Estimation of Obstacle Locations Using a Distribution Model of Small Jumping Swarm Robots

To detect obstacles in narrow spaces, such as under-floor area, herein, we proposed a method that estimates the location of obstacles using a distributed model of swarm robots. First, we constructed a swarm robot model, in which a robot moves by jumping at regular intervals in a field enclosed by walls. We confirmed that, when no obstacles were placed in the field, the spread of the swarm robots followed a Gaussian distribution over a certain period. We then assumed that, even when obstacles were present, the distribution of the robots in the field would follow the Gaussian distribution except in the neighborhood of obstacles. Under this assumption, we proposed a method to clearly estimate the positions of obstacles by taking the difference between the approximated Gaussian distribution based on an average gross distribution of robots over a certain period and the actual average gross distribution of robots in each subdivided area of the field.