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 vision-aided 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:在一个未知且杂乱无章的高度限制环境中, 大型机器人在未知且杂乱无章的环境中导航是一个挑战。 不仅需要快速和可靠的规划算法, 才能绕过障碍, 机器人也应该能够通过弯曲来改变其内在层面, 从而在高度限制地区下旅行。 很少有移动机器人能够应对这一挑战, 双向机器人提供了解决方案。 但是, 由于双向机器人具有非线性和混合动态, 轨迹规划同时确保这些机器人的动态可行性和安全具有挑战性。 本文展示了一个端到端的、 愿景辅助的自主导航框架, 利用三层规划者和可变步行高度控制器来让双层机器人安全地探索高度限制的环境。 引入了垂直激活的春季旋转旋转的彭杜伦( VSLIP) 模型, 以捕捉机器人在计划行走和垂直行走高度的动态。 这一减序模型被用来优化长期和短期的安全轨道计划。 一个可变高度高度控制器, 使双向高度的行行行车高度控制器控制器可以让双向的机器人运行轨道运行, 同时验证一个稳定的飞行器运行一个稳定的轨道。 。 正在展示一个稳定的轨道, 运行一个稳定的轨道, 向一个稳定的轨道运行中, 并展示一个稳定的轨道。