The Mixing Algorithm of ACO and GA Based Global Path Planning Method for Mobile Robot

An ant colony algorithm is a stochastic searching optimization algorithm that is based on the heuristic behavior of the biologic colony. Its positive feedback and coordination make it possible to be applied to a distributed system. It has favorable adaptability in solving combinatorial optimization and has great development potential for its connotative parallel property. This study focused on global path planning with an ant colony algorithm in an environment based on grids, which explores a new path planning algorithm. How to present and update the pheromone of an ant system was investigated. The crossover operation of a genetic algorithm was used in the ant system for path optimization. Experimental results show that the algorithm has better path planning optimization ability than other algorithms.

Download Full-text

A Path-Planning Algorithm for AGV Based on the Combination between Ant Colony Algorithm and Immune Regulation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.422.3 ◽

2011 ◽

Vol 422 ◽

pp. 3-9 ◽

Cited By ~ 3

Author(s):

Jian Zhong Huang ◽

Yu Wan Cen

Keyword(s):

Path Planning ◽

Immune Regulation ◽

Ant Colony Algorithm ◽

Visibility Graph ◽

Ant Colony ◽

Environment Modeling ◽

Environment Model ◽

Global Path Planning ◽

Planning Algorithm ◽

Path Planning Algorithm

For the demand of AGV’s environment modeling and path-planning,the paper discusses how to establish static environment model of visibility graph and proposes a visibility table method.Moreover,based on the environment modeling,we put forward a new kind of global path-planning algorithm by the combination between ant colony algorithm and immune regulation.

Download Full-text

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Mathematical Problems in Engineering ◽

10.1155/2019/2902170 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Guoqing Xia ◽

Zhiwei Han ◽

Bo Zhao ◽

Caiyun Liu ◽

Xinwei Wang

Keyword(s):

Path Planning ◽

Ant Colony Algorithm ◽

High Efficiency ◽

Multiple Objectives ◽

Ant Colony ◽

Marine Environments ◽

Global Path Planning ◽

Planning Algorithm ◽

Path Planning Algorithm ◽

Number Of Iterations

As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

Download Full-text