Vision Based Obstacle Avoidance and Odometery for Swarms of Small Size Robots

Abstract

In multi-robotic systems, an approach to the coordination of multiple robots with each other is called swarm robotics. In swarm robotic systems, small size robots with limited memory and processing resources are used. Integration of vision sensors in such robots can complicate the design of the robots but at the same time, a single vision sensor can be used for multiple objectives as it provide rich surrounding information. As the vision algorithms are normally computationally demanding and robots in swarm systems has limited memory and processing capabilities, so the requirements of light weight vision algorithms also arises. In this research, the use of vision sensor information is made for achieving multiple objectives. A solution to obstacle avoidance, which is the basic requirement as robots move in a cluttered environment and also odometry which is essential for robot localization, is provided using only visual clues. The approach developed in this research is computationally less expensive and suitable for small size robots, where processing and memory constraints limit the use of computationally expensive approaches. To achieve this a library of vision algorithms is developed and customized for Blackfin processor based robotic systems.