Energy-Efficient Motion Planning for Multi-Modal Hybrid Locomotion

TL;DR

This paper introduces an approximate dynamic programming approach for energy-efficient motion planning in hybrid robots that combine multiple locomotion modalities, demonstrated on various robotic platforms.

Contribution

It presents a novel trajectory planning method using approximate dynamic programming and optimal transport costs for hybrid locomotion systems.

Findings

Effective planning for hybrid robots demonstrated on diverse platforms

Approximate dynamic programming captures complex vehicle dynamics

Trajectory optimization reduces energy consumption

Abstract

Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a novel method for planning multi-modal locomotion trajectories using approximate dynamic programming. We formulate this problem as a shortest-path search through a state-space graph, where the edge cost is assigned as optimal transport cost along each segment. This cost is approximated from batches of offline trajectory optimizations, which allows the complex effects of vehicle under-actuation and dynamic constraints to be approximately captured in a tractable way. Our method is illustrated on a hybrid double-integrator, an amphibious robot, and a flying-driving drone, showing the practicality of the approach.