Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

. 2022 Nov 26;7(4):214. doi: 10.3390/biomimetics7040214

Algorithm 1. Starling-behavior-inspired flocking control algorithm for UAVs

/*Initialization*/
Set initial parameters of the proposed algorithm and the model of fixed-wing UAV
Generate the position

x_{i}, y_{i}, h_{i}, heading angle ψ_{i} and velocity V_{i}, {\dot{h}}_{i}

of UAV i randomly
/*Begin*/
for i = 1 to n
for j = 1 to n
Select neighbors according to Equation (1)
if UAV j is the neighbor of UAV i
UAV i interact with UAV j according to Equation (21)

Calculate C_{i j}

according to Equation (9)
end if

Calculate φ_{i}

according to Equation (12)

Find the local leader l_{i}

according to Equations (10) and (11)
end for

Calculate R_{i}^{o b s}

to Equation (16)

if R_{d} \leq R_{i}^{o b s}

Execute collision prediction according to Equation (17)

if φ - Δ φ \leq θ_{i} \leq φ + Δ φ

Set parameter γ = 1

Calculate the control signal of evasion pattern of UAV i according to Equation (22)
end if
if there exist a local leader l_i
Set parameter β = 0
UAV i follows the local leader according to Equation (21)
end if
end if
Follow the virtual leader according to Equation (23)
/*Limitation*/
Set limitations according to Equation (15)
Update the position

q_{i}

and velocity

p_{i}

according to Equations (24) and (25)
end for