Abstract
Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced. Second, the research status and the advantages and disadvantages of the two allocation models are analyzed. Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.
Highlights
A single unmanned aerial vehicle (UAV) cannot complete tasks with increasing complexity; the cooperation of UAV swarms is important[1]
On the basis of the traveling salesman model, the sudden time sensitive target situation and task execution time were considered to realize time sensitive task allocation. 2.3.2 Local dynamic allocation model To reduce the complexity of a problem to be solved, the method of local dynamic allocation focuses on the characteristics of the task at hand and the UAV, groups the task and UAV and solves the task allocation problem within the task regroup
The results addressed the defects of the original algorithm, including its failure to realize task migration in the absence of an auctioneer and auction multiple tasks simultaneously. 3.1.2 Contract net protocol The contract net protocol (CNP) is a negotiation-oriented task allocation method[38], which adopts the mechanism of “tendering–bidding–winning–signing”; it is widely used in studies aiming to solve military problems[39−43]
Summary
A single unmanned aerial vehicle (UAV) cannot complete tasks with increasing complexity; the cooperation of UAV swarms is important[1]. Dynamic task allocation is more difficult to implement than static task allocation This difference can be explained as follows: First, the environment changes rapidly, and the tasks are complex and diverse. These factors greatly increase the difficulty of the model establishment. Dynamic task allocation involves a large number of constraint indicators, such as task balance, damage cost, flight distance, consumption cost, and target income. It is an NP-hard problem and requires timeliness for the algorithm. The key to solving the problem is to establish and solve the dynamic task allocation model
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