Tengyu Jing

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.

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.