Authors:
Hanan Alsouly
and
Hachemi Bennaceur
Affiliation:
Al-lmam Muhammad Ibn Saud Islamic University, Saudi Arabia
Keyword(s):
Genetic Algorithm, Path Planning, Mobile Robot, Dynamic Environment, Static Environment.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Robotics and Intelligent Agents
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
Path planning is an important component for a mobile robot to be able to do its job in different types of environments. Furthermore, determining the safest and shortest path from the start location to a desired destination, intelligently and in quickly, is a major challenge, especially in a dynamic environment. Therefore, various optimisation methods are recommended to solve the problem, one of these being a genetic algorithm (GA). This paper investigates the capabilities of GA for solving the path planning problem for mobile robots in static and dynamic environments. First, it studies the different GA approaches. Then, it carefully designs a new GA with intelligent crossover to optimise the search process in static and dynamic environments. It also conducts a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality and execution time, comparing it against the well-known A* algorithm and MGA in a static scenario, and against the Improved GA in a d
ynamic scenario. The simulation results show that the proposed GA is able to find an optimal or near optimal solution with fast execution time compared to the three other algorithms, especially in large problems.
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