基础上扩展了多速杜宾运动模型。虽然Dubins模型在恒定速度约束下产生时间最优路径,但如果将该约束放宽到包含多个速度,这些路径可能是次优的。这是因为恒定的速度导致较大的最小转弯半径,从而产生更长的机动和更大的旅行时间的路径。相比之下,多速放松允许低速急转弯,从而产生更直接的路径,更短的机动和更短的旅行时间。此外,Dubins模型无法降低速度可能导致在障碍物附近快速机动,从而产生具有高碰撞风险的路径。在这方面,GMDM通过允许沿着路径改变速度,为运动规划者提供了共同优化时间和风险的能力。GMDM是在考虑路径段上速度变化的六种dubin路径类型的基础上建立的。从理论上证明了GMDM为任意速度选择提供了配置空间的完全可达性。此外,还证明了Dubins模型是等速GMDM的一个特殊情况。GMDM的解决方案是分析性的,适合于实时应用。通过广泛的蒙特卡洛仿真,比较了GMDM在无障碍物和多障碍物环境下与现有运动模型在求解质量(即时间/时间风险成本)和计算时间方面的性能。结果表明,在无障碍物环境下,GMDM模型产生的近时间最优路径的行程时间明显低于Dubins模型,且计算时间相似。在障碍物多的环境中,GMDM产生了时间风险优化的路径,大大降低了碰撞风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized Multispeed Dubins Motion Model
The article develops a novel motion model, called generalized multispeed Dubins motion model (GMDM), which extends the Dubins model by considering multiple speeds. While the Dubins model produces time-optimal paths under a constant-speed constraint, these paths could be suboptimal if this constraint is relaxed to include multiple speeds. This is because a constant speed results in a large minimum turning radius, thus producing paths with longer maneuvers and larger travel times. In contrast, multispeed relaxation allows for slower speed sharp turns, thus producing more direct paths with shorter maneuvers and smaller travel times. Furthermore, the inability of the Dubins model to reduce speed could result in fast maneuvers near obstacles, thus producing paths with high collision risks. In this regard, GMDM provides the motion planners the ability to jointly optimize time and risk by allowing the change of speed along the path. GMDM is built upon the six Dubins path types considering the change of speed on path segments. It is theoretically established that GMDM provides full reachability of the configuration space for any speed selections. Furthermore, it is shown that the Dubins model is a specific case of GMDM for constant speeds. The solutions of GMDM are analytical and suitable for real-time applications. The performance of GMDM in terms of solution quality (i.e., time/time-risk cost) and computation time is comparatively evaluated against the existing motion models in obstacle-free as well as obstacle-rich environments via extensive Monte Carlo simulations. The results show that in obstacle-free environments, GMDM produces near time-optimal paths with significantly lower travel times than the Dubins model while having similar computation times. In obstacle-rich environments, GMDM produces time-risk optimized paths with substantially lower collision risks.
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来源期刊
IEEE Transactions on Robotics
IEEE Transactions on Robotics 工程技术-机器人学
CiteScore
14.90
自引率
5.10%
发文量
259
审稿时长
6.0 months
期刊介绍: The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles. Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.
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