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

Abstract

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.

Keywords:

• Unknown complex environment
• multiobjective search
• swarm of UAVs
• nearest neighbour exclusion diffusion algorithm (NNED)
• 3D simplified virtual force mode (3D-SVFM)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Defense Basic Research Program of China [grant number JCKY2019403D006], the Outstanding Youth Project of the Education Department of Hunan Province of China [grant number 19B200], and the Doctoral Scientific Research Initial Funds of the Human University of Science and Technology [grant number E56126].

Notes on contributors

Xinjie He

Xinjie He, 1996, Yongzhou City, Hunan Province, Master's degree, research direction: swarm of UAVs system, swarm intelligence optimization algorithm.

Shaowu Zhou

Shaowu Zhou, 1964, Xiangtan City, Hunan Province, Doctor, second-level professor, doctoral supervisor, research direction: Robust control of complex systems, robotics.

Hongqiang Zhang

Hongqiang Zhang, 1979, Yanjin City, Henan Province, Doctor, lecturer, Master supervisor, Research direction: swarm robotics, swarm intelligence optimization algorithm.

Lianghong Wu

Lianghong Wu, 1977, Changsha City, Hunan Province, Doctor, professor, doctoral supervisor, Research direction: swarm intelligence optimization algorithm, Intelligent scheduling.

You Zhou

You Zhou, born in 1988, from Xiangtan, Hunan Province, PhD student, research direction: machine vision; swarm robot system.

Yujuan He

Yujuan He, born in 1981, from Yongzhou, Hunan Province, Bachelor, research direction: Logistics management and robot scheduling.

Mao Wang

Mao Wang, Born in 1996, from Xiangtan, Hunan Province, Master student, research direction: swarm robot system, swarm intelligence algorithm.