Energy Prediction on Sloping Ground for Quadruped Robots

TL;DR

This paper presents a simple, sensor-based energy model for quadruped robots that predicts energy costs based on terrain slope and heading, aiding in efficient outdoor navigation.

Contribution

It introduces a novel, easily deployable energy model relying on standard sensors, validated through field experiments on natural terrain.

Findings

Energy cost increases approximately linearly with slope angle.

Lateral movement incurs higher energy costs than forward movement.

Energy costs add up predictably across trajectory segments.

Abstract

Energy management is a fundamental challenge for legged robots in outdoor environments. Endurance directly constrains mission success, while efficient resource use reduces ecological impact. This paper investigates how terrain slope and heading orientation influence the energetic cost of quadruped locomotion. We introduce a simple energy model that relies solely on standard onboard sensors, avoids specialized instrumentation, and remains applicable in previously unexplored environments. The model is identified from field runs on a commercial quadruped and expressed as a compact function of slope angle and heading. Field validation on natural terrain shows near-linear trends of force-equivalent cost with slope angle, consistently higher lateral costs, and additive behavior across trajectory segments, supporting path-level energy prediction for planning-oriented evaluation.