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Rule-Based Conflict Management for Unmanned Traffic Management Scenarios

机译:基于规则的无人交通管理场景冲突管理

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The growing use of Unmanned Aerial Vehicles (UAVs) operations will require effective conflict management to keep the shared airspace safe and avoid conflicts among airspace users. Conflicts pose high risk and hazard to human lives and assets as they ma may result in financial and human loss. The proposed rule-based conflict management model consists of three main stages. The first stage includes strategic deconfliction during the flight plan generation. The second stage, pre-tactical deconfliction, applies a ground delay to the agent to resolve the conflict. The third stage corresponds to the tactical deconfliction, where the drone hovers or loiter in the last waypoint before the conflict area until the conflict time window passes. The proposed method differs from most existing conflict management approaches in that it applies deconfliction methods sequentially using a rule-based strategy. Furthermore, a high number of published studies do not consider realistic airspace constraints and potential airspace modernization concepts such as dynamic flight restrictions Assessment and validation are performed in three simulation scenarios that consider different patterns of the airspace availability in the areas where flights may be restricted, such as airfields, recreational areas, and prisons. The Particle Swarm Optimization (PSO) algorithm was used for drone path planning. For the simulated scenarios all of the conflicts were resolved after implementation of the proposed method. The implemented method is simple, flexible and suitable for the management of more complex and dense airspaces.
机译:越来越多地使用无人飞行器(UAV)作战将需要有效的冲突管理,以保持共享空域的安全并避免空域用户之间的冲突。冲突对人的生命和财产构成高风险和危害,因为它们可能会导致财务和人员损失。所提出的基于规则的冲突管理模型包括三个主要阶段。第一阶段包括制定飞行计划期间的战略冲突。第二阶段是战术前冲突,对地面人员施加地面延迟以解决冲突。第三阶段对应于战术上的冲突,其中无人机在冲突区域之前的最后一个航路点徘徊或游荡,直到冲突时间窗口过去。所提出的方法与大多数现有冲突管理方法的不同之处在于,它使用基于规则的策略顺序地应用去冲突方法。此外,大量已发表的研究并未考虑实际的空域限制和潜在的空域现代化概念(例如动态飞行限制)。评估和验证是在三种模拟方案中进行的,这些方案考虑了可能限制飞行区域内不同的空域可用性模式,例如飞机场,娱乐区和监狱。粒子群优化(PSO)算法用于无人机路径规划。对于模拟方案,在实施所提出的方法之后,所有冲突都得到解决。所实现的方法简单,灵活,适合于管理更复杂,更密集的空域。

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