Reducing Interrupts Among Robots in Quantum-Behaved Swarm Exploration With MR-Leach

Abstract

A quantum-behaved robot exploration algorithm such as the Quantum Robotic Darwinian Particle Swarm Optimization (QRDPSO) gives stable swarm movement in unstructured conditions but suffers from communication interruptions. This paper examines the Multi-hop Routing with Low Energy Adaptive Clustering Hierarchy (MR-LEACH) to improve the inter-connectivity in the QRDPSO. The MR-LEACH identifies partitions in the network into multi-hop network paths. The multi-hop network paths allow all robots to exchange information without unnecessarily restricting the swarm’s range explicitly. As a result, the QRDPSO with MR-LEACH shows seamless inter-connectivity among the robots, lowering power consumption and increasing robots’ lifetime. Interestingly, this paper also shows that the QRDPSO can reach a faster optimal solution when adopting other communication protocols such as the Ad-hoc On-Demand Distance Vector (AODV) communication schema. However, swarm endurance and reduced robot loss are considered vital resources over convergence speed for a swarm robot exploration in unstructured scenarios, such as search and rescue missions.