Real-time Trajectory Optimization and Control for Ball Bumping with Quadruped Robots

TL;DR

This paper presents a real-time trajectory planning and control framework for quadruped robots to perform ball bumping, combining nonlinear optimization and model predictive control to achieve accurate, time-sensitive motions validated on a real robot.

Contribution

It introduces a novel real-time planning and control scheme for ball bumping with quadruped robots, incorporating strict time-state constraints and validated on physical hardware.

Findings

Planning can be computed in approximately 60ms on average.

The framework enables successful ball bumping with various initializations.

Validated on a real Aliengo robot with successful real-time performance.

Abstract

This paper studies real-time motion planning and control for ball bumping motion with quadruped robots. To enable the quadruped to bump the flying ball with different initializations, we develop a nonlinear trajectory optimization-based planning scheme that jointly identifies the take-off time and state to achieve accurate ball hitting during the flight phase. Such a planning scheme employs a two-dimensional single rigid body model that achieves a satisfactory balance between accuracy and efficiency for the highly time-sensitive task. To precisely execute the planned motion, the tracking controller needs to incorporate the strict time-state constraint imposed on the take-off and ball-hitting events. To this end, we develop an improved model predictive controller that respects the critical time-state constraints. The proposed planning and control framework is validated with a real Aliengo robot. Experiments show that the problem planning approach can be computed in approximately 60ms on average, enabling successful accomplishment of the ball bumping motion with various initializations in real time.