Obstacle Avoidance for Wheeled Mobile Robots

Obstacle avoidance technology for wheeled mobile robots enhances their mobility in constrained environments by preventing collisions with obstacles. To achieve this, robots are typically equipped with multiple sensors such as LiDAR, cameras, and infrared sensors, which collect environmental data across different scenarios. Through processing algorithms implemented in code (often using ROS packages or MATLAB toolboxes), robots can calculate their own position and map surrounding environments, enabling better motion path planning through algorithms like A* or RRT (Rapidly-exploring Random Tree) to achieve desired movement patterns.

Furthermore, by integrating motion controllers (typically implemented via PID controllers or model predictive control in embedded systems), robots can perform autonomous behaviors such as obstacle avoidance and target tracking, accomplishing more complex motion tasks. These technical implementations not only improve robot movement efficiency and precision through real-time feedback loops, but also create diverse application scenarios across industries including automated warehousing (using SLAM algorithms for navigation), logistics distribution, and smart home systems.