Improved Ant Colony Algorithm And Its Application To Service Robots

In recent years,the rapid development and advancement of service robot technology has greatly facilitated people's lives and production.Most service robots need mobile functions,so path planning technology to solve robot mobility problems is one of the important research fields of service robots.Scholars have proposed many intelligent algorithms to solve mobile robot path planning problems,including ant colony algorithm.The ant colony algorithm is a bionic intelligent algorithm proposed in the 1990 s.The algorithm was originally applied to solve the traveling salesman problem.As the performance of the algorithm continues to improve,the ant colony algorithm is applied to more combinatorial optimization problems,such as mobile robot path.planning.Mobile robot path planning means that the robot finds a safe and collision-free shortest path from the starting point to the target point in a given environment.Although the ant colony algorithm can solve the robot path planning problem,it still has problems such as easy to fall into local optimum,slow convergence and poor diversity.This paper has done the following work on these issues.Firstly,although MMAS have made significant improvements on the basis of AS,there are still some shortcomings in the algorithm.These defects lead to the algorithm being easy to fall into local optimum and enter the iterative stagnant state.This paper proposes a new synergy based on collaborative strategy for multiple groups of ant colony algorithms.The algorithm introduces multiple ant group mechanisms.When the algorithm falls into local optimum,a new ant group is created to replace the previous group to restart the iteration,and the pheromone is adjusted.These improvements effectively improve the defects that MMAS is prone to stagnation,and ensure the diversity of the algorithm,with tring not to increase the time complexity of the algorithm.The experimental results of the improved algorithm and MMAS are compared and show the effectiveness of the proposed method.The experimental results show the effectiveness of the proposed method.Secondly,for ant colony algorithm and many other algorithms,the balance of the algorithm diversity and convergence speed is important in algorithm research.In order tosolve poor diversity,easy to fall into local optimum and slow convergence,this paper proposes a two-population ant colony algorithm based on dynamic heuristic operator.The algorithm obtains a dynamic random heuristic function based on the existing path,which can effectively avoid the algorithm falling into local optimum.Secondly,the new population is introduced and the interaction between the populations is realized by sharing the pheromone.The two group parameters are adjusted to accelerate the convergence speed of the algorithm.The robot path planning simulation experiment presented in this paper shows the effectiveness of the improved algorithm.This paper also introduces the characteristics and the tools of the robot operating platform applied to the service robots,and introduces the operating platform architecture in detail.It describes the navigation mapping algorithm that a mobile robot can perform autonomous positioning and navigation to the specified target location in a given scene,avoiding obstacles.Finally,the improved algorithm is applied to the operation platform,and the robot navigation simulation experiment of 2D robot simulator and the robot navigation construction experiment under real map environment are carried out.