An Efficient Trajectory Planner for Car-like Robots on Uneven Terrain

TL;DR

This paper introduces a novel trajectory planning method for car-like robots navigating uneven terrain, effectively assessing terrain impact and respecting robot dynamics for improved efficiency and tracking.

Contribution

It proposes terrain pose mapping and a trajectory optimization framework that considers terrain effects and robot dynamics, enhancing planning accuracy and feasibility.

Findings

Trajectories are less conservative and more accurate.

Method is validated through simulations and real-world tests.

Generated trajectories are well tracked by controllers.

Abstract

Autonomous navigation of ground robots on uneven terrain is being considered in more and more tasks. However, uneven terrain will bring two problems to motion planning: how to assess the traversability of the terrain and how to cope with the dynamics model of the robot associated with the terrain. The trajectories generated by existing methods are often too conservative or cannot be tracked well by the controller since the second problem is not well solved. In this paper, we propose terrain pose mapping to describe the impact of terrain on the robot. With this mapping, we can obtain the SE(3) state of the robot on uneven terrain for a given state in SE(2). Then, based on it, we present a trajectory optimization framework for car-like robots on uneven terrain that can consider both of the above problems. The trajectories generated by our method conform to the dynamics model of the system without being overly conservative and yet able to be tracked well by the controller. We perform simulations and real-world experiments to validate the efficiency and trajectory quality of our algorithm.