Simulating micro-robots to find a point of interest under noise and with limited communication using Particle Swarm Optimization

This paper presents the simulation results of a swarm of micro-robots collaborating to find a point of interest in 2D space. Guided by a fitness function, the Particle Swarm Optimization (PSO) algorithm is highly efficient to explore the solution space and find such an optimum. However, in real-world scenarios in which the particles are micro-robots, there are practical constraints. The two most significant constraints are: (1) given communication and measurement noise, the fitness function evaluation will be noisy, (2) given the limited communication range of micro-robots, broadcasting the global best solution is too expensive. A neighborhood PSO (NPSO) algorithm is proposed to replace the global best by the neighborhood best. Different applications call for different fitness functions, and three benchmark functions, representing three typical scenarios, are examined: (1) a unimodal and symmetric scenario with only one global optimum, (2) a multi-modal scenario with one global optimum but many local optima, and (3) a uni-model but asymmetric scenario. For each fitness function, simulations on the effects of the two aforementioned constraints, individually or combined, are carried out. The results demonstrate that PSO is tolerant to noise up to certain level and NPSO is a practical adaptation to implement swarm intelligence in swarm robotics.