Energy-Efficient Motion Planner for Legged Robots

2025-03-08

AI Summary

Legged robots face a fundamental trade-off between energy efficiency and robustness in online motion planning. Method: This paper proposes an energy-efficient optimization framework triggered dynamically by a parametric virtual foot placement set modeled beneath the hip joint. The framework jointly optimizes the geometric shape of the placement set, leg-lifting height, and swing duration, while formulating a bi-objective cost function balancing energy consumption and disturbance robustness under geometric constraints for online gait generation. Contributions/Results: (1) A novel dynamic placement-set triggering mechanism enables adaptation to slippery terrain, seamless gait transitions, and external disturbance rejection; (2) Extensive validation demonstrates generalizability in simulation and on the Unitree A1 quadruped; (3) Under medium-to-low-speed locomotion, the proposed method reduces transport cost (CoT) by 50.4% compared to the best model-free reinforcement learning baseline, while significantly enhancing robustness.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We propose an online motion planner for legged robot locomotion with the primary objective of achieving energy efficiency. The conceptual idea is to leverage a placement set of footstep positions based on the robot's body position to determine when and how to execute steps. In particular, the proposed planner uses virtual placement sets beneath the hip joints of the legs and executes a step when the foot is outside of such placement set. Furthermore, we propose a parameter design framework that considers both energy-efficiency and robustness measures to optimize the gait by changing the shape of the placement set along with other parameters, such as step height and swing time, as a function of walking speed. We show that the planner produces trajectories that have a low Cost of Transport (CoT) and high robustness measure, and evaluate our approach against model-free Reinforcement Learning (RL) and motion imitation using biological dog motion priors as the reference. Overall, within low to medium velocity range, we show a 50.4% improvement in CoT and improved robustness over model-free RL, our best performing baseline. Finally, we show ability to handle slippery surfaces, gait transitions, and disturbances in simulation and hardware with the Unitree A1 robot.

Problem

• Develops energy-efficient motion planner for legged robots.
• Optimizes gait using virtual footstep placement sets.
• Improves robustness and handles disturbances in various terrains.

Innovation

• Online motion planner for energy-efficient legged robots
• Virtual placement sets optimize footstep positions
• Parameter framework balances energy and robustness