How do robots handle obstacle avoidance and path planning?

Robots handle obstacle avoidance and path planning through a combination of sensors, algorithms, and real-time decision-making. Sensors like LiDAR, cameras, ultrasonic rangefinders, or infrared detectors gather data about the robot’s surroundings. This data is processed to identify obstacles, map the environment, and determine safe paths. Algorithms such as potential fields, vector field histograms, or dynamic window approaches enable reactive obstacle avoidance by calculating immediate steering or velocity adjustments. For example, a robot vacuum might use infrared sensors to detect walls and ultrasonic sensors to spot furniture, adjusting its path on-the-fly to avoid collisions while maintaining coverage.

Path planning involves generating an optimal route from a starting point to a goal. Global planning algorithms like A* or Dijkstra’s algorithm precompute paths using a known map, prioritizing efficiency or shortest distance. Local planners, such as Rapidly-exploring Random Trees (RRT) or Model Predictive Control (MPC), handle dynamic environments by continuously updating the path based on real-time sensor input. For instance, an autonomous warehouse robot might use A* to plan a route around static shelves but switch to a local planner to navigate around moving forklifts. These algorithms balance computational efficiency with accuracy, often leveraging probabilistic methods to handle uncertainty in sensor data or environmental changes.

The integration of obstacle avoidance and path planning relies on frameworks like ROS (Robot Operating System), which modularize sensing, mapping, and control. SLAM (Simultaneous Localization and Mapping) builds and updates maps in real time, while motion controllers execute planned trajectories using PID loops or inverse kinematics. For example, a drone might use SLAM to map a forest, RRT* to plan a collision-free path through trees, and PID controllers to adjust thrust and orientation. Developers often simulate these systems in tools like Gazebo before deployment, testing edge cases like sensor noise or sudden obstacles. The result is a layered system where high-level planning and low-level reactive behaviors work together to ensure safe, efficient navigation.