Task Space Regions: A framework for pose-constrained manipulation planning
Task Space Regions: A framework for pose-constrained manipulation planning
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DOI：
10.1177/0278364910396389
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发表时间：
2011-10-01
影响因子：
9.2
通讯作者：
Kuffner, James
中科院分区：
文献类型：
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作者：
Berenson, Dmitry;Srinivasa, Siddhartha;Kuffner, James
我们提出了一个操作规划框架，允许机器人在存在末端执行器姿态约束以及其他常见约束的情况下进行规划。该框架包括三个主要组成部分：约束表示、约束满足策略和通用规划算法。这些组件结合在一起创建了一种高效的、概率上完整的操作规划算法，称为受限双向快速探索随机树(RRT)-CBiRRT2。我们的姿势约束框架的基础是我们的任务空间区域(TSR)表示。TSR很直观，可以有效地采样，并且可以非常快速地评估到TSR的距离，使它们成为基于采样的规划的理想选择。最重要的是，TSR是姿势约束的通用表示，可以完全描述许多实际任务。对于更复杂的任务，例如操纵关节对象，可以将TSR链接在一起以创建更复杂的末端效应器姿势约束。TSR也可以相交，这是我们用来规划姿势不确定性的一个性质。我们对我们的框架进行了详细的描述，证明了我们的规划方法的概率完备性，并描述了几个真实世界的例子问题，说明了TSR框架的效率和通用性。
We present a manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints. The framework has three main components: constraint representation, constraint-satisfaction strategies, and a general planning algorithm. These components come together to create an efficient and probabilistically complete manipulation planning algorithm called the Constrained BiDirectional Rapidly-exploring Random Tree (RRT) - CBiRRT2. The underpinning of our framework for pose constraints is our Task Space Regions (TSRs) representation. TSRs are intuitive to specify, can be efficiently sampled, and the distance to a TSR can be evaluated very quickly, making them ideal for sampling-based planning. Most importantly, TSRs are a general representation of pose constraints that can fully describe many practical tasks. For more complex tasks, such as manipulating articulated objects, TSRs can be chained together to create more complex end-effector pose constraints. TSRs can also be intersected, a property that we use to plan with pose uncertainty. We provide a detailed description of our framework, prove probabilistic completeness for our planning approach, and describe several real-world example problems that illustrate the efficiency and versatility of the TSR framework.