The dynamic task allocation problem for Unmanned Aerial Vehicle (UAV) swarms is characterized by dynamic uncertainty, high nonlinearity, and multimodality, which increasingly becomes a focal point and challenge within the realm of task allocation. This study investigates the mixed interaction modes of “individual-individual,” “individual-environment,” and “swarm-environment,” drawing inspiration from the labor division observed in wolf packs. We design a mechanism for alliance formation that is predicated on both individual democratic choice and centralized swarm decision-making, integrating the concept of alliance formation with a labor division model. Moreover, we propose a democratic-centralization model (DCM) that incorporates a bottom-up labor division approach aligned with the wolf pack alliance framework. This model is adeptly adapted to the dynamic task allocation for UAV swarms, employing a strategy of “individual cyclic competition plus overall coordinated decision-making.” We compare simulation experiments using the DCM with three contemporary, leading methods addressing the dynamic allocation problem for UAV swarms tasked with dynamic operations. The results demonstrate that our model can efficiently orchestrate the dynamic allocation of UAV swarms, exhibiting considerable dynamic adaptability, collaborative efficiency, and robustness.

Collaborative search by air-ground unmanned swarm, as a pivotal and efficient approach for intelligence gathering and disaster relief, highlights the critical role of search path planning in enhancing overall performance. Addressing the inefficiency resulting from insufficient collaboration between air and ground unmanned platforms in current research, this paper delves into the fundamental characteristics and challenges of collaborative search by air-ground unmanned swarm. This paper clarifies the objectives and constraints of path planning and introduces a method for collaborative search path planning based on the Learning Wolf Pack Algorithm (LWPA). This method initially constructs an optimization model that comprehensively considers area coverage, target detection probability, and search uncertainty. It integrates Distributed Model Predictive Control (DMPC) with the Distributed Constraint Optimization Problem (DCOP) framework, forming an architecture for real-time search path planning. To overcome the limitation of existing DCOP solution methods, which tend to get stuck in local optimal solutions, the LWPA employs a Q-learning mechanism for hierarchical learning and dynamically adjusts parameters to balance local refinement and global exploration. Experimental results demonstrate that this method offers significant advantages in improving search efficiency, coverage, and target detection rates, with an average area coverage of 99.28% and uncertainty as low as 0.86%. These results fully validate its effectiveness and superiority in search tasks in complex urban environments. Furthermore, tests on dynamic adaptability and scalability further verify the potential and value of this method in practical applications.

Face regions are visual focuses in conversational video communications, thus better reconstruction quality of the regions of interest (ROI) is highly desired or necessary in the bandwidth-constrained conversational video coding. In this paper, we introduce an efficient motion based face detection method to identify face blocks in the first step, which can reduce computational complexity substantially without any loss in face detection results. Then an active contour model is applied to find face contours for more refined and compact face regions. Based on the well-located and compact face regions, facial feature priority based bit allocation is proposed for face ROI based conversational video coding. Experimental results demonstrate that the proposed face region based coding can considerably improve the coding results in the face regions, compared with two other relevant video coding schemes, in terms of objective rate-distortion performance as well as subjective visual quality.

Coalition formation of unmanned aerial vehicle (UAV) swarms, an effective solution for UAV swarm task allocation, is an important technology for UAV swarms to perform real-time and efficient collaborative task allocation in a dynamic and unknown environment. This paper summarizes the task allocation methods of UAV swarm coalition comprehensively and systematically. First, starting with the related work of UAV swarm coalition task allocation, this paper introduces the basic concept, general model, and constraint index of UAV swarm coalition task allocation. Then, the specific content, research status, advantages, and disadvantages of the coalition formation methods are analyzed, respectively. Third, the commonly used solution algorithms and research status of coalition task allocation are introduced, and the advantages and disadvantages of the existing coalition formation solution algorithms are compared and analyzed. Finally, it provides significant guidance for future related research.

In response to the increasing safety concerns posed by low, slow, and small unmanned aerial vehicles (UAVs), the use of flexible nets for interception emerges as a promising solution due to its high tolerance, minimal requirements, and cost-effectiveness. To enhance the effectiveness of the flexible net capture system for these types of UAVs, an optimization of the system’s parameters is conducted. A dynamic model of the flexible net capture system is developed, and its deployment process is simulated and analyzed through a combination of ABAQUS 2022/Explicit and MATLAB R2020b software. The coverage rate and hang time are proposed as the key performance indicators for quantitatively assessing the interception capabilities of the rope net. A mathematical model is formulated to optimize the capture system parameters, considering both spatial and temporal tolerances. The Multi-objective Wolf Pack Algorithm, which incorporates an Elite Leadership Strategy and a crowding distance-based population update mechanism, is utilized to optimize the design variables. This approach leads to the derivation of the optimized design parameters for the flexible net. Ultimately, the optimal parameter configuration for the flexible net capture system is achieved through the application of the Multi-objective Wolf Pack Algorithm to the design variables. This optimization ensures the system’s peak performance in intercepting low, slow, and small UAVs.

To address the highly nonlinear and multimodal planning challenges encountered by the air-ground cross-domain unmanned swarm when executing time-sensitive missions in dynamic battlefield environments, this paper proposes a Flexible Task Planning (FTP) method inspired by the self-organized hunting behavior of wolf packs. By constructing models for heterogeneous unmanned platforms, time-constrained multi-task scenarios, and three-dimensional battlefield environments, a multi-objective effectiveness evaluation system, which integrates value gains, path costs, time costs, and damage costs, is established. Leveraging the wolf pack hunting mechanism, a rolling horizon strategy is adopted to achieve online real-time planning in dynamic settings: target priorities are dynamically ranked based on cost-effectiveness assessments, and a transition probability calculation method that integrates relative cost-effectiveness and responsiveness is designed to achieve flexible task allocation. Additionally, swarm collaboration effects are quantified with parallel path planning and a cooperative gain factor. Simulation results indicate that FTP performs exceptionally well in scenarios involving expanded swarm scales and increased task complexity, with a stable comprehensive score above 0.8 and a task completion rate over 94%. Moreover, its computational efficiency is significantly superior to that of traditional methods, which validates its remarkable advantages in terms of dynamic adaptability, collaborative efficiency, and scalability. This proposed approach offers an efficient solution for the cooperative operations of air-ground cross-domain unmanned swarm.