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

TL;DR

This paper proposes a decentralized control method for multi-drone conflict resolution using differential game theory, optimizing individual drone behavior to achieve efficient collective traffic flow through self-organization.

Contribution

It introduces a generic, game-theoretic framework for multi-drone conflict resolution that accounts for uncertainties, priorities, and higher-level controls, enhancing current decentralized drone management strategies.

Findings

The approach achieves efficient drone traffic flow with self-organized patterns.

Numerical examples demonstrate effective conflict resolution and flow optimization.

The method is adaptable to sensing errors, communication issues, and regulatory constraints.

Abstract

This paper introduces decentralized control concepts for drones using differential game theory. The approach optimizes the behavior of an ego drone, assuming the anticipated behavior of the opponent drones using a receding horizon approach. For each control instant, the scheme computes the Nash equilibrium control signal which is applied for the control period. This results in a multi-drone conflict resolution scheme that is applied to all drones considered. The paper discusses the approach and presents the numerical algorithm, showing several examples that illustrate the performance of the model. We examine at the behavior of the ego drone, and the resulting collective drone flow operations. The latter shows that while the approach aims to optimize the operation cost of the ego drone, the experiments provide evidence that resulting flow operations are very efficient due to the self-organization of various flow patterns. The presented work contributes to the state of the art in providing a generic approach to multi-drone conflict resolution with good macroscopic flow performance characteristics. The approach enables relatively straightforward inclusion of error due to sensing and communication. The approach also allows for including different risk levels (e.g., for malfunctioning of sensor and communication technology), priority rules, regulations, and higher-level control signals (e.g., routing, dynamic speed limits).