Abstract
The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.
Highlights
The three-dimensional (3d) route planning for unmanned aerial vehicle (UAV) can be considered as a multi-constraint global optimization problem [1], and the main purpose of this problem is to search for the optimal route from the departure point to the target point autonomously according to the task requirements and the flight constraints
This paper proposed a modified sparrow search algorithm called CASSA and successfully applied it for solving the 3d UAV route planning problem in complex task space
In the proposed CASSA, the chaotic strategy is used to enhance the stability of the algorithm, and an adaptive inertia weight is used to balance the convergence speed and exploration ability
Summary
The three-dimensional (3d) route planning for unmanned aerial vehicle (UAV) can be considered as a multi-constraint global optimization problem [1], and the main purpose of this problem is to search for the optimal route from the departure point to the target point autonomously according to the task requirements and the flight constraints. Deterministic algorithms are quite mature in mathematical theory, there exists the problem that they are less effective in dealing with discontinuous and non-derivative functions, and they are trapped into local optimal solutions when solving the UAV route planning problem with multiple constraints [10]. Due to those problems that deterministic algorithms have, researchers have begun to pay more attention to heuristic algorithms. It is significant to use heuristic algorithms to obtain the optimal solution for global optimization problems
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