Randomized kinodynamic planning
Randomized kinodynamic planning
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DOI：
10.1177/02783640122067453
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发表时间：
2001-05-01
影响因子：
9.2
通讯作者：
Kuffner, JJ
中科院分区：
文献类型：
--
作者：
LaValle, SM;Kuffner, JJ
本文介绍了第一个随机方法kinodynamic规划（也称为轨迹规划或轨迹设计）。该任务是确定控制输入，以驱动机器人从初始配置和速度到目标配置和速度，同时服从基于物理的动态模型，并避免机器人环境中的障碍物。作者认为，一般的系统，表示机器人的高维配置空间的机器人的非线性动力学。Kinodynamic规划被视为一个运动规划问题，在一个更高的维状态空间，既有一阶微分约束和障碍物的全局约束。状态空间的作用与基本路径规划的配置空间相同;然而，标准的随机化路径规划技术不直接应用于状态空间中的规划轨迹。作者开发了一种随机规划方法，特别适合于高维状态空间中的轨迹规划问题。这种方法的基础是快速探索随机树的建设，它提供的好处是类似于成功的随机完整规划方法，但适用于更广泛的一类问题。对该算法进行了理论分析。实验结果提出了一个实现，计算气垫船和卫星在杂乱的环境中的轨迹，导致高达12维的状态空间。
This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based dynamical models and avoiding obstacles in the robot's environment. The authors consider generic systems that express the nonlinear dynamics of a robot in terms of the robot's high-dimensional configuration space. Kinodynamic planning is treated as a motion-planning problem in a higher dimensional state space that has both first-order differential constraints and obstacle based global constraints. The state space serves the same role as the configuration space for basic path planning; however standard randomized path-planning techniques do not directly apply to planning trajectories in the state space. The authors have developed a randomized planning approach that is particularly tailored to trajectory planning problems in high-dimensional state spaces. The basis for this approach is the construction of rapidly exploring random trees, which offer benefits that are similar to those obtained by successful randomized holonomic planning methods but apply to a much broader class of problems. Theoretical analysis of the algorithm is given. Experimental results are presented for an implementation that computes trajectories for hovercrafts and satellites in cluttered environments, resulting in state spaces of up to 12 dimensions.