Improving Inertial Odometry Through Particle Swarm Optimization in the RoboCup Small Size League

Small Size League (SSL) robots require mobile navigation to interact with their surroundings. Therefore, robots may rely on odometry to track their movement from the actuator’s data and navigate. The odometry is based on the robot’s kinematic model, which explains how actuators influence movement. However, robot’s kinematic models have parameter inaccuracy and cause systematic errors that accumulate. This study proposes to optimize odometry accuracy using Particle Swarm Optimization (PSO). The method records the robot’s movement from its sensor to simulate the traveled path with different robot’s kinematic models enabling parameters optimization. The proposed technique improved an SSL odometry accuracy by 76%, with less than 5 cm error in a 10-m path. With a reduced computational cost, it enables longer autonomous navigation for SSL robots and outperforms previous methods.