This paper addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles -- a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub problems and combining the local solutions into the global one. The crux of any planning method for non-holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot environments. We show that POLAMP is able to plan collision-free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the-art competitors.
翻译:本文论述在动态环境中对不光彩机器人的动态动态动态运动规划,这种动态环境中既有静态障碍,也有动态障碍 -- -- 这是一个挑战性的问题,目前还缺乏普遍的解决办法。解决该问题的有希望的方法之一是将问题分解成较小的子问题,并将当地解决办法纳入全球解决办法。对于非光线机器人,任何规划方法的关键在于产生运动原始方法,为当地规划子问题产生解决办法。在这项工作中,我们引入了一种新的可学习指导功能(政策),它考虑到机器人的动态动力制约以及静态和动态障碍。这一政策通过政策优化得到了有效的培训。我们生动地展示了我们的指导功能能够将问题概括到不可见的问题中。我们随后将经过培训的政策插入到基于取样和基于花式的机器人规划者之中,并评估由此产生的POLAMP算法(政策优化,即学会适应性动动动动运动的原始特征)在一系列具有挑战性的设置中,其中涉及到一个像汽车一样的机器人在有障碍的停车场环境中运行。我们展示了比移动性强的机能率50,我们展示了磁力压率比移动式的机压率率更高的动作环境。