SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images
This work addresses the challenge of safe autonomous navigation on the lunar surface for space exploration robots, but it is incremental as it builds on existing deep reinforcement learning techniques.
The paper tackled the problem of enabling a lunar rover to navigate and avoid hazardous obstacles like rocks using only camera images, by developing a visuomotor system with deep reinforcement learning and comparing neural network architectures and preprocessing methods, achieving real-time obstacle avoidance.
The visuomotor system of any animal is critical for its survival, and the development of a complex one within humans is large factor in our success as a species on Earth. This system is an essential part of our ability to adapt to our environment. We use this system continuously throughout the day, when picking something up, or walking around while avoiding bumping into objects. Equipping robots with such capabilities will help produce more intelligent locomotion with the ability to more easily understand their surroundings and to move safely. In particular, such capabilities are desirable for traversing the lunar surface, as it is full of hazardous obstacles, such as rocks. These obstacles need to be identified and avoided in real time. This paper seeks to demonstrate the development of a visuomotor system within a robot for navigation and obstacle avoidance, with complex rock shaped objects representing hazards. Our approach uses deep reinforcement learning with only image data. In this paper, we compare the results from several neural network architectures and a preprocessing methodology which includes producing a segmented image and downsampling.