Research On Method Of Control And Collision Avoidance For UAV Formation Based On Strategy Coordination

With the development trend of clustering and autonomous UAVs,collision has increasingly become an important factor restricting the development of the field,and the conflict-detection and resolution(CDR)algorithm has also become an important research problem in the field of UAV.This paper mainly studies the conflict-detection and resolution problem of the fixed wing UAV,including the conflict between multiple UAVs,the control of UAV swarms and the conflict between multiple swarms.Especially,for the conflict between multiple UAVs,this paper proposes a distributed conflict-detection and Resolution algorithms based on key-node selection and strategy coordination;for the control of UAV swarms,this paper propose a consensus-based cooperative formation control with collision;for UAV swarms,this paper proposes a distributed conflictdetection and resolution algorithm based on consensus algorithm and strategy coordinationEach aircraft is regarded as a critical node in a complex network.A novel temporal and spatial integrated conflict-detection methodology is proposed for aircraft formations and clusters.Each node selects three candidate strategies from a pre-set pool of strategies and generates corresponding planned tracks using uncertainty modeling based on the sixdegrees-of-freedom motion.The primary combination of strategies required for coordination the aircraft in conflict is based on the maximum-robustness principle.To address partial knowledge of the environment,a special token-allocation strategy for coordination with incomplete information is proposed in this paper.To address potential data dropouts,this study damps the solution to achieve coordination.Two extreme scenarios are constructed to examine the proposed methodology.This paper constructs a two-level control structure for UAV swarms,which can effectively ensure the convergence of UAV trajectory within the swarms and ensure the security and stability of the cluster.To cooperatively fly in formation,a consensus-based algorithm and a leader-follower structure are applied to the UAVs.Collisions among the UAVs can occur while they are flying with the cooperative control algorithm.The collision-avoidance strategy is based on an artificial potential approach.The convergence is guaranteed even when the cooperative formation control algorithm and the collisionavoidance control algorithm are simultaneously applied to the UAVs.This paper proposes a distributed conflict detection and resolution method for multiUAVs in formation based on consensus algorithm and strategy coordination.When encountering threat swarms,the UAVs in one swarm act as one unit and are together treated as one control object.Each swarm in conflict selects three candidate collision avoidance maneuvers from the preset strategy pool,generates the corresponding planned trajectories with an uncertainty trajectory modeling,and then broadcasts and shares them.All of the swarms in conflict coordinate and determine an optimal combination of strategies.When a collision is imminent,the primary strategy is activated.Each swarm adopts a “leader-follower” strategy,that where the leader UAV is regarded as the controller and flies independently,and the others follow the leader UAV.During motion,a decentralized consensus algorithm is adopted for agents to converge to their positions for the desired formation and to maintain a stable geometric configuration.A temporal and spatially integrated conflict-detection model is improved especially for UAV swarms.A token-allocation strategy is especially improved for distributed coordination to resolve partial knowledge of the airspace condition.Damping of the coordination is proposed to address data dropouts and transmission delaysIn addition,aiming at the problems appearing in the actual situation,this paper puts forward solutions.A temporal and spatially integrated conflict-detection model is improved especially for UAV swarms.A token-allocation strategy is especially improved for distributed coordination to resolve partial knowledge of the airspace condition.Damping of the coordination is proposed to address data dropouts and transmission delays.The cooperation strategy is improved,a distributed control framework is proposed for cooperation between multiple UAVs,a distributed / centralized hybrid control framework is proposed for cooperation between multiple swarms.