Robots utilize artificial neural networks (ANNs) to improve their ability to perform various tasks by mimicking the way human brains function. ANNs are composed of interconnected nodes, similar to neurons, that process information through weighted connections. When a robot is tasked with a specific job, such as navigating a maze or picking up objects, the neural network is trained on large datasets related to that task. During training, the model learns to recognize patterns and make decisions based on the input data it receives, enabling the robot to execute tasks more effectively.
For instance, in a robotic arm designed for sorting objects, an ANN can learn to identify different shapes and sizes. The robot's camera captures images of objects as input. The ANN processes these images and produces output that indicates how to sort the objects—whether to pick them up, where to place them, or how to avoid obstacles. By adjusting the weights of connections based on feedback, the ANN can improve its accuracy. Over time, this allows the robot to become more adept at performing the sorting task, even in varying conditions.
Furthermore, robots can also use ANNs for more advanced tasks, like autonomous navigation. For example, a mobile robot equipped with an ANN can analyze sensor data from its surroundings, such as distance measurements from lidar or visual inputs from a camera. The ANN processes this information to make decisions regarding movement, obstacle avoidance, and path planning. Continuous learning through reinforcement can also be applied here, enabling the robot to adapt to new environments and improve its navigation skills through experience. Overall, ANNs enhance the flexibility and efficiency of robots, allowing them to perform complex tasks with greater autonomy and precision.