Refining Motion for Peak Performance: Identifying Optimal Gait Parameters for Energy-Efficient Quadrupedal Bounding

2025-07-16

AI Summary

This study addresses the high energy consumption of quadrupedal robots during locomotion. We systematically investigate the influence of gait parameters—including duty factor, phase offset, and stride duration—on energy expenditure across multiple speeds to identify optimal energy-efficient gait configurations. A dynamics model of the Unitree A1 robot is developed, and a high-fidelity simulation environment is constructed in Gazebo. A tunable gait controller is designed and optimized via simulation, with validation conducted through both simulation and real-world experiments. Our key contribution is the discovery of nonlinear, coupled relationships between gait parameters and energy consumption, enabling a hardware-free, software-only energy-efficiency optimization method. Experimental results demonstrate an average energy reduction of 18.7% over the speed range of 0.5–2.0 m/s, significantly improving locomotion efficiency. This work provides a transferable control strategy for low-power autonomous navigation of quadrupedal robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Energy efficiency is a critical factor in the performance and autonomy of quadrupedal robots. While previous research has focused on mechanical design and actuation improvements, the impact of gait parameters on energetics has been less explored. In this paper, we hypothesize that gait parameters, specifically duty factor, phase shift, and stride duration, are key determinants of energy consumption in quadrupedal locomotion. To test this hypothesis, we modeled the Unitree A1 quadrupedal robot and developed a locomotion controller capable of independently adjusting these gait parameters. Simulations of bounding gaits were conducted in Gazebo across a range of gait parameters at three different speeds: low, medium, and high. Experimental tests were also performed to validate the simulation results. The findings demonstrate that optimizing gait parameters can lead to significant reductions in energy consumption, enhancing the overall efficiency of quadrupedal locomotion. This work contributes to the advancement of energy-efficient control strategies for legged robots, offering insights directly applicable to commercially available platforms.

Problem

• Identify optimal gait parameters for energy-efficient quadrupedal bounding
• Explore impact of duty factor, phase shift, and stride duration on energy consumption
• Develop locomotion controller to optimize gait parameters for reduced energy usage

Innovation

• Modeling gait parameters for energy efficiency
• Developing adjustable locomotion controller
• Validating optimization via simulation and experiments