懒惰转向RRT*:一种基于约束运动神经网络的无探索转向优化规划

Mohammadreza Yavari, K. Gupta, M. Mehrandezh
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引用次数: 8

摘要

Kinodynamic-RRT*为运动学约束和动力学约束的机器人运动规划提供了基于采样的渐近最优解。对于非线性系统,通常情况下,RRT*内所需的时间和能量受限的转向函数解使用数值迭代格式,如射击方法,这在计算上很麻烦。对这些解算器的调用次数随着树的大小而增加。因此,寻找可行转向函数的时间复杂性阻碍了非线性系统的动力学- rrt *在实时或需要快速规划和重新规划的情况下的应用。文献中提出了通过运动学/动力学约束约简使转向函数实时可解的方法,但这些方法会影响解的最优性。在本文中,我们提出了一种惰性转向动力学RRT*,其中机器学习技术用于(1)预测随机选择的节点是否可转向,(2)如果转向被认为是可行的,那么执行它时相关的估计能量成本是多少。这为实现kinodynamicrrt *提供了一个很有前途的框架,其中数值方法的使用被延迟(因此称为懒惰转向),直到找到一个潜在的无碰撞路径,然后才调用数值技术。这在运行时方面带来了巨大的改进,而在最优性方面的损失很小。我们提出的方法通过仿真测试了一个欠驱动的,非完整的,非线性动力学的四轴飞行器在一个充满障碍物的环境中的运动规划。惰性转向的RRT*比它的对手(基于最近的一些研究成果)快了两个数量级。
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Lazy Steering RRT*: An Optimal Constrained Kinodynamic Neural Network Based Planner with no In-Exploration Steering
Kinodynamic-RRT* provides a sampling based asymptotically-optimal solution for motion planning of kinematically- and dynamically-constrained robots. For nonlinear systems, normally, the time- and energy-clamped steering function solutions needed within the RRT* use numerical iterative schemes such as shooting methods, which are computationally cumbersome. The number of calls to these solvers increases with the size of the tree. Hence, the time complexity of finding feasible steering functions prevents kinodynamic-RRT* for non-linear systems from being utilized in realtime or in situations where fast planning and re-planning are needed. Kinematic/dynamic constraints reduction to make the steering functions solvable in real time has been proposed in literature, however, these methods would affect the optimality of the solution. In this paper, we propose a lazy-steering kinodynmaic RRT* in which, machine learning techniques are used to (1) predict if a randomly-selected node is steerable to, and (2) if the steering is deemed feasible, what would be the estimated energy cost associated, when executing it. This provides a promising framework for implementing Kinodynamic-RRT* in which the use of numerical methods is delayed (hence the name lazy steering) until a potential collision free path has been found, and only then the numerical techniques are invoked. This results in a huge improvement in the run time with little trade off on optimality. Our proposed method was tested via simulation for motion planning of an under-actuated, non-holonomic, quadcopter with nonlinear dynamics in an environment cluttered with obstacles. The lazy-steering RRT* was faster than its counterpart (which was based on some recent works) by two orders of magnitude.
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