SCIENTIA SINICA Informationis, Volume 49 , Issue 5 : 555-569(2019) https://doi.org/10.1360/N112018-00338

## Real-time task allocation for a heterogeneous multi-UAV simultaneous attack

• AcceptedMar 10, 2019
• PublishedMay 8, 2019
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### Abstract

In this study, we address a coupled task allocation and path planning problem for a multi-unmanned aerial vehicle (UAV) reconnaissance/attack. Both coupled task allocation and path planning problem are addressed. We propose a task allocation algorithm for maximizing system utility and a path planning algorithm for simultaneous arrival. Moreover, we consider the target's resource requirement and UAV's resource constraints based on a contract net protocol for task allocation. Benefits of destroying the target and costs of UAV attacking target are considered in the objective function. To address the multi-UAV path planning problem, a combination of cooperative particle swarm optimization algorithm, coordination variables, and coordination functions is proposed. UAV's kinematics constraint is considered for the path planning method. Compared with a polynomial time coalition formation algorithm (PTCFA), simulation results show that the proposed algorithm improves the average performance by at least $8%$ with simultaneous arrival using the path planning method.

### References

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• Figure 1

Task allocation process based on contract network protocol

• Figure 2

Path planning process using cooperative particle swarm optimization algorithm (CPSO)

• Figure 3

(Color online) Search task for multi-UAVs. (a) Initial time; (b) target 1 is detected ($t=29.4$ s)

• Figure 4

(Color online) The trajectories and curvature for UAV coalition. (a) Simultaneous arrival path for attacking target 1; (b) curvature for target 1; (c) simultaneous arrival path for attacking target 2; (d) curvature for target 2

• Figure 5

(Color online) Control inputs of angular velocity and velocity for six UAVs. (a) Angular velocity control inputs; (b) velocity control inputs

• Figure 6

(Color online) The performance of three tasks allocation algorithms by var-ying the number of UAVs. (a) Average mission time; (b) average system utility; (c) computation time of task allocation

• Table 1   Targets information
 Target Position (m) Resource requirement 1 (1500, 1500) (4, 2) 2 (2000, 1500) (3, 1)
•

Algorithm 1 Obtain all the feasible coalitions

Input: potential coalition members set $U_i^R$, the resource requirement of the target $T_j$: $R^{T_j}$.

Output: feasible coalitions set $C_{\rm~feasible}$.

Step 1: set minimum coalition size $s_{\rm~min}=1$.

Step 2: calculate all potential coalitions of size $s_{\rm~min}$ using the UAVs in the potential coalition members set $U_i^R$.

Step 3: calculate the resource of each potential coalition of size $s_{\rm~min}$, if it satisfies the resource requirement (8), then, this potential coalition is saved in $C_{\rm~feasible}$.

Step 4: if $C_{\rm~feasible}~\not=~\emptyset$, then output $C_{\rm~feasible}$. Otherwise, go to Step 5.

Step 5: $s_{\rm~min}=s_{\rm~min}+1$, go to Step 2.

• Table 2   Initial information of six UAVs
 UAV Position (m) Heading angle (rad) Resource Type $U_1$ (0, 0) $\pi/6$ (2, 1) 1 $U_2$ (0, 0) $\pi/3$ (0, 0) 2 $U_3$ (1000, 2000) $\pi/6$ (3, 2) 1 $U_4$ (1000, 2000) $\pi/3$ (2, 0) 1 $U_5$ (2000, 2000) $\pi/6$ (2, 2) 1 $U_6$ (2000, 2000) $\pi/3$ (2, 0) 1
• Table 3   Simulation parameters
 Parameter Value Parameter Value $V_{\rm~min}$ 40 m/s $\omega_{\rm~max}$ 2 rad/s $V_{\rm~max}$ 60 m/s $c_1$ 2 $\kappa_{\rm~max}$ 0.02 $c_2$ 2 $R_{s1}$ 500 m Popsize 30 $R_{s2}$ 800 m
• Table 4   Average system utility improvement
 Number of UAVs The proposed algorithm PTCFA Percentage increase in utility (%) 6 60.19 55.67 8.12 8 64.81 57.83 12.07 10 66.88 59.58 12.25 15 73.56 57.03 28.96 20 77.75 56.22 38.30
• Table 5   Number of UAVs in different cases
 Case Number of UAVs Number of reconnaissance UAVs Number of attack UAVs 1 6 1 5 2 8 1 7 3 10 2 8 4 15 3 12 5 20 4 16

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