Abstract
In this paper, we proposed an adaptive ACS algorithm by introducing an adaptive pheromone volatility coefficient and the algorithm diversity dynamically varying in different iterations of the algorithm. It incorporates a shunting hierarchical hybrid neural network application algorithm (Shunting HHNN Application Algorithm, SHAA) to overcome the drawbacks of global optimization capabilities of ant colony system (ACS) in solving robot path and easily being trapped into the local optimal solution. Considering the influence of the activation value size on the selection of the grid in the SHAA neural network algorithm, the distance factor and the activation value are combined to improve the heuristic function. This will not only ensure the convergence speed, but also avoid the premature stagnation and being trapped into a local optimal path. Simulation results show that the algorithm discussed in this paper outperforms better in both the global optimization ability and the robustness.
Highlights
As the world enters a new era of industry, robots play an irreplaceable role in various industries
In Lamini et al.2 the improved genetic algorithm (GA) was used for the path planning, which had the advantages of strong robustness and implicit parallelism, but it was prone to a premature convergence; Hassani et al.3 has used the grid method to model the environment and used A* algorithm to find the optimal path
In order to store the open set and close set in complex environment, this method takes up a lot of memories and runs slowly; In Sun,4 the artificial potential field method was used for path planning of the robot
Summary
As the world enters a new era of industry, robots play an irreplaceable role in various industries. In order to store the open set and close set in complex environment, this method takes up a lot of memories and runs slowly; In Sun, the artificial potential field method was used for path planning of the robot This method is very simple and fast, but it is easy to fall into a local minimum and the target is not reachable; In Tang et al. particle swarm optimization (PSO) was used to simulate the foraging behaviors of birds. This method has the advantages of less parameter adjustment and fast search speed, but it is prone to the premature convergence in the late search period. These methods have achieved good results in solving robot path planning problems, but they still have certain defects when facing with complex environments
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