论文标题
一种快速探索的随机树运动计划算法，用于混合动力学系统
A Rapidly-Exploring Random Trees Motion Planning Algorithm for Hybrid Dynamical Systems
论文作者
论文摘要
本文提出了一种快速探索的随机树（RRT）算法，以解决混合系统的运动计划问题。在每次迭代中，所提出的算法称为hyrrt，随机选择状态样本并通过流或跳跃扩展搜索树，在可能的情况下，这也是随机选择的。通过定义在混合时间域上定义的函数串联的定义，我们表明hyrt概率是完整的，即，随着算法的迭代次数增加，未能找到运动计划的概率接近零。在定义运动计划的数据的轻度条件下，保证了该特性，其中包括对经典系统文献中施加的通常的积极清除假设的放松。运动计划是通过解决两个优化问题的解决方案来计算的，一个与流量有关，另一个与系统的跳跃相关。提出的算法应用于行走机器人，以突出其通用性和计算特征。
This paper proposes a rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HyRRT, randomly picks a state sample and extends the search tree by flow or jump, which is also chosen randomly when both regimes are possible. Through a definition of concatenation of functions defined on hybrid time domains, we show that HyRRT is probabilistically complete, namely, the probability of failing to find a motion plan approaches zero as the number of iterations of the algorithm increases. This property is guaranteed under mild conditions on the data defining the motion plan, which include a relaxation of the usual positive clearance assumption imposed in the literature of classical systems. The motion plan is computed through the solution of two optimization problems, one associated with the flow and the other with the jumps of the system. The proposed algorithm is applied to a walking robot so as to highlight its generality and computational features.
