Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots

TL;DR

This paper introduces a combined sampling and optimization planning method for legged-wheeled robots that improves navigation on challenging terrains by generating better initial guesses and handling complex terrain representations.

Contribution

It presents a novel integrated approach that combines sampling and optimization to enhance planning robustness and efficiency for legged-wheeled robots on difficult terrains.

Findings

Sampling stage accelerates optimization convergence.

Good initial guesses are crucial for successful optimization.

Terrain and collision constraints are more challenging than robot model constraints.

Abstract

Planning for legged-wheeled machines is typically done using trajectory optimization because of many degrees of freedom, thus rendering legged-wheeled planners prone to falling prey to bad local minima. We present a combined sampling and optimization-based planning approach that can cope with challenging terrain. The sampling-based stage computes whole-body configurations and contact schedule, which speeds up the optimization convergence. The optimization-based stage ensures that all the system constraints, such as non-holonomic rolling constraints, are satisfied. The evaluations show the importance of good initial guesses for optimization. Furthermore, they suggest that terrain/collision (avoidance) constraints are more challenging than the robot model's constraints. Lastly, we extend the optimization to handle general terrain representations in the form of elevation maps.