Modeling and Control of a Hybrid Wheeled Jumping Robot

Abstract

In this paper, we study a wheeled robot with a prismatic extension joint. This allows the robot to build up momentum to perform jumps over obstacles and to swing up to the upright position after the loss of balance. We propose a template model for the class of such two-wheeled jumping robots. This model can be considered as the simplest wheeled-legged system. We provide an analytical derivation of the system dynamics which we use inside a model predictive controller (MPC). We study the behavior of the model and demonstrate highly dynamic motions such as swing-up and jumping. Furthermore, these motions are discovered through optimization from first principles. We evaluate the controller on a variety of tasks and uneven terrains in a simulator.

Application of Predictive Reinforcement Learning on Extendable Wheeled Systems

“…Goher [4] in 2019, presented the findings on aspects of system responses pitting Fuzzy Logic against PID controllers, in which Fuzzy Logic proved to be better in terms of performance. Yue et al [5] and Dinev, T. et al [6] have contributed in testing MPC algorithms rigorously, Yue et. al.…”

Multi-Fidelity Receding Horizon Planning for Multi-Contact Locomotion

“…Towards this end, local re-planning strategies-such as Receding Horizon Planning (RHP) [11]-can be a promising approach. Similar to Model Predictive Control (MPC) [12], [13], [14], [15], [16], RHP framework constantly plans online Fig. 1: Snapshots of our simulations (video available in https://youtu.be/3FjKPmnYxvU).…”