CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

TL;DR

This paper presents CDM-MPC, a comprehensive control framework for bipedal robots that integrates dynamic planning and control, effectively managing centroidal momentum and inertia variations for robust jumping maneuvers.

Contribution

The paper introduces a novel integrated framework combining kinodynamic planning, MPC, and inverse kinematics to enhance bipedal robot jumping capabilities considering complex dynamics.

Findings

Successful simulation and experimental validation on KUAVO robot.

Improved stability during high-impact landings.

Effective real-time trajectory tracking and replanning.

Abstract

Performing acrobatic maneuvers like dynamic jumping in bipedal robots presents significant challenges in terms of actuation, motion planning, and control. Traditional approaches to these tasks often simplify dynamics to enhance computational efficiency, potentially overlooking critical factors such as the control of centroidal angular momentum (CAM) and the variability of centroidal composite rigid body inertia (CCRBI). This paper introduces a novel integrated dynamic planning and control framework, termed centroidal dynamics model-based model predictive control (CDM-MPC), designed for robust jumping control that fully considers centroidal momentum and non-constant CCRBI. The framework comprises an optimization-based kinodynamic motion planner and an MPC controller for real-time trajectory tracking and replanning. Additionally, a centroidal momentum-based inverse kinematics (IK) solver and a landing heuristic controller are developed to ensure stability during high-impact landings. The efficacy of the CDM-MPC framework is validated through extensive testing on the full-sized humanoid robot KUAVO in both simulations and experiments.