A Comparative Analysis of Optimization Algorithms for Control in Autonomous Robots Navigation

Effective control is crucial for the autonomous navigation of robots, ensuring safe and efficient maneuvering through complex environments. This study undertakes a comparative analysis of metaheuristic optimization algorithms for altitude and attitude stabilization of a complex flying robot, specifically a quadrotor. The Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Differential Evolution (DE) algorithms serve to adjust the control laws using the Integral Square Error (ISE). The evaluation criteria include dynamic performance metrics (overshoot, stabilization error, settling time, rise time, mean error, and standard deviation), energy consumption, and disturbance rejection. Among these algorithms, the DE algorithm demonstrates superior performance.