Multiobjective coordinated search algorithm for swarm of UAVs based on 3D-simplified virtual forced model

This paper aims to tackle the problem of multiobjective search for swarms of UAVs in unknown complex environments and proposes a multiobjective coordinated search algorithm based on a 3D-simplified virtual forced model (MOCS-3D-SVFM). First, it decomposes the search behaviour into the roaming search state and coordinated search state based on the detection of target signals by a swarm of UAVs. Second, a nearest neighbour exclusion diffusion (NNED) algorithm is introduced for the UAV of the wander search state, and a 3D adaptive inertia weight extended particle swarm algorithm (IAEPSO) is proposed by combining the motion characteristics of UAV with a 3D particle swarm algorithm aiming at the UAV with coordinated search state. Finally, the 3D-simplified virtual force model proposed based on the concept of the 2D-simplified virtual force model by the rotation matrix is introduced to solve the model parameters and the control strategy under the UAV of wander search state and coordinated search state is established, which effectively solves the real-time obstacle avoidance problem. Moreover, this paper sets the comparison mode of the three search methods; compared to Mode1, the search time T and energy consumption S can be significantly reduced, and the numerical simulations verify its effectiveness.