Simulation Experiment for Mobile Robot Obstacle Avoidance

The simulation experiment for mobile robot obstacle avoidance involves a robot navigating through a predefined environment to avoid obstacles and reach a target destination. In this experiment, the robot must perform a series of operations, including: environment perception, path planning, obstacle avoidance, and motion control. First, the robot needs to perceive its surroundings—typically using simulated sensors such as LiDAR or depth cameras—to detect the position and dimensions of obstacles. This can be implemented in code by accessing sensor data arrays and applying filtering algorithms to reduce noise. Next, the robot must plan a collision-free path to the destination, taking into account factors such as path length, obstacle density, and kinematic constraints. Path planning algorithms like A*, Dijkstra, or Rapidly-exploring Random Trees (RRT) are commonly used, often with cost functions that balance efficiency and safety. Once the path is generated, the robot executes obstacle avoidance behaviors, which require robust motion control—often implemented using PID controllers or model predictive control (MPC) to ensure smooth and precise movement. After reaching the destination, performance metrics such as travel time, path smoothness, and success rate are evaluated. These analyses help refine algorithms and improve the robot’s autonomy and reliability. In summary, the obstacle avoidance simulation is a complex yet engaging research area that enhances robotic intelligence and technical capabilities. It also provides valuable insights for the development of future intelligent robotic systems.