Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction

TL;DR

This paper introduces a unified multi-level planning approach for hybrid driving-stepping robots navigating complex terrains, balancing detailed local plans with coarser global ones to enable efficient large-scale navigation.

Contribution

It presents a novel multi-level planning method that integrates different abstraction levels into a single framework, improving planning efficiency for complex environments.

Findings

Effective planning for large, challenging scenarios

Plans generated within feasible computational time

Multi-level representation enhances navigation robustness

Abstract

Navigating in search and rescue environments is challenging, since a variety of terrains has to be considered. Hybrid driving-stepping locomotion, as provided by our robot Momaro, is a promising approach. Similar to other locomotion methods, it incorporates many degrees of freedom---offering high flexibility but making planning computationally expensive for larger environments. We propose a navigation planning method, which unifies different levels of representation in a single planner. In the vicinity of the robot, it provides plans with a fine resolution and a high robot state dimensionality. With increasing distance from the robot, plans become coarser and the robot state dimensionality decreases. We compensate this loss of information by enriching coarser representations with additional semantics. Experiments show that the proposed planner provides plans for large, challenging scenarios in feasible time.