Effective Footstep Planning Using Homotopy-Class Guidance

TL;DR

This paper introduces a novel footstep planning method for humanoid robots that uses user-defined homotopy classes to automatically generate heuristics, significantly improving planning efficiency in complex environments.

Contribution

The paper presents a new approach that leverages homotopy classes for heuristic generation, reducing the need for domain-specific heuristic design in footstep planning.

Findings

In simple scenarios, performance is comparable to standard methods.

In complex scenarios, it achieves several orders of magnitude speedup.

Effectively guides footstep planning in multi-level environments.

Abstract

Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot's feet---a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. Additionally, we present an extension to homotopy classes such that they are applicable to complex multi-level environments. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach.