Optimal configuration control of planar leg/wheel mobile robots for flexible obstacle avoidance

Abstract

In this study, we propose a method that can be used to enhance the configuration of mobile robots equipped with articulated legs and wheels by simultaneously optimizing leg joint angles and wheel positions using model predictive control (MPC). To present a manageable expression for MPC, the kinematics of a robot that has highly articulated legs with wheels at their ends are represented as serially connected constrained links. The other end of the leg is constrained by its position relative to the robot body. To actively allocate and adapt movements to the surrounding environment, we use a potential field method applied to each wheel. Our method is based on the following strategy. First, we will design a simple collision-free reference path for a point mass, and then we will apply MPC to induce optimal path tracking control that balances the input and artificial potential costs. This will allow the robot to adapt its wheel position configurations to the surrounding environment. Based on that strategy, we conducted numerical simulations and experiments using an actual mobile robot to verify the efficacy and feasibility of our proposed method, the results of which demonstrated its effective ability to adaptively configure its articulated legs in ways that allowed the robot to avoid obstacles.