Path planning and trajectroy tracking of a mobile robot using bio-inspired optimization algorithms and PID control

Path planning and trajectory tacking are the fundamental task in mobile robotic science, and they enable the robot to navigate autonomously. In this work, the path planning task is carried out using three bio-inspired optimization algorithms, including PSO, ABC and FA. The duty of the algorithms is to determine a collision-free path through fixed obstacles in the working environment. The maximum speed of the robot is applied to the optimization problem as a constraint. In order to evaluate the performance of the algorithms, four workspaces with different obstacle layout are simulated in MATLAB, and the quality of path planning task is analyzed statistically and numerically, considering four different criteria, including, convergency quality, convergency time, path length and success rate. In the next step, a control model is designed to track the path curve determined by the path planning algorithms. A PID-based control structure is simulated in MAT LAB Simulink and the controller was able to track the pre-determined trajectories with proper approximation. The controller is applied on a dynamic model of a two-wheeled mobile robot offered by [1]. In order to validate the control inputs it is necessary to apply them on a real platform. The experimental study is implemented on a two-wheeled mobile robot which is designed and built based on the authors' previous paper [2] in various enverioment and obstacles. The result shows control inputs were applied to the real robot and the robot was able to imitate the applied path curve, and find its way toward the target point without colliding obstacles in real and simulation task.