Mobile robot path planning method based on ant colony algorithm under dynamic environment

Abstract

The invention discloses a mobile robot path planning method based on an ant colony algorithm under a dynamic environment. The method comprises the following steps of 1, modeling the environment by using a grating method; 2, building local diffusion information pheromone tau''rs; 4, computing a transition probability; 5, adding a feasible node S into a tabu table; 6, completing paths; 7, acquiringthe shortest path via path information recorded in the tabu table; 8, updating pheromone matrix elements; and 9, finding the optimal path via iteration, that is to say an algorithm ending condition ismet, and outputting a final result. According to the ant colony algorithm based on a potential field method provided by the invention, the faster convergence speed and optimizing ability are provided, the local diffusion information pheromone has relative good smoothness, the collaboration ability of individual ants is further enhanced, the local cross paths are reduced, the quantity of lost antsis reduced, and thus the ant colony algorithm is converged to the global optimum with the faster speed, and meanwhile also achieves the diversity of learning, and the defect that an artificial potential field method is easy to trap in the local optimum is overcome.

Description

technical field [0001] The invention relates to the technical field of robots, in particular to a path planning method for a mobile robot based on an ant colony algorithm in a dynamic environment. Background technique [0002] The research of robot technology is becoming more and more popular, and more and more scientific researches have carried out the research on robot path planning. At present, the traditional methods of mobile robot path planning include grid method and artificial potential field method. The grid method is suitable for global path planning. planning, but when the environment space increases, the required storage space is large, the calculation efficiency is low, and the real-time decision-making is poor; Local optimum and deadlock phenomenon. With the increase of the complexity of the environment system and the difficulty of the task, the traditional path planning method based on the mathematical model is difficult to achieve the desired effect. Some bio...

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Problems solved by technology

[0002] The research of robot technology is becoming more and more popular, and more and more scientific researches have carried out the research on robot path planning. At present, the traditional methods of mobile robot path planning include grid method and artificial potential field method. The grid method is suitable for global path planning. planning, but when the environment space increases, the required storage space is large, the calculation efficiency is low, and the real-time decision-making is poor; Local optimum and deadlock phenomenon. With the increase of the complexity of the environment system and the difficulty of the task, the traditional path planning method based on the mathematical model is difficult to achieve the desired effect. Some bionic intelligent optimization algorithms have appeared, such as immune algorithm, artificial fish swarm Algorithm, genetic algorithm, particle swarm algorithm, etc., but these methods have problems such as large search space, complex algorithm, difficult to determine parameters, low search efficiency, easy to generate local optimal paths, and even no feasible paths can be found. Ant colony algorithm has been It is widely used to solve the traveling salesman problem, path problem, workpiece sequence problem, vehicle transportation scheduling problem, graph coloring problem, and robot path planning problem. In the classic ant colony algorithm, ants move towards the path with higher pheromone concentration to find the optimal solution. Path, this positive feedback mechanism can speed up the convergence speed, but it will lead to a decrease in the diversity of the ant colony and a weakening of the global search ability. In some more complex environments, the ant colony algorithm may enter a deadlock state, or in the It is difficult to plan mobile paths in the environment