Height Control and Optimal Torque Planning for Jumping With Wheeled-Bipedal Robots

TL;DR

This paper introduces a novel dynamical model and an optimization method for precise height control in wheeled-bipedal robots, significantly reducing errors and energy consumption during jumping.

Contribution

It proposes the W-JBD model for accurate height control and the BOTP method for optimal torque planning without detailed dynamic models.

Findings

BOTP reduces height error by 82.3%

Energy cost decreases by 26.9% with continuous torque

The approach converges quickly, averaging 40 iterations

Abstract

This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of underactuated, nonlinear estimation, and instantaneous impact in the jumping process, accurate control of the wheeled-bipedal robot's jumping height is complicated. In reality, robots often jump at excessive height to ensure safety, causing additional motor loss, greater ground reaction force and more energy consumption. To solve this problem, a novel wheeled-bipedal jumping dynamical model(W-JBD) is proposed to achieve accurate height control. It performs well but not suitable for the real robot because the torque has a striking step. Therefore, the Bayesian optimization for torque planning method(BOTP) is proposed, which can obtain the optimal torque planning without accurate dynamic model and within few iterations. BOTP method can reduce 82.3% height error, 26.9% energy cost with continuous torque curve. This result is validated in the Webots simulation platform. Based on the torque curve obtained in the W-JBD model to narrow the searching space, BOTP can quickly converge (40 times on average). Cooperating W-JBD model and BOTP method, it is possible to achieve the height control of real robots with reasonable times of experiments.