Parallel asynchronous control strategy for target search with swarm robots

Upon mapping swarm robots search to particle swarm optimization (PSO), we extend PSO algorithm to model and control swarm robots for target search at an abstract level. At first, we analytically compare the characteristics of different versions of PSO to facilitate design of robots control algorithm by tailoring and transferring parallel asynchronous property of PSO into case of swarm search. Next, a concept of time-varying character swarm is proposed to afford decision-making on the best-found position according to swarm intelligence principles. Based on control principle of expected evolution position of individual robot, an asynchronous communication policy for swarm search is presented. In this case, each robot detects and fuses real-time heterogeneous target signals propagating over search environment in parallel fashion as fitness evaluate in PSO every time step. Accordingly, a series of expected evolution positions of each individual robot can be decided. Thus, each robot moves respectively toward its current expected position, controlled under related control laws. The need for number of time steps depends on kinematics and dynamics constraints of real robot. Meanwhile each robot evaluates those positions in which it is situated every time step, updating its cognition as soon as a better finding of itself has been found, and updating the shared information and broadcasting this message all over the character swarm if a better finding of swarm has been found. At the same time, each robot listens to the change of shared information. Either knowing change caused by certain neighbour or arriving at the current expected position, robot starts to compute new expected position and turn out next round of control at once. Simulation results indicate that the presented evolution position-based asynchronous communication strategy has advantage over the popular control strategy in search efficiency.