Stable, Autonomous, Unknown Terrain Locomotion for Quadrupeds Based on Visual Feedback and Mixed-Integer Convex Optimization

Abstract

This paper presents a complete motion planning approach for quadruped locomotion across an unknown terrain using a framework based on mixed-integer convex optimization and visual feedback. Vision data is used to find convex polygons in the surrounding environment, which acts as potentially feasible foothold regions. Then, a goal position is initially provided, which the best feasible destination planner uses to solve for an actual feasible goal position based on the extracted polygons. Next, a footstep planner uses the feasible goal position to plan a fixed number of footsteps, which may or may not result in the robot reaching the position. The center of mass (COM) trajectory planner using quadratic programming is extended to solve for a trajectory in 3D space while maintaining convexity, which reduces the computation time, allowing the robot to plan and execute motions online. The suggested method is implemented as a policy rather than a path planner, but its performance as a path planner is also shown. The approach is verified on both simulation and on a physical robot, ALPHRED, walking on various unknown terrains.