予測的エネルギー認識に基づく公称歩容選択による車輪付き四脚ロボットの省エネ全方向移動
Energy-Efficient Omnidirectional Locomotion for Wheeled Quadrupeds via Predictive Energy-Aware Nominal Gait Selection
車輪付き四脚ロボットの消費電力を1秒先まで予測するネットワークを構築し、最も省エネな歩容を選んで強化学習で微調整することで、速度追従性能を保ちつつ最大35%の省エネを実現した。
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著者: Xu Yang, Wei Yang, Kaibo He, Bo Yang, Yanan Sui, Yilin Mo
分類: cs.RO
原文アブストラクト
Wheeled-legged robots combine the efficiency of wheels with the versatility of legs, but face significant energy optimization challenges when navigating diverse environments. In this work, we present a hierarchical control framework that integrates predictive power modeling with residual reinforcement learning to optimize omnidirectional locomotion efficiency for wheeled quadrupedal robots. Our approach employs a novel power prediction network that forecasts energy consumption across different gait patterns over a 1-second horizon, enabling intelligent selection of the most energy-efficient nominal gait. A reinforcement learning policy then generates residual adjustments to this nominal gait, fine-tuning the robot's actions to balance energy efficiency with performance objectives. Comparative analysis shows our method reduces energy consumption by up to 35\% compared to fixed-gait approaches while maintaining comparable velocity tracking performance. We validate our framework through extensive simulations and real-world experiments on a modified Unitree Go1 platform, demonstrating robust performance even under external disturbances. Videos and implementation details are available at \href{https://sites.google.com/view/switching-wpg}{https://sites.google.com/view/switching-wpg}.
