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

TL;DR

This paper addresses dynamic jumping for humanoid robots by incorporating centroidal momentum and non-constant centroidal composite rigid body inertia into planning and control. It introduces CDM-MPC, an integrated framework combining an optimization-based kinodynamic motion planner, a real-time centroidal dynamics MPC, a centroidal momentum-based IK, and a landing heuristic to stabilize high-impact landings, validated on KUAVO in simulation and experiments. Results show improved disturbance rejection, robust landings, and smooth transitions across jumping velocities, with demonstrated applicability to walking locomotion. The work highlights the importance of accounting for centroidal momentum and CCRBI variability for dynamic tasks and points toward unified walking/running/jumping control and potential reinforcement learning enhancements.

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.

Figures (9)

• Figure 1: The proposed integrated dynamic planning and control framework endows bipedal robots capable of continuously forward jumping. The trajectories of the foot and the torso links are plotted in thin and bold black lines, respectively.
• Figure 2: Hardware design and configuration of the bipedal humanoid robot KUAVO. Each leg contains 5 dof: 3 dof for the hip joint, 1 dof for the knee joint and 1 dof for the ankle joint.
• Figure 3: External forces analysis. In the $xOz$ plane, the robot is subjected to gravitational force $m\pmb{g}$ and ground reaction force $\pmb{f}_{\rho}$.
• Figure 4: The CDM-MPC dynamic planning and control framework. (i) The CDM-based kinodynamic motion planner produces the centroidal momentum reference trajectory. (ii) The real-time MPC controller provides accurate trajectory tracking and fast replanning under disturbances. (iii) The centroidal moment-based IK solves whole-body trajectory without simplifying leg dynamics. (iv) The landing heuristic controller guarantees robust landing stabilization.
• Figure 5: Case1 (Simulation): Disturbance rejection performance study during in-place jumping. (a) The SRBM-MPC method cannot maintain the robot's stability when subjected to disturbance torque, leading to its collapse. (c) Conversely, the CDM-MPC method preserves the robot's stability throughout the entire flight phase, culminating in a successful landing. (b)(e) depict the joint configuration of hip pitch and knee pitch joints while (c)(f) depict the pitch angle of the torso and the CoM height for both methods, respectively.