Distributed Multi-robot Collaboration for Tasks Allocation based on Optimized Greedy Algorithm Approach

Abstract

During the last decades, the intervention using robots in sensitive areas reached appreciable mathematical confidence. Robots are equipped with adequate payload and embed processes using high-performance algorithms oriented topology, statistical observations, ontology, or bio-inspired. These algorithms improve considerably the processing capacity in time savings and computational efficiency.The modified GREEDY approach adopted in this contribution aims to optimize the gain in time and cost of processing for task allocation among a cluster of micro-robots with adequate means for the purpose of identifying sensitive areas.Evaluation of the efficiency of the task’s planning process to order each agent micro-robot, we optimally evaluate the cost function by grouping the dependencies; radio connectivity, energy at disposal, and the absolute and relative availability of the agent for itself and within the group. One of the first concerns is to validate the positive trend of the growing number of agents forming the cluster.For this objective, our approach introduces a cluster of three micro-robots. The proposed idea is qualified as an adaptive approach for a mission to identify victims at risk in a challenging environment. Each micro-robot in the cluster is configured to maintain interoperability and collaboration that gain support to evolve in the target scene in order to perform the assigned task. Collaboration algorithms are implemented as an adaptive strategy where it is necessary to optimize agents' mobility according to criteria depending on the characteristic of the place to be identified.