Safe navigation in real-time is an essential task for humanoid robots in real-world deployment. Since humanoid robots are inherently underactuated thanks to unilateral ground contacts, a path is considered safe if it is obstacle-free and respects the robot's physical limitations and underlying dynamics. Existing approaches often decouple path planning from gait control due to the significant computational challenge caused by the full-order robot dynamics. In this work, we develop a unified, safe path and gait planning framework that can be evaluated online in real-time, allowing the robot to navigate clustered environments while sustaining stable locomotion. Our approach uses the popular Linear Inverted Pendulum (LIP) model as a template model to represent walking dynamics. It incorporates heading angles in the model to evaluate kinematic constraints essential for physically feasible gaits properly. In addition, we leverage discrete control barrier functions (DCBF) for obstacle avoidance, ensuring that the subsequent foot placement provides a safe navigation path within clustered environments. To guarantee real-time computation, we use a novel approximation of the DCBF to produce linear DCBF (LDCBF) constraints. We validate the proposed approach in simulation using a Digit robot in randomly generated environments. The results demonstrate that our approach can generate safe gaits for a non-trivial humanoid robot to navigate environments with randomly generated obstacles in real-time.

翻译：实时安全导航是人形机器人在实际部署中的关键任务。由于单侧地面接触导致人形机器人本质上是欠驱动的，一条路径若同时满足无障碍物、符合机器人物理限制及底层动力学约束，方可被视为安全路径。现有方法常因全阶机器人动力学带来的巨大计算挑战，将路径规划与步态控制解耦处理。本研究开发了一种统一的安全路径与步态规划框架，能够在线实时计算，使机器人在保持稳定运动的同时穿越密集环境。该方法采用流行的线性倒立摆（LIP）模型作为描述行走动力学的模板模型，通过在模型中引入航向角来准确评估实现物理可行步态所需的关键运动学约束。此外，我们利用离散控制屏障函数（DCBF）进行避障，确保后续足部落点能在密集环境中提供安全导航路径。为保证实时计算性能，我们采用一种新颖的DCBF近似方法生成线性DCBF（LDCBF）约束。通过在随机生成的环境中使用Digit机器人进行仿真验证，结果表明本方法能够为复杂人形机器人实时生成安全步态，使其在随机障碍环境中实现导航。