Abstract
Navigation of mobile robots in a grid based environment is useful in applications like warehouse automation. The environment comprises of a number of free grid cells for navigation and remaining grid cells are occupied by obstacles and/or other mobile robots. Such obstructions impose situations of collisions and dead-end. In this work, a neural dynamics based algorithm is proposed for complete coverage of a grid based environment while addressing collision avoidance and dead-end situations. The relative heading of the mobile robot with respect to the neighbouring grid cells is considered to calculate the neural activity. Moreover, diagonal movement of the mobile robot through inter grid cells is restricted to ensure safety from the collision with obstacles and other mobile robots. The circumstances where the proposed algorithm will fail to provide completeness are also discussed along with the possible ways to overcome those situations. Simulation results are presented to show the effectiveness of the proposed algorithm for a single and multiple mobile robots. Moreover, comparative studies illustrate improvements over other algorithms on collision free effective path planning of mobile robots within a grid based environment.
Highlights
Path planning of a single and multiple robots is a very fundamental issue in the field of robotics
When the workspace is divided into several grid cells it is called a complete grid path planning (CGPP) or a complete grid coverage (CGC) [1, 2]
A neural dynamic based algorithm is proposed for complete grid coverage
Summary
Path planning of a single and multiple robots is a very fundamental issue in the field of robotics. In [2], the authors presented a complete area path planning algorithm to cope up with a dead-end situation. They had used a global backtracking method for the robot to find an unvisited position when the robot faces the dead-end situation. Oh et al [21] presented a complete grid coverage algorithm based on the triangular cell decomposition method where the robot was capable to construct the map of an unknown environment. The article is organized as follows; Section 2 describes the environmental set up
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