Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm

Abstract

SUMMARYThe current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

Path Planning for the Mobile Robot: A Review

“…Fei et al [35] proposed tailored GA for mobile robot's optimal path planning. Shorakaei et al [36] used a parallel GA for unmanned aerial vehicles' optimal cooperative path planning. The effectiveness of the method was shown by several simulations.…”

Unmanned vehicle path planning using a novel ant colony algorithm

“…Each grid has its corresponding serial number and coordinates, that is, the i-th row, the grid of the j-th column is denoted as D(i, j), and the corresponding serial number is k. The relationship between the grid number k and the coordinates (x i , y j ) is as shown in Eqs. (1) and (2).…”

“…Due to its importance, many researchers have conducted a large path planning algorithms. In the literature [2], the paper proposes a drone route planning based on particle swarm optimization algorithm. The corresponding mutation and fine adjustment of the inactive particles are carried out to ensure the particle group has strong vitality in the evolution process.…”

Research and Solution for the Shortest Routing Problem of Logistics Vehicle Based on Genetic Algorithm

“…Certain targets (for example, the shortest driving route, the lowest distribution cost, the optimal logistics distribution and service level and the less number of distribution vehicles etc) have been reached through optimizing vehicle scheduling and under the premise of satisfying customer demands (Shorakaei, 2014). In research, some basic assumptions are usually made for constraint conditions, for example, accomplish distribution task with one distribution center and single type of vehicle; the distributed goods can be mixed; the location of each client and their distances to distribution center are already known; distribution center has enough goods for delivery and has enough transportation capacity (Panday and Bansal, 2015); each client is served by one vehicle and can only be served by one vehicle; the demand of each route can't exceed the maximum load of vehicle; all the vehicles depart from distribution center and go back to distribution center after accomplishing the task (Mcgehee, 2013;Mousavi et al, 2011;Skok et al, 2001) It may possibly need to consider about multiple distribution centers, multiple vehicles, time demand of customer service as well as the randomization of customer demand.…”