What are the best ways to use robot vision and perception for obstacle avoidance?

2 Deep learning

Another way to use robot vision and perception for obstacle avoidance is to apply deep learning techniques, such as convolutional neural networks (CNNs), to process the sensor data and extract useful features and patterns. Deep learning can help to improve the performance and accuracy of the obstacle detection and avoidance system, and to enable the robot to handle complex and diverse scenarios. For example, CNNs can be used to classify and segment the objects in the camera images, and to estimate their distances and poses. CNNs can also be used to fuse the data from different sensors, and to generate a semantic map of the environment. Deep learning can also help to enhance the robot's ability to learn from its own experience and feedback, and to adapt to changing conditions and situations.

3 Reactive control

A third way to use robot vision and perception for obstacle avoidance is to implement a reactive control strategy, which means that the robot responds to the sensor data and the obstacles in real time, without relying on a predefined map or plan. Reactive control can help to increase the speed and flexibility of the obstacle avoidance system, and to cope with dynamic and uncertain environments. For example, a reactive control strategy can use a potential field method, which assigns attractive and repulsive forces to the goal and the obstacles, respectively, and computes the optimal direction and velocity for the robot. A reactive control strategy can also use a behavior-based method, which defines a set of behaviors, such as go forward, turn left, or stop, and selects the appropriate one based on the sensor data and the obstacle situation.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?