Navigating a large-scaled robot in unknown and cluttered height-constrained environments is challenging. Not only is a fast and reliable planning algorithm required to go around obstacles, the robot should also be able to change its intrinsic dimension by crouching in order to travel underneath height-constrained regions. There are few mobile robots that are capable of handling such a challenge, and bipedal robots provide a solution. However, as bipedal robots have nonlinear and hybrid dynamics, trajectory planning while ensuring dynamic feasibility and safety on these robots is challenging. This paper presents an end-to-end autonomous navigation framework which leverages three layers of planners and a variable walking height controller to enable bipedal robots to safely explore height-constrained environments. A vertically-actuated Spring-Loaded Inverted Pendulum (vSLIP) model is introduced to capture the robot's coupled dynamics of planar walking and vertical walking height. This reduced-order model is utilized to optimize for long-term and short-term safe trajectory plans. A variable walking height controller is leveraged to enable the bipedal robot to maintain stable periodic walking gaits while following the planned trajectory. The entire framework is tested and experimentally validated using a bipedal robot Cassie. This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.

翻译：在未知且杂乱的高度受限环境中导航大型机器人具有挑战性。不仅需要快速且可靠的规划算法来规避障碍，机器人还应能够通过蹲伏改变自身固有尺寸，以穿过高度受限区域下方。能够应对这一挑战的移动机器人屈指可数，而双足机器人提供了一种解决方案。然而，由于双足机器人具有非线性和混合动力学特性，在保证动力学可行性和安全性的同时进行轨迹规划极具挑战性。本文提出了一种端到端的自主导航框架，该框架利用三层规划器和一个可变步行高度控制器，使双足机器人能够安全探索高度受限环境。引入了一个垂直驱动的弹簧负载倒立摆（vSLIP）模型，用于表征机器人在平面行走与垂直行走高度上的耦合动力学。该降阶模型用于优化长期和短期安全轨迹规划。利用可变步行高度控制器使双足机器人在跟踪规划轨迹的同时保持稳定的周期性步行步态。整个框架在双足机器人Cassie上进行了测试和实验验证，结果表明该自主导航系统能够可靠地驱动机器人在各种高度受限的杂乱环境中安全避障，并步行到达目标位置。