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This paper introduces decentralized control concepts for drones usingdifferential game theory. The approach optimizes the behavior of an ego drone,assuming the anticipated behavior of the opponent drones using a recedinghorizon approach. For each control instant, the scheme computes the Nashequilibrium control signal which is applied for the control period. Thisresults in a multi-drone conflict resolution scheme that is applied to alldrones considered. The paper discusses the approach and presents the numericalalgorithm, showing several examples that illustrate the performance of themodel. We examine at the behavior of the ego drone, and the resultingcollective drone flow operations. The latter shows that while the approach aimsto optimize the operation cost of the ego drone, the experiments provideevidence that resulting flow operations are very efficient due to theself-organization of various flow patterns. The presented work contributes tothe state of the art in providing a generic approach to multi-drone conflictresolution with good macroscopic flow performance characteristics. The approachenables relatively straightforward inclusion of error due to sensing andcommunication. The approach also allows for including different risk levels(e.g., for malfunctioning of sensor and communication technology), priorityrules, regulations, and higher-level control signals (e.g., routing, dynamicspeed limits).

Game-theoretical approach to decentralized multi-drone conflict resolution and emergent traffic flow operations