Dynamic modeling and stability optimization of a redundant mobile robot using a genetic algorithm

Abstract

SUMMARYKinematic reconfigurable mobile robots have the ability to change their structure to increase stability and decrease the probability of tipping over on rough terrain. If stability increases without decreasing center of mass height, the robot can pass more easily through bushes and rocky terrain. In this paper, an improved sample return rover is presented. The vehicle has a redundant rolling degree of freedom. A genetic algorithm utilizes this redundancy to optimize stability. Parametric motion equations of the robot were derived by considering Iterative Kane and Lagrange's dynamic equations. In this research, an optimal reconfiguration strategy for an improved SRR mobile robot in terms of the Force–Angle stability measure was designed using a genetic algorithm. A path-tracking nonlinear controller, which maintains the robot's maximum stability, was designed and simulated in MATLAB. In the simulation, the vehicle and end-effector paths and the terrain are predefined and the vehicle has constant velocity. The controller was found to successfully keep the end-effector to the desired path and maintained optimal stability. The robot was simulated using ADAMS for optimization evaluation.