Autonomous maneuvering decision-making in unmanned serial vehicles (UAVs) is crucial for executing complex missions involving both individual and swarm UAV operations. Leveraging the successful deployment of recommendation systems in commerce and online applications, this paper pioneers a framework tailored for UAV maneuvering decisions. This novel approach harnesses recommendation systems to enhance decision-making in UAV maneuvers. Our framework incorporates a comprehensive six-degree-of-freedom dynamics model that integrates gravitational effects and defines mission success criteria. We developed an integrated learning recommendation system capable of simulating varied mission scenarios, facilitating the acquisition of optimal strategies from a blend of expert human input and algorithmic outputs. The system supports extensive simulation capabilities, including various control modes (manual, autonomous, and hybrid) and both continuous and discrete maneuver actions. Through rigorous computer-based testing, we validated the effectiveness of established recommendation algorithms within our framework. Notably, the prioritized experience replay deep deterministic policy gradient (PER-DDPG) algorithm, employing dense rewards and continuous actions, demonstrated superior performance, achieving a 69% success rate in confrontational scenarios against a versatile expert algorithm after 1000 training iterations, marking an 80% reduction in training time compared to conventional reinforcement learning methods. This framework not only streamlines the comparison of different maneuvering algorithms but also promotes the integration of multi-source expert knowledge and sophisticated algorithms, paving the way for advanced UAV applications in complex operational environments.

Addressing the real-time control challenges within large-scale, complex resource-constrained project scheduling, this paper investigates control strategies to ensure the on-time initiation of critical task nodes during the execution of aviation cluster mission plans in the presence of disturbances. Conventional resource-constrained project scheduling problem (RCPSP) models typically treat task start times as the primary decision variables, overlooking the intrinsic link between task duration and resource allocation. Moreover, their reliance on intelligent optimization algorithms struggles to simultaneously balance solution accuracy and computational efficiency, thus failing to meet the demands of precise, real-time control. This paper proposes a real-time project schedule control system with the primary objective of preventing delays in critical tasks. The system aims to maximize the remaining anti-disturbance capacity under resource constraints, and establishes five control constraints tailored to the practical problem’s characteristics. The limitations of traditional approaches mainly lie in the fact that they take the start time of each task as the decision variable. When the scale of task quantity in the project is large, the decision dimension increases exponentially; meanwhile, the start times of various tasks are interdependent, leading to extremely complex constraint relationships. To overcome the limitations of traditional methods, this paper introduces a precise control method based on the Critical Path Transform Tree (CPTT). This method takes task duration as the decision variable, calculates the start time of each task using a recursive formula, and integrates expert heuristic knowledge to transform the dynamic network schedule from a “black box” to a “gray box” model. It effectively addresses the technical challenge of reverse mapping in the recursive formula, ultimately realizing precise and real-time control of the project schedule. The simulation results show that while maintaining high solution accuracy, the computational efficiency of the proposed control method is significantly improved to 1.6 s—compared with an average of 6.9 s for the adaptive differential evolution algorithm—thus verifying its effectiveness and practicality in real-time control applications.

This study addresses continuous UAV patrol scheduling for forest fire prevention in the Greater Khingan Mountains under multiple airports, UAV types, and monitoring zones. A multi-objective scheduling model is established considering UAV availability, weather conditions, runway capacity, fuel safety, ground turnaround, coordinated entry, and overlap coverage. The objectives are to minimize UAV workload-rate variance and global total fuel consumption. A two-layer solution method combining NSGA-II with temporal simulation is developed, in which the upper layer optimizes airport assignments and the lower layer performs UAV matching, takeoff- and landing-completion time calculation, UAV reuse, and constraint checking. Comparative experiments are conducted across 27 scenarios combining nine weather conditions and three airport-resource configurations, with fixed three-shift rotation and simulated annealing (SA) used as comparison methods. Each of the 1647 scheduling runs completes 52 monitoring rounds and 260 UAV sorties without reported constraint violations. NSGA-II achieves lower mean fuel consumption than SA in all 27 scenarios, while obtaining lower mean workload-rate variance in 17 scenarios. The proposed method also provides multiple Pareto solutions for different preferences between workload balance and fuel consumption.

Situation assessment (SA) supports maneuver decisions in dynamic three-dimensional robotic interactions, yet explicit methods depend on subjective factors and weights, whereas implicit learning-based methods are difficult to interpret. We propose a traceable benchmarking framework that standardizes motion dynamics, scenarios, capture-tagging rules, round-score metrics, and the decision carrier. Unified-Dimensional Situation Assessment (UDSA) converts heterogeneous advantages to a common altitude-equivalent scale, and Situation Assessment-Based Balanced Expert Decision-Making (SABED) enables controlled closed-loop comparison. Across five independent seeds, UDSA achieved mean round scores of 0.790 ± 0.010 against expert weighting and 0.710 ± 0.016 against Criteria Importance Through Intercriteria Correlation (CRITIC). Scenario-stratified, stress, and symmetric self-play summaries further supported robustness and label invariance. The released package enables reproduction of UDSA scoring, equivalence conversion, trajectory assessment, and the reported statistical summaries.