Vision Aided Autonomous Navigation of Bipedal Robots in Height Constrained Environments

Navigating a largescaled robot in unknown and cluttered heightconstrained 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 heightconstrained 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. We present an endtoend visionaided autonomous navigation framework which leverages three layers of planners and a variable walking height controller to enable bipedal robots to safely explore heightconstrained environments. A verticallyactuated SpringLoaded Inverted Pendulum (vSLIP) model is introduced to capture the robot s coupled dynamics of planar walking and vertical walking height. This reducedorder model is