A Fast Motion and Foothold Planning Framework for Legged Robots on Discrete Terrain

Abstract

Legged robot proved their capability to cross complex terrain in recent research, yet the autonomy of robots on discrete terrain still needs to be enhanced since it requires a full stack framework. This paper introduces a real-time motion and foothold planning framework tailored for legged robots navigating uneven terrains, such as stepping stones. Our approach addresses the critical challenges of determining feasible global paths and local footholds to enhance autonomous mobility across complex landscapes. By using a sampling-based global path planner integrated with terrain segmentation and the robot’s kinematic model, our framework swiftly generates viable navigation paths. Concurrently, it utilizes a Mixed Integer Programming (MIP) methodology for real-time foothold optimization, ensuring the robot’s stability and safety through dynamic terrain interaction. Finally, an execution layer including Model Predictive Control (MPC) and Whole-Body Control (WBC) generates the robots’ motion. Simulation and real-world experiments demonstrate that our framework improves legged robots’ adaptability on discrete terrains.