Abstract – This work introduces the implementation of particle system to be
simulated to work as a group of unmanned mobile robots (swarm robots). These robots
are able to locate a specified target in the predefined environment with high efficiency
when driven by an optimized Particle Swarm Optimization (PSO) algorithm. The
application of the particle system to the mobile robots to search for a target in the
environment is called Collective Robotics Search (CRS) problem. The main benefit of
this application is to evolve better solutions than using single robot through the
collective interaction of all robots between them to achieve the searching task
successfully. Particle system has been chosen in this work to employ the mobile robots
in the CRS problem due to its simplicity and easy to implement. To measure the
performance of this simulation, a simple obstacle free environment will be used to
implement behaviors of the group of mobile robots when those robots are used to search
for a single target. The results of this work show that applying PSO to a CRS problem
in off-line and on-line approaches are efficient in terms of minimum error and also
minimum number of iterations during the evolutionary process.