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Abstract
This paper presents a new algorithm for global path planning to a goal for a mobile robot using GA and fuzzy Algorithms. A genetic algorithm is used to find the optimal path for a mobile robot to move in a dynamic environment expressed by a map with nodes and links. Locations of target and obstacles to find an optimal path are given in an environment that is a 2-D workplace. Each via point (landmark) in the net is a gene which is represented using binary code. The number of genes in one chromosome is function of the number of obstacles in the map. Therefore, we used a fixed length chromosome. The generated robot path is optimal in the sense of the shortest distance. The fitness function of genetic algorithm takes full consideration of three factors: the collision avoidance path, the shortest distance and smoothness of the path. The specific genetic operators are also selected to make the genetic algorithm more effective. The simulation results verify that the genetic algorithm is high effective under various complex dynamic alien environments.
Highlights
IntroductionThe path planning problem of a mobile robot can be stated as: given (starting location, goal location, 2-D map of workplace including static obstacles), plan a collision-free path between two specified points in satisfying an optimization criterion with constraints (most commonly: shortest path)
The path planning problem of a mobile robot can be stated as: given, plan a collision-free path between two specified points in satisfying an optimization criterion with constraints
Over the latest decade, fuzzy logic and genetic algorithm has been widely used for mobile robots control to handle both general navigation problems, and specific motion control problems, such as, e.g., parking problem (Gray, 1998)
Summary
The path planning problem of a mobile robot can be stated as: given (starting location, goal location, 2-D map of workplace including static obstacles), plan a collision-free path between two specified points in satisfying an optimization criterion with constraints (most commonly: shortest path). The adoption of such an approach leads to the use of fuzzy modules whose design is simple, rapid, inexpensive, and maintained because the rules can be linguistically interpreted by a human expert For these reasons, over the latest decade, fuzzy logic and genetic algorithm has been widely used for mobile robots control to handle both general navigation problems, and specific motion control problems, such as, e.g., parking problem (Gray, 1998). Following the preliminary work in (LaValle & Kuffner, 2001) this paper combines a velocity controller with a simple fuzzy system that limits on-line the advancing speed of the vehicle so as to allow following an assigned path in compliance with the holding kinematic constraints To accomplish this task, the linguistic variables curve and distance are introduced, that give information concerning the relevant geometric characteristics of the path ahead the robot.
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