Motion planning with hybrid dynamics and temporal goals

Motion planning with hybrid dynamics and temporal goals
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具有混合动力学和时间目标的运动规划

DOI:
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发表时间:
2010
期刊:
IEEE Conference on Decision and Control
影响因子:
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通讯作者:
Moshe Y. Vardi
Moshe Y. Vardi
中科院分区:
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文献类型:
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作者:
A. Bhatia;L. Kavraki;Moshe Y. Vardi

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在本文中,我们考虑的问题,移动的机器人与非线性混合动力学的运动规划,高层次的时间目标。我们使用一个多层的协同框架,最近提出的解决规划问题,涉及混合动力系统和高层次的时间目标。在该框架中,一个高层次的计划者采用了用户定义的离散抽象的混合系统,以及探索信息,建议高层次的计划。一个低层次的抽样为基础的规划器使用的混合动力系统的动态和建议的高层次的计划,探索可行的解决方案的状态空间。在以前的工作中,我们已经提出了一个基于几何的方法,当机器人被建模为一个连续系统的情况下,离散抽象的建设。在这里,我们扩展的离散抽象的机器人建模为非线性混合系统的情况下,建设的方法。为了更有效地使用所产生的抽象,我们还提出了一个高层次的规划,减少了搜索空间的大小,通过重用以前构建的高层次的计划初始化搜索的惰性搜索方法。我们提出的技术导致计算加速接近10倍,超过其他可能的方法,二阶非线性混合机器人模型在具有挑战性的工作空间环境中的障碍物和各种时间逻辑规范。
In this paper, we consider the problem of motion planning for mobile robots with nonlinear hybrid dynamics, and high-level temporal goals. We use a multi-layered synergistic framework that has been proposed recently for solving planning problems involving hybrid systems and high-level temporal goals. In that framework, a high-level planner employs a user-defined discrete abstraction of the hybrid system as well as exploration information to suggest high-level plans. A low-level sampling-based planner uses the dynamics of the hybrid system and the suggested high-level plans to explore the state-space for feasible solutions. In previous work, we have proposed a geometry-based approach for the construction of the discrete abstraction for the case when the robot is modeled as a continuous system. Here, we extend the approach for the construction of the discrete abstraction to the case when the robot is modeled as nonlinear hybrid system. To use the resulting abstraction more efficiently, we also propose a lazy-search approach for high-level planning that reduces the size of the search space by reusing previously constructed high-level plans for initializing the search. Our proposed techniques result in computational speedups of close to 10 times over other possible approaches for second-order nonlinear hybrid robot models in challenging workspace environments with obstacles and for a variety of temporal logic specifications.