Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

Abstract

In this paper, we study the problem of conflict detection and resolution for unmanned aerial vehicle (UAV) swarms. Specially, we propose a distributed conflict detection and resolution method for multi-UAVs 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, 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 the partial knowledge of the airspace condition. Damping of the coordination is proposed to address data dropouts and transmission delays. Two typical scenarios are conducted to test the methodology proposed in this paper. The simulation result demonstrates the effectiveness and rationality of the proposed methodology.

Cooperative Target Search of UAV Swarm with Communication Distance Constraint

“…In [18][19][20], the speed obstacle method broadcasts automatic correlation monitoring to give each UAV the position and speed of the others; thus it solved the potential problem by detecting flight conflict and determining a relief flight path; however, the relief path can easily deviate the UAV from the search target point, thus compromising mission efficiency. In [21], the distributed model predictive control method is adopted, in which the collision avoidance management unit and the interactive graph updating mechanism address conflict resolution in multi-UAV route planning, but it requires a large amount of computation. To study cooperative strategy in the search for moving targets, this paper introduced the artificial potential field term to meet the basic requirements of collision prevention.…”

“…Step 5: update the target probability map. In the cooperative search process of a UAV swarm, according to UAV decision information and the target probability map updating method, the target probability distribution map is updated by formula (16) to formula (21), and the environment uncertain map is updated by formula (23).…”

“…It can be seen from Figure 7(d) that when some members are damaged, which leads to a decline in swarm size, a UAV can still carry out search tasks through autonomous decision-making, which has good robustness. (9) end for (10) for k � 0: k max (11) Update the target probability distribution map according to equations ( 16)-( 21); (12) for U i � 1: N u (13) According to formula (26), judge whether to make an independent decision; (14) According to formula (11), a differential evolution algorithm is adopted to make a real-time decision; (15) for U j � 1: N u (16) Determine the interactive member set according to formula (27); (17) Complete information interaction and fusion according to formula (25); (18) end for (19) According to formula ( 14), the differential evolution algorithm is adopted to make real-time decisions; (20) Update the UAV position according to formula ( 5); (21) Update your own environmental cognition map according to formula ( 23); (22) end for (23) end for ALGORITHM 1: Algorithm pseudocode.…”

“…(1) It designed a cooperative search method suitable for dynamic communication distance (2) It analyzed the prior information of four typical moving targets to generate a mathematical model that defines them in a cooperative search (3) It provided an interface to apply to current UAV conflict resolution results [18][19][20][21] in swarm cooperative search missions…”

Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments

“…This process is similar to the one that manned aerial vehicles have to perform when navigating in automatic mode, and, therefore, it may seem natural to use the same techniques (already studied and developed) in UAVs. In the case of TCAS, it is designed mainly for paired-manned-aircraft encounters [ 23 , 24 ]. The authors of [ 25 , 26 ] emphasized that using the same techniques in UAVs as in manned vehicles can be unfeasible because the set of UAVs is much more heterogeneous (in relation to vehicle characteristics, control mode, UAV type, etc.).…”