Robots avoid collisions in dynamic environments through a combination of sensors, algorithms, and real-time processing. The primary approach is to use various types of sensors such as cameras, LiDAR, ultrasonic sensors, and infrared detectors. These sensors gather data about the robot's surroundings, allowing it to identify obstacles and moving objects in its path. For example, a delivery robot might use a camera for visual input and LiDAR to measure distances, creating a 3D map of its environment.
Once the robot has collected sensor data, it processes this information using algorithms that determine the best course of action. One common method is known as the “potential field” approach, where the robot treats obstacles as repelling forces that drive it away. Conversely, the target destination acts as an attractive force. By calculating these forces in real-time, the robot can adjust its movement to navigate around obstacles smoothly. Additionally, pathfinding algorithms such as A* or RRT (Rapidly-exploring Random Tree) can be used to plot a safe route that avoids collisions while considering both static obstacles and dynamic objects.
Finally, the robot must continually update its understanding of the environment. This is typically achieved by employing a technique called Sensor Fusion, which combines data from multiple sensors to create a comprehensive picture of the surroundings. By constantly analyzing this information, the robot can make split-second decisions to adjust its speed and trajectory. For instance, if a person suddenly steps into the robot's path, it can quickly react by slowing down or changing direction. This ability to interpret sensor data, process it effectively, and act accordingly allows robots to navigate complex, changing environments safely.