Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in Conflict Detection and Resolution (CD&R) methods. With the new applications of drones, and the implications of a profoundly different urban airspace, new demands are placed on such algorithms, further spurring new research. This paper presents a review of current CR methods for both manned and unmanned aviation. It presents a taxonomy that categorises algorithms in terms of their approach to avoidance planning, surveillance, control, trajectory propagation, predictability assumption, resolution manoeuvre, multi-actor conflict resolution, considered obstacle types, optimization, and method category. More than a hundred CR methods were considered, showing how most work on a tactical, distributed framework. To enable a reliable comparison between methods, this paper argues that an open and ideally common simulation platform, common test scenarios, and common metrics are required. This paper presents an overview of four CR algorithms, each representing a commonly used CR algorithm category. Both manned and unmanned scenarios were tested, through fast-time simulations on an open-source airspace simulation platform.

/pdf/review-of-conflict-resolution-methods-for-manned-and-3nydeq15cj.pdf

Review of Conflict Resolution Methods for Manned and Unmanned Aviation

Conflict resolution becomes crucial when one is dealing with a large number of robots working in cluttered environments. The majority of the developed conflict resolution approaches in the literature deal with a motion-liveness problem which fails to gain collision-free movements for a large number of robots. This paper develops a systematic approach for coordinating the motions of multi-robot systems by adjusting their speeds to avoid collisions and guarantee motion-liveness of the robots. We mathematically formulate the multi-robot motion as a constrained optimization problem to minimize the time it takes for each robot to reach its target while avoiding collisions. Using two metaheuristic optimization methods, multiobjective genetic algorithm and particle swarm optimization, we can solve the conflict resolution problem up to 30 robots in a highly cluttered environment. Results show that we can find collision-free movements for a large number of robots in cluttered environments, while also guaranteeing multi-robot motion-liveness.

Conflict Resolution of Cluttered Multi-robot Systems Using Metaheuristic Optimization Algorithms

In order to resolve the conflict among multiple aircrafts, a method to get the optimal route quickly and accurately based on genetic algorithm is proposed. By applying genetic algorithm, the conflict is resolved between two aircrafts as well as among multi-planes. An optimization method to accelerate the convergence of the algorithm is proposed. Simulation results show that the algorithm can get the optimal conflict resolution route quickly. This method is of significance for actual path planning. Index Terms free flight, genetic algorithm, conflict resolution, algorithm optimization

/pdf/study-of-multi-aircraft-conflict-resolution-and-algorithm-4twgpkgmj2.pdf

Study of Multi-aircraft Conflict Resolution and Algorithm Optimization Based on Genetic Algorithm

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Study of Multi-aircraft Conflict Resolution and Algorithm Optimization Based on Genetic Algorithm