蚁群优化算法
运动规划
最短路径问题
路径(计算)
数学优化
趋同(经济学)
计算机科学
算法
背景(考古学)
图形
人工智能
数学
机器人
理论计算机科学
经济增长
古生物学
经济
生物
程序设计语言
作者
Charis Ntakolia,Dimitrios V. Lyridis
标识
DOI:10.1016/j.oceaneng.2022.111418
摘要
The increased use of unmanned surface vehicles (USVs) to various applications in complex environments has led to the need of novel path planning approaches that can effectively address multi-objective path planning problems. Ant Colony Optimization (ACO) is a well-known and popular path search algorithm for graph-based problems that finds the shortest path. It has been widely used for solving path planning problems for robotic vehicles, and unmanned aircraft and surface vehicles, among others. To this end, this study focuses on extending the use of ACO to cope with multiple objectives by using fuzzy inference systems, such as Mamdani and Takagi–Sugeno–Kang (TSK), or the root mean square error (RMSE) criterion adopted to the state-of-the-art SIGPA algorithm. A comparative evaluation of these approaches is conducted in the context of solving different scenarios of multi-objective USV path planning problems. The results showed that ACO with Mamdani reached better performance in terms of solution quality compared to the other approaches under examination, while ACO with RMSE presented higher convergence speed and ACO with TSK balanced better among convergence speed and solution quality. Thus, each of the proposed approaches can be considered for multi-objective path planning of USVs depending the application needs.
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