面向安全双足机器人导航的统一路径与步态规划
Unified Path and Gait Planning for Safe Bipedal Robot Navigation
AI总结：
针对双足机器人分层规划路径与步态易忽略物理约束导致不安全的问题，提出基于LIP模型和MPC的统一规划方法，利用离散时间CBF生成落脚点，在20个随机环境中验证了其安全性和鲁棒性的提升。
摘要：
安全的路径与步态规划对于双足机器人在复杂现实环境中导航至关重要。现有方法通常以分层方式单独规划路径和步态，由于忽略了步行机器人的物理约束，可能导致不安全的运动。安全关键的路径不仅要避开障碍物，还必须确保机器人的步态满足其动力学和运动学约束。本文提出了一种新方法，通过使用表示双足运动的 Linear Inverted Pendulum (LIP) 模型的 Model Predictive Control (MPC)，统一了路径规划与步态规划。该方法考虑了障碍物等环境约束，以及机器人的运动学和动力学约束。通过使用离散时间 Control Barrier Functions 进行避障，该方法生成下一个落脚点位置，确保在密集环境中实现稳健的行走步态和安全的导航路径。我们在仿真中使用 Digit 机器人在 20 个随机创建的环境中验证了该方法。结果表明，与分层的路径和步态规划框架相比，该方法在安全性和鲁棒性方面表现出更好的性能。
英文摘要：
Safe path and gait planning are essential for bipedal robots to navigate complex real-world environments. The prevailing approaches often plan the path and gait separately in a hierarchical fashion, potentially resulting in unsafe movements due to neglecting the physical constraints of walking robots. A safety-critical path must not only avoid obstacles but also ensure that the robot's gaits are subject to its dynamic and kinematic constraints. This work presents a novel approach that unifies path planning and gait planning via a Model Predictive Control (MPC) using the Linear Inverted Pendulum (LIP) model representing bipedal locomotion. This approach considers environmental constraints, such as obstacles, and the robot's kinematics and dynamics constraints. By using discrete-time Control Barrier Functions for obstacle avoidance, our approach generates the next foot landing position, ensuring robust walking gaits and a safe navigation path within clustered environments. We validated our proposed approach in simulation using a Digit robot in 20 randomly created environments. The results demonstrate improved performance in terms of safety and robustness when compared to hierarchical path and gait planning frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
