An Intelligent Hybrid Control to Enhance Applicability of Mobile Robots in Cluttered Environments

Abstract

Trajectory planning and tracking are the most important aspects of mobile robot research for industrial application. In this research, an intelligent hybrid control is presented to enhance the usability of mobile robots in environments cluttered with static obstacles. The control is hybrid in two ways. On one hand, the algorithm combines obstacles avoidance and trajectory generation. The generated trajectory acts as a reference path to be followed by the mobile robot. On the other hand, the algorithm not only generates trajectory but also tracks the generated trajectory. An optimization-based intelligent algorithm, simulated annealing, is designed to plan and generate a trajectory for collision-free robot motion in an environment with stationary obstacles. The PID (Proportional-Integral-Derivative) control algorithm is designed to track the generated trajectory with minimum error. Both algorithms are hybridized such that the generated trajectory becomes a reference trajectory for the tracking control algorithm. The simulated annealing generates a realistic trajectory that consists of a series of the best points with obstacles on the path. The best point is selected with the shortest distance from the robot's destination. These points are selected one from each of the grids, generated between the start and destination point of the robot motion. The dimensions of the robot are considered and included in the obstacles dimension to reduce the dimension complexity of the robot. The effectiveness of the proposed hybrid technique is tested in simulation and in real-time experiments for seven types of cluttered environments. The environments vary with size, shape, placement and arrangement of the obstacles. The results show that the simulated annealing generates a collision-free trajectory intelligently without trapping in local minima in these cluttered environments while the PID control tracks the reference trajectory with a maximum absolute error of 0.2 mm. Hence, the proposed method enhances the usability of mobile robots in environments cluttered with static objects.