Abstract
The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multiple sensors with complementary functions to adapt to the complex mission constraints. Reasonable task assignment, task scheduling, and UAV trajectory planning are the prerequisites for efficient cooperation of multi-functional heterogeneous UAVs. In this paper, a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching is proposed to solve the multi-functional heterogeneous UAV cooperative mission planning problem with the criterion of simultaneously minimizing the makespan and the total mission time. First, the multi-swarm mechanism is introduced to enhance the global search capability of the fruit fly optimization algorithm. Second, in the smell-based search phase, the local search strategies and large-scale search strategies are designed to drive multiple fruit fly swarms, and the dual strategy switching method is presented. Third, in the vision-based search stage, the greedy selection strategy is adopted. Finally, numerical simulation experiments are designed. The simulation results show that the MFOA algorithm is more effective and stable for solving the multi-functional heterogeneous UAV cooperative mission planning problem compared with other algorithms.
Highlights
The application of unmanned aerial vehicles (UAV) in military and civil fields is growing rapidly, such as for pesticide spraying, forest fire prevention, material delivery, reconnaissance, etc
To solve the multi-functional heterogeneous UAV cooperative mission planning problem (MFHCMPP) problem, we propose the multi-swarm fruit fly optimization algorithm (MFOA)
The decoding method of UAV task commands was given, and the greedy selection strategy vision-search stage, the decoding method of UAV task commands was given, and the greedy selection strategy was applied to update the center of the fruit fly swarm
Summary
The application of unmanned aerial vehicles (UAV) in military and civil fields is growing rapidly, such as for pesticide spraying, forest fire prevention, material delivery, reconnaissance, etc. The unmanned aerial vehicle control method is very flexible [1,2], including the following: (i) human operator remote control; (ii) pre-programmed or uploaded mission plan via communication device; (iii) UAV autonomous operation. For complex tasks such as large-scale information gathering, regional reconnaissance, and search and rescue, the cooperation of multiple UAVs in the form of a team is usually required. The multi-UAV cooperative mission planning problem (MCMPP) plays an essential role in the autonomous operation of multiple UAVs, which is a non-deterministic polynomial time (NP)-hard combinatorial optimization problem. The MCMPP involves rationally assigning the UAVs and scheduling the tasks on the premise of meeting the Sensors 2020, 20, 5026; doi:10.3390/s20185026 www.mdpi.com/journal/sensors
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