Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Path Planning of Mobile Robot Based on Improved Ant Colony Optimization
For the global path planning of mobile robots in static environment, the traditional ant colony optimization (ACO) algorithm has some disadvantages, such as slow convergence speed and easy to fall into local optimum. In order to deal with these problems, a novel improved ACO algorithm is proposed in this paper. First, in order to accelerate the convergence of the traditional ACO algorithm, several coefficients, such as pheromone volatility coefficient, path feasible coefficient, pheromone eliciting factor, expectation eliciting factor, memory parameter, social phobia parameter, personality factor and emotion fluctuation coefficient, were designed in the improved ACO algorithm. Meanwhile, in order to decrease the probability of the traditional ACO algorithm falling into the local optimum, a specific parameter is designed to make the ants evolve continuously, so the ants can reach the target by the shortest path. Finally, three simulation experiments are carried out in static environment to verify the potential of improved ant colony algorithm in mobile robot path planning.
Path Planning of Mobile Robot Based on Improved Ant Colony Optimization
For the global path planning of mobile robots in static environment, the traditional ant colony optimization (ACO) algorithm has some disadvantages, such as slow convergence speed and easy to fall into local optimum. In order to deal with these problems, a novel improved ACO algorithm is proposed in this paper. First, in order to accelerate the convergence of the traditional ACO algorithm, several coefficients, such as pheromone volatility coefficient, path feasible coefficient, pheromone eliciting factor, expectation eliciting factor, memory parameter, social phobia parameter, personality factor and emotion fluctuation coefficient, were designed in the improved ACO algorithm. Meanwhile, in order to decrease the probability of the traditional ACO algorithm falling into the local optimum, a specific parameter is designed to make the ants evolve continuously, so the ants can reach the target by the shortest path. Finally, three simulation experiments are carried out in static environment to verify the potential of improved ant colony algorithm in mobile robot path planning.
Path Planning of Mobile Robot Based on Improved Ant Colony Optimization
J. Inst. Eng. India Ser. B
Zhou, Yuyang (Autor:in) / Wang, Dongshu (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 103 ; 2073-2083
01.12.2022
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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