Abstract
Abstract A path planning method for mobile robots based on two dimensional cellular automata is proposed. The method can be applied for environments with both concave and convex obstacles. It is also appropriate for multi-robot problems as well as dynamic environments. In order to develop the planning method, environment of the robot is decomposed to a rectangular grid and the automata is defined with four states including Robot cell, Free cell, Goal cell and Obstacle cell. Evolution rules of automata are proposed in order to direct the robot toward its goal. CA based path planner method is afterwards modified by a colony technique to be applicable for concave obstacles. Then a layered architecture is proposed to autonomously implement the planning algorithm. The architecture employs an abstraction approach which makes the complexity manageable. An important feature of the architecture is internal artifacts that have some beliefs about the world. Most actions of the robot are planned and performed with re...
Highlights
Path planning problem of a mobile robot means finding a collision-free path between two specified positions in robot's configuration space[1]
Due to the advantages of Cellular Automata (CA) in fast and reliable parallel computation and its local computation properties it was selected as a tool to develop a path planning method for mobile robots
The robot environment is considered as two dimensional automata with four states: Robot cell, Free cell, Obstacle cell and Goal cell
Summary
Path planning problem of a mobile robot means finding a collision-free path between two specified positions in robot's configuration space[1]. Marchese presented a reactive path-planning algorithm for a non-holonomic mobile robot on multilayered cellular automata[18] He afterwards introduced a fast path planner for a multi-robot environment composed of robots with generic shapes and sizes (user defined) and different kinematics[19]. In this paper we will unify our abovementioned preliminary works to introduce a planning method that does not need primary information of the environment and it is suitable for both convex and concave obstacles. According to the advantages of the ant colony algorithms in solving complex problems with large search spaces[24, 25], we can nominate ant colonies to be employed in path planning algorithms of mobile robots in the environments with concave obstacles. The architecture employs an abstraction approach which makes the complexity manageable
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