Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Unmanned Aerial Vehicles (UAVs) play an essential role in information collection where routing and time scheduling are two critical factors and are considered sequently. Given the paths of UAVs, metaheuristic algorithms are explored to allocate searching time of UAVs, which are hard to guarantee the optimal solution for time allocation. In this paper, a novel searchinG timE allocatioN undEr coopeRative pAth pLaning (GENERAL) is proposed to solve the optimal solution of the time allocation given paths of UAVs. GENERAL adopts a semi-greedy construction and a repair procedure to initialize and amend the routing solutions during iterations. Motivated by the Newton’s method from convex optimization domains, we introduce a new Perturbed Parametric Nonlinear Complementarity Problem function (PPNCP-function), which reformulates the time allocation problem as a smoothing system of equations according to Karush-Kuhn-Tucker (KKT) theorem. Then a smoothing Newton method is introduced to obtain the optimal time allocation solution with superlinear convergence. Experimental results empirically indicate the GENERAL’s effectiveness compared to Ant Colony Optimization with Simulated Annealing (ACO-SA) and Genetic Algorithm (GA). Besides, some numerical results indicate that the smoothing Newton method with PPNCP function is promising. The emerging aspects of this paper include: 1) it integrates the path planning with time allocation problem for maximizing the information collection reward; 2) a new PPNCP-function and a smoothing Newton method have been proposed to solve the optimal time allocation under path planning of UAVs; 3) the theoretical convergence analysis of the smoothing Newton method has been provided in this paper.