Addressing prevalent challenges in current cooperative task assignment methods for cross-domain unmanned swarm, such as the disconnection between decision-making and execution processes, and the inadequate incorporation of platform kinematic constraints, this study introduces an integrated decision-control cooperative task assignment approach based on a bi-level optimization framework. The proposed framework formulates a bi-level programming model that tightly couples upper-level task assignment with lower-level optimal control. The upper-level model aims to minimize the maximum task completion time by optimizing the assignment and visitation sequences of diverse target types across heterogeneous unmanned platforms. The lower-level model, given the task sequences from the upper level, addresses a minimum-time optimal control problem based on a comprehensive nonlinear kinematic model. This approach enables precise computation of task execution times, which are subsequently fed back to the decision-making layer, thereby establishing a closed-loop optimization mechanism. To solve this complex model efficiently, the lower-level employs differential flatness transformation to eliminate trigonometric functions in the kinematic equations and discretizes the continuous-time optimal control problem into a nonlinear programming problem via the Radau pseudospectral method. For the upper-level combinatorial optimization, an improved genetic algorithm is developed, integrating hybrid encoding, dual-archive elitism preservation, adaptive crossover and mutation strategies, and periodic local search. Simulation results demonstrate that, compared with traditional Euclidean-distance-based assignment methods, the proposed approach generates kinematically feasible and smooth trajectories while thoroughly accounting for the kinematic constraints of heterogeneous platforms, thereby demonstrating its effectiveness and superiority in improving the comprehensive mission performance of cross-domain unmanned swarms.

The emergence and stable evolution of cooperation among self-interested individuals is a central issue spanning evolutionary biology, social dynamics, and artificial intelligence. Conventional imitation-based evolutionary game models, lacking mechanisms for active exploration and experiential accumulation, often trap populations in suboptimal steady states and fail to explain the persistence of complex cooperative patterns. In this study, we construct a multi-agent reinforcement learning framework for the spatial snowdrift game and propose a spatiotemporal cell theory that systematically elucidates the mechanisms underlying cooperation driven by autonomous learning. Our results show that agents accumulating interaction experience via Q-learning achieve cooperation levels significantly surpassing classical replicator dynamics across a broad parameter range, with the system self-organizing into robust collective decision-making structures. From an experiential learning perspective, we reveal an endogenous mechanism of cooperative emergence, demonstrating that efficient cooperation can arise solely from individual exploration and local feedback, without external punishment, reputation mechanisms, or centralized control. The spatiotemporal cell theory provides a unified analytical framework that quantifies the coupling between microscopic learning trajectories and macroscopic pattern evolution. Based on this theory, we derive a contour plot of the fraction of cooperators in the α–γ parameter plane that delineates cooperative stability, breakdown, and frozen defect-line phases, and uncover two distinct evolution pathways: cooperative amplification induced by synchronous exploration and noise accumulation driven by asynchronous exploration. This work deepens the understanding of cooperative evolution and provides theoretical support for designing decentralized adaptive multi-agent systems.

To address the challenge of stable tracking of moving maritime targets by unmanned aerial vehicle(UAV) swarm in environments with threat zones and platform failure risks, this paper proposes a cooperative tracking and guidance strategy integrating Distributed Model Predictive Control (DMPC) with Sequential Quadratic Programming (SQP). A cooperative tracking model is developed incorporating UAV kinematics, environmental threats, stereo-vision positioning, and field-of-view constraints. Two original strategies are introduced within the DMPC framework: an altitude-cooperative target recapture strategy reduces target total loss duration by approximately 7 s compared to fixed-altitude baselines, while a distributed formation reconfiguration strategy restores stable tracking within 10 s after member failure and ensures safe inter-UAV separation. A multi-constraint trajectory tracking controller based on DMPC-SQP achieves real-time co-optimization of threat avoidance, formation maintenance, and tracking accuracy. Simulation results in dense threat environments demonstrate a 93.4% Quadratic Programming feasibility rate, with mean tracking error reduced by 25.4% over fixed-altitude DMPC and 48.7% over methods based on the Linear Quadratic Regulator (LQR), while maintaining robust performance under 300 ms communication delay, sensor noise, and moderate wind disturbance.

Target tracking of unmanned aerial vehicle (UAV) swarms remains a significant challenge due to highly maneuverable target swarms and complex environments. To address these challenges, a hierarchical target tracking architecture is proposed, comprising a leader layer and a follower layer. This design reduces task complexity while improving formation adaptability and system scalability. In the leader layer, a distributed time-varying optimization model and a distributed protocol are developed to enable the UAV swarm to track highly maneuverable target swarms in real time. In the follower layer, a control protocol based on an affine transformation is employed to enable adaptive formation control under complex environmental constraints (e.g., threat avoidance). Moreover, the convergence performance of the proposed method is rigorously demonstrated through theoretical analysis. Finally, simulation results validate the convergence, feasibility, and scalability of the proposed method. Comparative simulations further demonstrate the superiority of the proposed method.