Research on Task Allocation of Ground-Air Collaborative Cluster Based on Two Improved Firefly Algorithms

• Main Authors: Gao, Ya, Ma, Rui, Cao, Heyang, Yu, Changli, Ma, Guangcheng, Xia, Hongwei, Ma, Changbo
• Format: Conference Proceeding
• Language: English
• Subjects: Clustering algorithms; Collaboration; firefly algorithm; ground-air collaborative; mutation; Resource management; Simulated annealing; Simulation; task allocation; Task analysis; Unmanned aerial vehicles
• ISSN: 2161-2927
• DOI: 10.23919/CCC52363.2021.9550682

The ground-air collaborative cluster system can fully improve the efficiency and ability of the robot system to perform investigation and combat tasks in unknown fields by combining the advantages of ground and aerial robots. Task allocation is a key technology and a complex decision and optimization problem for the ground-air collaborative cluster system. In this paper, based on the background that the ground-air collaborative cluster performs the ground targets attack task on the battlefield, a combinatorial optimization model of the task allocation problem is established. Then, to solve the problem that the standard firefly algorithm is easy to fall into local optimum, two improved firefly algorithms based on simulated annealing and adaptive mutation are used to solve the task allocation problem respectively. The final simulation results show that the two improved algorithms can produce uniform and reasonable task allocation schemes, with the better optimization ability and higher solution accuracy, which verifies the effectiveness of the two proposed algorithms.