Robots handle obstacle avoidance and path planning through a combination of sensors, algorithms, and programming logic designed to navigate their environment safely and efficiently. At the core of this process are sensors such as cameras, LiDAR, and ultrasonic sensors, which allow the robot to gather information about its surroundings. These sensors help the robot to detect obstacles and determine their distance, shape, and size. For example, a robot equipped with LiDAR can create a detailed 3D map of its environment, identifying obstacles like walls, furniture, or people in real-time.
Once the robot collects data about its environment, it uses algorithms for path planning. Path planning involves calculating a route from a starting point to a destination while avoiding obstacles. Common methods include grid-based search algorithms, like A*, which map out the environment in a grid format, and are good for static conditions. For dynamic environments where obstacles may move, more advanced algorithms like Rapidly-exploring Random Trees (RRT) or Dynamic Window Approach (DWA) can be used. These algorithms allow robots to quickly recalibrate their paths based on new sensor data, allowing them to respond to obstacles that may appear suddenly.
In practice, a mobile robot navigating a home may use simple rules to avoid obstacles. For instance, if it detects a chair blocking its path, it can either stop and assess alternative routes or backtrack to find a way around. Integration of the path planning algorithms with obstacle detection allows the robot to make real-time decisions and adapt to changes in its environment. Moreover, some robots use machine learning techniques to improve their navigation skills over time, learning from past experiences to handle similar obstacles more effectively in the future. This combination of sensors and intelligent algorithms is key to enabling robots to navigate complex spaces safely and autonomously.