Robots avoid collisions in dynamic environments using a combination of sensors, real-time data processing, and path-planning algorithms. Sensors like LiDAR, cameras, ultrasonic sensors, or radar continuously monitor the robot’s surroundings to detect obstacles, including moving objects. Algorithms process this data to build a real-time map of the environment and predict the motion of nearby objects. For example, an autonomous delivery robot in a warehouse uses LiDAR to detect pallets, forklifts, or workers, then calculates safe paths while adjusting its speed and direction to avoid collisions. The core challenge is balancing speed and accuracy: the system must react quickly to changes without overloading computational resources.
Collision avoidance relies heavily on path-planning techniques like the A* algorithm, Rapidly-exploring Random Trees (RRT), or Dynamic Window Approach (DWA). These algorithms generate potential paths and evaluate them for safety based on factors like obstacle proximity, velocity, and acceleration limits. In dynamic settings, robots also use velocity obstacles or model predictive control to anticipate where moving objects might be in the near future. For instance, a drone navigating through a forest might use RRT to explore multiple paths, then discard routes where branches sway into its projected path. Additionally, machine learning models, such as neural networks trained on obstacle scenarios, can improve prediction accuracy in complex environments like crowded sidewalks or construction sites.
Redundancy and fail-safes are critical for reliability. Robots often combine multiple sensor types (e.g., cameras with LiDAR) to cross-validate data and handle sensor failures. Sensor fusion techniques, like Kalman filters, merge data streams to create a more accurate environmental model. If a collision risk is detected, robots may trigger emergency protocols like stopping, retreating, or rerouting. For example, industrial robotic arms in factories use force-torque sensors to detect unexpected contact and immediately halt motion. These layers of safety ensure robots operate safely even when sensors are occluded or algorithms face edge cases, such as sudden obstacle appearances or unpredictable human behavior.