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.
As a NP-hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle (UAV) swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real-time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary wolf pack algorithm (BWPA) to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost-effectiveness ratio and task execution time window. In addition, one-dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm (WPA) is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems.
Autofluorescence is frequently observed in animal tissues, interfering with an experimental analysis and leading to inaccurate results. Sudan black B (SBB) is a staining dye widely used in histological studies to eliminate autofluorescence. In this study, our objective was to characterize brain tissue autofluorescence present in three models of acute brain injury, including collagenase-induced intracerebral hemorrhage (ICH), traumatic brain injury (TBI), and middle cerebral artery occlusion, and to establish a simple method to block autofluorescence effectively. Using fluorescence microscopy, we examined autofluorescence in brain sections affected by ICH and TBI. In addition, we optimized a protocol to block autofluorescence with SBB pretreatment and evaluated the reduction in fluorescence intensity. Compared to untreated, pretreatment with SBB reduced brain tissue autofluorescence in the ICH model by 73.68% (FITC), 76.05% (Tx Red), and 71.88% (DAPI), respectively. In the TBI model, the ratio of pretreatment to untreated decreased by 56.85% (FITC), 44.28% (Tx Red), and 46.36% (DAPI), respectively. Furthermore, we tested the applicability of the protocol using immunofluorescence staining or Cyanine-5.5 labeling in the three models. SBB treatment is highly effective and can be applied to immunofluorescence and fluorescence label imaging techniques. SBB pretreatment effectively reduced background fluorescence but did not significantly reduce the specific fluorescence signal and greatly improved the signal-to-noise ratio of fluorescence imaging. In conclusion, the optimized SBB pretreatment protocol blocks brain section autofluorescence of the three acute brain injury models.
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