MATLAB Simulation Implementation for Robot Obstacle Avoidance

This article presents a comprehensive guide to implementing robot obstacle avoidance simulation using MATLAB, complete with source code and animated visualization capabilities. The simulation program enables robots to perceive their environment through integrated sensor models that detect distance measurements and obstacle positions. Based on this sensory input, the robot executes avoidance maneuvers using navigation algorithms to prevent collisions with obstacles. The implementation utilizes MATLAB's robotics toolbox and includes key functions for sensor data processing, obstacle detection algorithms, and motion control systems. The source code demonstrates how to programmatically define obstacle geometries, implement distance calculation methods using Euclidean distance formulas, and create real-time path planning routines. The animation component visualizes the robot's movement trajectory, sensor range coverage, and dynamic interaction with obstacles. This article provides detailed explanations of the MATLAB code structure, covering critical implementation aspects such as the main simulation loop, sensor modeling techniques, and collision avoidance decision-making logic. The simulation process demonstrates how to model robot kinematics, implement control algorithms, and visualize results using MATLAB's plotting functions. Additionally, the discussion addresses current limitations of the simulation approach, including computational constraints in complex environments and sensor modeling accuracy. The article concludes with potential improvement directions such as implementing advanced path planning algorithms (A*, RRT), enhancing sensor fusion techniques, and incorporating machine learning approaches for adaptive obstacle avoidance, aiming to facilitate broader applications and further research in robotic navigation systems.