Bio-inspired search strategies for robot swarms

Our goal is as follows: build a suite/swarm of (very) small robots that can search a room for a “target”. We envision that the robots will be about the size of a quarter dollar, or smaller, and have a sensor or sensors that “sniff” out the desired target. For example, the target could be a bomb and the robot sensors would be a chemical detectors that can distinguish the bomb from its surroundings. Or the target could be a radiation leak and the sensors would be radiation detectors. In each search scenario, we assume that the target gives off a diffuse residue that can be detected with a sensor. It is not very efficient to have the suite of robots looking randomly around the room hoping to “get lucky” and find the target. There needs to be some way to coordinate the movements of the many robots. There needs to be an algorithm that can guide the robots toward promising regions to search while not getting distracted by local variations. The search algorithm must have the following constraints:  The search algorithm should be distributed among the many robots. If the algorithm is located in one robot, then the system will fail if that robot fails.  The search algorithm should be computationally simple. The processor on each bot is small, has limited memory, and there is a limited power source (a battery) so the processor needs to be power efficient. Therefore, the processor will be a simple processor.  The algorithm needs to be scalable from one robot up to 10’s, 100’s, even 1000’s of robots. The upper limit on the number of robots will be set by the communication links among the robots; there needs to be a way to share information among the robots without requiring lots of communication traffic.  The search algorithm must allow for contiguous movement of the robots. This chapter will describe two search strategies for robot swarms that are based on biological systems. The first search strategy is based on the flocking behavior of birds and fishes. This flocking behavior is the inspiration behind the Particle Swarm Optimization (PSO) algorithm that has been used in software for many types of optimization problems. In the PSO algorithm the potential solutions, called particles, “fly” through the problem space by following some simple rules. All of the particles have a fitness value based on the value or measurement at the particle’s position and have velocities which direct the flight of the particles. The velocity of each particle is updated based on the particle’s current velocity as well as the best fitness of any particle in the group. 1