Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

This letter 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.

I. Introduction

Autonomous robotic systems have become one of the most popular research topics in recent years due to their pronounced potential social benefits. In particular, autonomous driving is developing rapidly and at the same time requires efficient motion planning in complex and highly dynamic environments, meanwhile taking into account the kinodynamic constraints of an non-holonomic autonomous vehicle. Often, the planners that address the first aspect of the problem, i.e. dynamic environment, like the ones presented in [1], [2], [3] do not take into account the kinodynamic constraints. On the other hand, kinodynamic planners often do not explicitly reason about the future changes of the environments, even if these changes are for-seen, e.g. predicted by the control system of the robot. In this work we want to enrich the kinodynamic planning methods with the ability to take the dynamics of the environment as well (at the planning stage).