对抗环境下异构无人机的协同多智能体路径规划
Cooperative Multi-Agent Path Planning for Heterogeneous UAVs in Contested Environments
AI总结：
本研究针对对抗环境下无人机导航难题，提出含两类异构无人机的协同多智能体框架，分别采用对应路径规划方法并结合优化技术，提升了任务安全穿越成功率。
摘要：
本文针对对抗环境中的无人机导航难题，提出了一种协同多智能体框架，以提升无人机安全穿越的概率。该方法涉及两类无人机：负责探索并定位威胁的低优先级智能体，以及在尽量降低被敌方雷达探测风险的同时安全导航至目标目的地的高优先级智能体。低优先级智能体采用一种去中心化优化算法，在探索、雷达定位与为高优先级智能体识别安全路径三者间取得平衡。针对高优先级智能体，本文提出了两种路径规划方法：一种适用于确定性场景，采用加权Voronoi图；另一种适用于不确定性场景，结合广义Voronoi图（融入由雷达探测概率不确定性推导的非欧几里得准则）与概率约束。两种方法均运用优化技术优化轨迹，同时考虑运动学约束与雷达探测概率。数值仿真验证了所提框架的有效性。本研究融合异构多智能体协作、概率建模与优化技术，完善了无人机路径规划方法，可提升对抗环境下的任务成功率。
英文摘要：
This paper addresses the challenge of navigating unmanned aerial vehicles in contested environments by introducing a cooperative multi-agent framework that increases the likelihood of safe UAV traversal. The approach involves two types of UAVs: low-priority agents that explore and localize threats, and a high-priority agent that navigates safely to its target destination while minimizing the risk of detection by enemy radar systems. The low-priority agents employ a decentralized optimization algorithm to balance exploration, radar localization, and safe path identification for the high-priority agent. For the high-priority agent, two path-planning methods are proposed: one for deterministic scenarios using weighted Voronoi diagrams, and another for uncertain scenarios that leverages generalized Voronoi diagrams (incorporating a non-Euclidean criterion derived from uncertainty in the radar's probability of detection) alongside probabilistic constraints. Both methods employ optimization techniques to refine the trajectories while accounting for kinematic constraints and radar detection probabilities. Numerical simulations demonstrate the effectiveness of our framework. This research advances UAV path planning methodologies by combining heterogeneous multi-agent cooperation, probabilistic modeling, and optimization to enhance mission success in adversarial environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
