Target search using swarm robots with kinematic constraints

Taking target search with swarm robots for instance, we explore an approach to control swarm whose members are autonomous wheeled mobile robots with non-holonomic constraints. Comparing the differences and similarities between robot and particle in properties and behaviors, the authors map swarm search to particle swarm optimization (PSO). Given definitions of neighborhood structure and time-varying character swarm of robot, we extend PSO to model swarm robotic system at an abstract level. Multi-source heterogeneous signals of target are detected and fused independently by each robot in parallel, being used to decide the best-found positions both of robot itself and of character swarm by comparison. Then the expected positional series can be gained in iteration control by synthesizing its inertia and experience as well as experience of its character swarm. Finally, control vector consisting of linear and angular velocity is translated as available control inputs to individual controller at each time step, depending on robot kinematics. By this way, swarm robots can work together cooperatively. Simulation results indicate the validity of our control strategy and designed algorithm.