Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

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A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419894787 ◽

2020 ◽

Vol 17 (1) ◽

pp. 172988141989478 ◽

Cited By ~ 1

Author(s):

Zhaoying Li ◽

Zhao Zhang ◽

Hao Liu ◽

Liang Yang

Keyword(s):

Path Planning ◽

Computational Efficiency ◽

Free Space ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Optimal Path ◽

Convex Polygons ◽

Convex Decomposition ◽

Bee Colony ◽

High Computational Efficiency

Free space algorithms are kind of graphics-based methods for path planning. With previously known map information, graphics-based methods have high computational efficiency in providing a feasible path. However, the existing free space algorithms do not guarantee the global optimality because they always search in one connected domain but not all the possible connected domains. To overcome this drawback, this article presents an improved free space algorithm based on map decomposition with multiple connected domains and artificial bee colony algorithm. First, a decomposition algorithm of single-connected concave polygon is introduced based on the principle of concave polygon convex decomposition. Any map without obstacle is taken as single-connected concave polygon (the convex polygon map can be seen as already decomposed and will not be discussed here). Single concave polygon can be decomposed into convex polygons by connecting concave points with their visible vertex. Second, decomposition algorithm for multi-connected concave polygon (any map with obstacles) is designed. It can be converted into single-connected concave polygon by excluding obstacles using virtual links. The map can be decomposed into several convex polygons which form multiple connected domains. Third, artificial bee colony algorithm is used to search the optimal path in all the connected domains so as to avoid falling into the local minimum. Numerical simulations and comparisons with existing free space algorithm and rapidly exploring random tree star algorithm are carried out to evaluate the performance of the proposed method. The results show that this method is able to find the optimal path with high computational efficiency and accuracy. It has advantages especially for complex maps. Furthermore, parameter sensitivity analysis is provided and the suggested values for parameters are given.

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Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment

Journal of Electrical and Computer Engineering ◽

10.1155/2016/3620895 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Yong-feng Dong ◽

Hong-mei Xia ◽

Yan-cong Zhou

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Mobile Robot ◽

Optimal Path ◽

Dynamic Environment ◽

Adaptive Genetic Algorithm ◽

Local Optimum ◽

Original Algorithm ◽

Sequencing Rule ◽

Planning Methods

In the smart home environment, aiming at the disordered and multiple destinations path planning, the sequencing rule is proposed to determine the order of destinations. Within each branching process, the initial feasible path set is generated according to the law of attractive destination. A sinusoidal adaptive genetic algorithm is adopted. It can calculate the crossover probability and mutation probability adaptively changing with environment at any time. According to the cultural-genetic algorithm, it introduces the concept of reducing turns by parallelogram and reducing length by triangle in the belief space, which can improve the quality of population. And the fallback strategy can help to jump out of the “U” trap effectively. The algorithm analyses the virtual collision in dynamic environment with obstacles. According to the different collision types, different strategies are executed to avoid obstacles. The experimental results show that cultural-genetic algorithm can overcome the problems of premature and convergence of original algorithm effectively. It can avoid getting into the local optimum. And it is more effective for mobile robot path planning. Even in complex environment with static and dynamic obstacles, it can avoid collision safely and plan an optimal path rapidly at the same time.

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