Unified Control Method for Complex Terrain Locomotion and Fall Recovery of Quadruped Robots—A Study Based on Deep Reinforcement Learning

随着四足机器人在非结构化环境中的应用需求日益增加，如何应对外部扰动和复杂地形引发的失衡与摔倒，成为了机器人自主性和任务连续性面临的重要挑战。传统的摔倒恢复方法依赖外部干预，限制了机器人的自主性，难以满足复杂应用中的需求。尽管深度强化学习在运动控制方面取得了一定进展，但在摔倒后的自主恢复和基于地形特征的动态恢复策略方面的研究仍显不足。本文提出了一种基于深度强化学习的统一运动与恢复控制方法，使四足机器人能够在复杂地形中行走，并自主恢复摔倒。该方法结合了摔倒恢复因子、动态增长策略和安全约束优化，解决了现有方法中的不足。实验表明，机器人能够在不同地形条件下快速恢复并稳定过渡到行走状态，表现出较强的适应性和鲁棒性。本研究为四足机器人在高风险应用中的自主执行能力提供了有效的解决方案。With the growing demand for quadruped robots operating in unstructured environments, addressing instability and falls caused by external disturbances and complex terrains has become a critical challenge for ensuring robot autonomy and mission continuity. Traditional fall recovery methods often rely on external interventions, which limit the autonomy of the robot and fail to meet the needs of complex, real-world applications. Although deep reinforcement learning (DRL) has made notable progress in motion control, research on autonomous post-fall recovery and dynamic recovery strategies based on terrain features remains limited. In this paper, we propose a unified locomotion and recovery control framework based on deep reinforcement learning, enabling quadruped robots to walk over complex terrains and autonomously recover from falls. The framework integrates a fall recovery factor, a dynamic scheduling strategy, and safety-constrained optimization to address the limitations of existing approaches. Specifically, a non-symmetric actor-critic architecture is adopted, enhanced with a context-aided estimator to improve terrain-aware decision-making. Additionally, a dynamic β-VAE latent constraint strategy is introduced to facilitate stable training, while the NP3O algorithm ensures safe and efficient policy optimization under torque and stability constraints. Extensive experiments demonstrate that the proposed method enables quadruped robots to quickly recover from falls under various terrain conditions and transition smoothly back into locomotion. The robots exhibit strong adaptability and robustness, significantly improving their operational autonomy in high-risk environments. This study provides an effective solution for enhancing the autonomous capabilities of quadruped robots in real-world applications involving challenging and hazardous terrains.