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Munan Li 1, 2
Academic Editor:David R. Salgado
1, School of Business Administration, South China University of Technology, Guangzhou 510641, China
2, Guangdong Provincial Key Laboratory of Innovation Method & Decision Management System, Guangzhou 510641, China
Received 23 February 2014; Revised 14 September 2014; Accepted 15 September 2014; 11 November 2014
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Generally, the three-dimensional path planning of robot can be defined simply in a given three-dimensional map. It is used to find the shortest path from the starting point to the target location [1, 2]. In the process of stepping forward to the destination, the robots need to avoid obstacles within the three-dimensional space and any locations that are difficult to cross. For example, the general underwater robot can move forward and backward, left and right, and up; however, it cannot jump, as it is hard to climb a very steep slope. Currently, the mature path planning of robots is still in the one-dimensional and two-dimensional field stages.
In three dimensions, the path planning of robot is challenging given the computing complexity, the large required information storage with multivariable and multiobjective constraints, and the difficulty in searching directly for the global optimization path. In engineering applications, three-dimensional path planning has been widely used in devices including mining robots, lunar rovers, EOD robots, underwater robots, and space detectors. Currently, the representative research on the problems of 3D path planning has achieved the following solutions: the A* algorithm, integer programming, the genetic algorithm, the particle swarm algorithm, and the ant colony algorithm [2-6]. However, in searching for the optimal path, the computation of the A* algorithm would be more difficult with an added dimension. The integer programming method attempts to simply transform the 2D path planning framework onto a three-dimensional domain. Given the strict constraint conditions, it is not easy to meet the needs of real environments. The genetic algorithm and particle swarm algorithm have been applied to path planning but have also encountered similar problems. Some scholars have even argued that most research on path planning of...