A Hybrid Multi-Population Genetic Algorithm for UAV Path Planning

被引：36

作者：

Arantes, Marcio da Silva ^{[1
]}

Arantes, Jesimar da Silva ^{[1
]}

Motta Toledo, Claudio Fabiano ^{[1
]}

Williams, Brian C. ^{[2
]}

机构：

[1] Univ Sao Paulo, Sao Carlos, SP, Brazil

[2] MIT, Cambridge, MA 02139 USA

来源：

GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2016年

关键词：

Evolutionary Computation; Optimization; Unmanned Aerial Vehicles; Path Planning; SYSTEMS;

D O I：

10.1145/2908812.2908919

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper proposes a hybrid method to define a path planning for unmanned aerial vehicles in a non-convex environment with uncertainties. The environment becomes non-convex by the presence of no-fly zones such as mountains, cities and airports. Due to the uncertainties related to the path planning in real situations, risk of collision can not be avoided. Therefore, the planner must take into account a lower level of risk than one tolerated by the user. The proposed hybrid method combines a multi-population genetic algorithm with visibility graph. This is done by encoding all possible paths as individuals and solving a linear programming model to define the full path to be executed by the aircraft. The hybrid method is evaluated from a set of 50 maps and compared against an exact and heuristic approaches with promising results reported.

引用

页码：853 / 860

页数：8

共 17 条

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