Bio-Inspired Self-Organized Fission–Fusion Control Algorithm for UAV Swarm

Abstract

Swarm control has become a challenging topic for the current unmanned aerial vehicle (UAV) swarm due to its conflicting individual behaviors and high external interference. However, in contrast to static obstacles, limited attention has been paid to the fission–fusion behavior of the swarm against dynamic obstacles. In this paper, inspired by the interaction mechanism and fission–fusion motion of starlings, we propose a Bio-inspired Self-organized Fission–fusion Control (BiSoFC) algorithm for the UAV swarm, where the number of UAVs in the sub-swarm is controllable. It solves the problem of swarm control under dynamic obstacle interference with the tracking function. Firstly, we establish the kinematic equations of the individual UAV and swarm controllers and introduce a fission–fusion control framework to achieve the fission–fusion movement of the UAV swarm with a lower communication load. Afterward, a sub-swarm selection algorithm is built upon the topological interaction structure. When a swarm is faced with different tasks, the swarm that can control the number of agents in a sub-swarm can accomplish the corresponding task with a more reasonable number of agents. Finally, we design a sub-swarm trapping algorithm with a tracking function for the dynamic obstacles. The simulation results show that the UAV swarm can self-organize fission sub-swarms to cope with dynamic obstacles under different disturbance situations, and successfully achieve the goal of protecting the parent swarm from dynamic obstacles. The experimental results prove the feasibility and effectiveness of our proposed control algorithm.

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…Moreover, Table 4 lists the multimodal navigation. The applications of the multimodal navigation consist of entertainment [ 143 ], security [ 133 ], transport [ 2 , 106 , 107 , 110 , 112 , 147 ], assistance [ 2 , 16 , 108 ], exploration [ 3 , 134 , 135 , 138 , 141 ], social applications [ 12 , 140 ], tracking [ 105 , 125 , 131 , 145 , 146 ], caring and monitoring [ 113 ], disaster monitoring or search and rescue [ 136 , 149 ], floor cleaning [ 137 ], wheeled robot [ 109 ], person following [ 129 ], and false-ceiling inspection [ 157 ]. The combination of virtual sensors and neural networks is most commonly used in multimodal navigation, which represents 77.19% of the cited research.…”

“…It used a classification method by a multilayer perceptron neural network, but it needed to be evaluated with a larger dataset. Zhang et al [ 145 ] developed a B self-organized fission–fusion control strategy for swarm control, considering dynamic obstacle interference. Their algorithm achieved fission–fusion movement with a control framework and then built a subswarm selection algorithm with a tracking function.…”

Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms

“…[37] Energy-efficient UAV mission planning in the presence of wind was simulated using the Dryden turbulence model. [38] Bio-inspired swarm control under dynamic obstacle interference. Reference Description [28] Formation control and link selection for UAVs in complex environments using APF.…”

“…[37] Energy-efficient UAV mission planning in the presence of wind was simulated using the Dryden turbulence model. [38] Bio-inspired swarm control under dynamic obstacle interference. Table 2.…”

Bio-Inspired Fission–Fusion Control and Planning of Unmanned Aerial Vehicles Swarm Systems via Reinforcement Learning

“…There has been limited research on swarm planning in the presence of unknown dynamic obstacles, particularly of those with tracking capabilities. This is primarily because existing path planning strategies for static or dynamic obstacles often rely on global environment information [25][26][27][28]. For instance, Wang et al [26] perform path planning through a sampling-based algorithm, while Garrett et al [27] propose to address the continuous space subproblems and integrate the discrete and continuous aspects of the search process to achieve path planning objectives.…”