A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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Rising manned air traffic and deployment of unmanned aerial vehicles in complex operations requires integration of innovative and autonomous conflict detection and resolution methods. In this paper, the task of conflict detection and resolution is defined as an optimization problem searching for a heading control for cooperating airplanes using communication. For the optimization task, an objective function integrates both collision penalties and efficiency criteria considering airplanes' objectives (waypoints). The probability collectives optimizer is used as a solver for the specified optimization task. This paper provides two different implementation approaches to the presented optimization-based collision avoidance: 1) a parallel computation using multiagent deployment among participating airplanes and 2) semicentralized computation using the process-integrated-mechanism architecture. Both implementations of the proposed algorithm were implemented and evaluated in a multiagent airspace test bed AGENTFLY. The quality of the solution is compared with a negotiation-based cooperative collision avoidance method - an iterative peer-to-peer algorithm.