This study addresses the challenge of bipedal navigation in a dynamic human-crowded environment, a research area that remains largely underexplored in the field of legged navigation. We propose two cascaded zonotope-based neural networks: a Pedestrian Prediction Network (PPN) for pedestrians' future trajectory prediction and an Ego-agent Social Network (ESN) for ego-agent social path planning. Representing future paths as zonotopes allows for efficient reachability-based planning and collision checking. The ESN is then integrated with a Model Predictive Controller (ESN-MPC) for footstep planning for our bipedal robot Digit designed by Agility Robotics. ESN-MPC solves for a collision-free optimal trajectory by optimizing through the gradients of ESN. ESN-MPC optimal trajectory is sent to the low-level controller for full-order simulation of Digit. The overall proposed framework is validated with extensive simulations on randomly generated initial settings with varying human crowd densities.

翻译：本研究探讨了在动态人群密集环境中双足导航的挑战，这一领域在腿部机器人导航研究中仍相对未被充分探索。我们提出了两种级联的基于zonotope的神经网络：用于预测行人未来轨迹的行人预测网络（PPN）和用于智能体社交路径规划的自我智能体社交网络（ESN）。将未来路径表示为zonotope可实现高效的基于可达性的路径规划和碰撞检测。随后，ESN与模型预测控制器（ESN-MPC）集成，用于由Agility Robotics设计的双足机器人Digit的落脚点规划。ESN-MPC通过优化ESN的梯度求解无碰撞最优轨迹，并将该最优轨迹发送至底层控制器，以完成Digit的全阶仿真。通过在不同人群密度的随机初始设置下进行大量仿真实验，验证了所提框架的整体有效性。