Abstract
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Highlights
In recent years, unmanned aerial vehicles (UAVs) have received significant attention from the military and commercial industries
Many studies have been conducted on UAV development because of its wide variety of applications, including surveillance, traffic monitoring, rescue mission, and aerial photography [1,2,3,4,5,6,7,8,9,10,11,12]
Various algorithms are applicable for UAV path planning
Summary
In recent years, unmanned aerial vehicles (UAVs) have received significant attention from the military and commercial industries. Many studies have been conducted on UAV development because of its wide variety of applications, including surveillance, traffic monitoring, rescue mission, and aerial photography [1,2,3,4,5,6,7,8,9,10,11,12]. Various algorithms are applicable for UAV path planning. This includes graph search algorithm [13, 14], potential field based algorithm [15], probabilistic roadmap algorithm [16, 17], rapidly-exploring random trees algorithm [18, 19], Dubin curve based algorithm [20, 21] and evolutionary algorithm [22]. Graph search algorithms are not efficient when they are used in large search space environment
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