Sampling-Based Methods for Motion Planning with Constraints
Sampling-Based Methods for Motion Planning with Constraints
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DOI：
10.1146/annurev-control-060117-105226
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发表时间：
2018-01-01
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影响因子：
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通讯作者：
Kavraki, Lydia E.
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文献类型：
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作者：
Kingston, Zachary;Moll, Mark;Kavraki, Lydia E.
具有多个自由度的机器人(如人形机器人和移动机械手)越来越多地被用于完成诸如救灾、航天器后勤和家庭护理等领域的现实任务。为这些机器人自主寻找可行的运动对它们的运行至关重要。基于采样的运动规划算法对于这些高维系统是有效的；然而，将任务约束(例如，保持杯子水平或在板上写字)融入规划过程中会带来巨大的挑战。这项调查描述了基于抽样的有约束计划的方法家族，并将它们放在由其复杂性描述的谱上。基于约束采样的方法基于两个核心基元操作：(A)采样满足约束的配置和(B)生成满足约束的连续运动。虽然本文介绍了背景背景下基于抽样的计划的基础知识，但它侧重于约束的表示和结合了约束的基于抽样的计划器。
Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: (a) sampling constraint-satisfying configurations and (b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.