This paper introduces a novel multi-step preview foot placement planning algorithm designed to enhance the robustness of bipedal robotic walking across challenging terrains with restricted footholds. Traditional one-step preview planning struggles to maintain stability when stepping areas are severely limited, such as with random stepping stones. In this work, we developed a discrete-time Model Predictive Control (MPC) based on the step-to-step discrete evolution of the Divergent Component of Motion (DCM) of bipedal locomotion. This approach adaptively changes the step duration for optimal foot placement under constraints, thereby ensuring the robot's operational viability over multiple future steps and significantly improving its ability to navigate through environments with tight constraints on possible footholds. The effectiveness of this planning algorithm is demonstrated through simulations that include a variety of complex stepping-stone configurations and external perturbations. These tests underscore the algorithm's improved performance for navigating foothold-restricted environments, even with the presence of external disturbances.

翻译：本文提出一种新颖的多步预测足部落点规划算法，旨在增强双足机器人在具有受限立足点的复杂地形上的鲁棒行走能力。传统单步预测规划在步行区域严重受限时（例如随机踏脚石场景）难以维持稳定性。本研究基于双足步态运动发散分量（DCM）的步间离散演化特性，开发了离散时间模型预测控制（MPC）方法。该方法通过自适应调整步长持续时间，在约束条件下实现最优足部落点，从而确保机器人在未来多步行走中的操作可行性，显著提升其在立足点严格受限环境中的导航能力。通过包含多种复杂踏脚石构型和外部扰动的仿真实验验证了该规划算法的有效性。测试结果表明，即使在存在外部干扰的情况下，该算法在受限立足点环境中的导航性能仍得到显著提升。