Abstract
Robot systems are gradually becoming more common in order to perform tasks that people can't. A multiple robot system is very difficult to work because the environment is very uncertain and information is often incomplete. Collision avoidance is a key problem, as autonomous robots must not collide with anything. There is pressure to use less and cheaper, lower quality sensors for widely spreading. Multiple robot systems will use collision avoidance algorithms which are based on low quality data, to in support of this situation. In this research, we designed a collision avoidance algorithm which use reinforcement learning to allow the robots to learn the most effective strategy. Computer simulation experiments were conducted in varying settings. We also experimented with real robots: autonomous lawn mowers RL500, and similarly good results were obtained.
