QRDPSO: A new optimization method for swarm robot searching and obstacle avoidance in dynamic environments

摘要

In this paper we show how the quantum-based particle swami optimization (QPSO) method is adopted to derive a new derivation for robotics application in search and rescue simulations. The new derivation, called the Quantum Robot Darwinian PSO (QRDPSO) is inspired from another PSO-based algorithm, the Robot Darwinian PSO (RDPSO). This paper includes comprehensive details on the QRDPSO formulation and parameters control which show how the swami overcomes communication constraints to avoid obstacles and achieve optimal solution. The results show the QRDPSO is an upgrade over RDPSO n terms of convergence speed, trajectory control, obstacle avoidance and connectivity performance of the swarm.