Prototype of a small autonomous vehicle
Prototype of a small autonomous vehicle (AGV) saw through the stereo camera used for the navigation. The development of an inexpensive AGV, but high performing, for small businesses and the light industry, allows for almost unlimited applications.
Artificial Intelligence is playing an essential role in the robot vision.
Combining machine learning (branch of artificial intelligence) and robot vision is enabling robots to navigate in complex environments. In the past AGVs were been very limited in their ability to move around an environment. They were programmed to execute a specific path, often guided by signals such as magnetic strips or lasers from devices installed specifically for that purpose. Past AGVs are moreover limited in their ability to respond to unexpected obstacles, not able to identify an alternative route.
Combining machine learning and robot vision results in the ability for a robot to go from one point to another autonomously. The robot uses a preprogrammed map of the environment, or can build a map in real time. It can identify its location within an environment, plan a path to the desired endpoint, sensing obstacles and changing its planned path in real time.
Robot navigation requires specific techniques for guiding a mobile robot to the desired destination. This project presents a new approach for autonomous navigation using machine learning techniques such as Convolutional Neural Network to identify markers from images and Robot Operating System and Object Position Discovery system to navigate towards these markers.