Research on dynamic task allocation for multiple unmanned aerial vehicles. (April 2017)

• Record Type: Journal Article
• Authors: Miao, Yongfei; Zhong, Luo; Yin, Yufu; Zou, Chengming; Luo, Zhenjun
• Other Names: Wu Zhipeng guest-editor.
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 39:Number 4(2017)
• Page Start: 466
• Page End: 474
• Publication Date: 2017-04
• Subjects: Immune algorithm -- MAS -- neighbourhood -- task allocation -- UAV; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331217693077
• Languages: English
• ISSNs: 0142-3312

Abstract

To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.