Optimizing Controller Tuning using Bio-Inspired Algorithms for Robot Path Tracking

In the field of industrial and commercial robotics, there has been a recent boom in research towards the navigation of wheel-based mobile robots. There are numerous aspects of this complex problem that need fixing, including issues with sensory data gathering and processing, trajectory planning, and controlling motion. In the field of motion control, creating reliable trajectory controllers is the highest priority. This study improves the Proportional-Integral-Derivative (PID) controller using Ant Colony Optimization (ACO) and Cat Swarm Optimization (CSO) to get the best possible trajectory tracking. The kinematics of a two-wheeled mobile robot serve as the foundation for its mathematical model. The user-specified reference trajectory is given to the robot. The conventional PID, ACO-PID, and CSO-PID controller's trajectory tracking capacity are evaluated using the metrics. The performance metrics such as the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) are taken. According to the simulation results, the CSO-PID controller beats the conventional PID and ACO-PID controllers in terms of error metrics. This discovery highlights the effectiveness of using CSO-PID to improve the trajectory-tracking performance of mobile robots.