Research On Path Planning Of Indoor Robot Based On Improved Ant Colony Algorithm

In recent years,research on artificial intelligence algorithms and application technology is in the ascendant,and mobile robots have attracted much attention as one of the representative carriers of artificial intelligence technology.The path planning of mobile robots has always been highly valued by scientists and engineers around the world.How to improve the real-time and flexibility of robots while moving is an important research content of path planning,especially indoor robots,because of their lack of reliability when indoor navigation.Global navigation satellite system signals,but also face the test of complex and changeable small real-world environments.Therefore,it is necessary to carry out research on the path planning of indoor robots.Ant colony algorithm is a common indoor robot path planning algorithm,which has the advantages of strong robustness,parallelism,and easy integration with other algorithms,but also has problems such as slow convergence speed and easy fall into local optimization.Aiming at these problems,this paper proposes an improved ant colony algorithm.It can overcome the long convergence time and efficiency of the traditional ant colony algorithm by dynamically adjusting the pheromone enhancement coefficient and pheromone volatility factor and establishing the interlocking relationship between pheromone factor and heuristic factor The disadvantages are low,and it has the advantages of fast convergence speed and strong global search ability.Through MATLAB simulation and analysis,the results show that the improved algorithm has better indoor path planning ability.In order to further optimize the path,the improved A* algorithm is integrated into the improved ant colony algorithm to enhance the directionality of the algorithm.At the same time,the B-spline curve smoothing algorithm is used to smooth the curve,reduce the steering angle of the robot,and enhance the smoothness of the path.It increases the flexibility of robot movement and forms a fusion algorithm with faster convergence speed and better generation path.Subsequently,this fusion algorithm is combined with a rolling window algorithm to perform dynamic obstacle avoidance for moving obstacles,and an approximately optimal path can still be obtained.Simulation and experimental analysis show that the fusion algorithm combined with the rolling window algorithm for dynamic obstacle avoidance has the advantages of fast convergence and strong environmental adaptability.Therefore,the research of indoor robot path planning based on improved ant colony algorithm proposed in this paper has good theoretical significance and practical engineering value.The main work of this article includes:First,it introduces and analyzes various existing path planning algorithms,establishes a mathematical model based on the principle of traditional ant colony algorithm,and performs path planning simulation through MATLAB platform.The analysis of simulation results shows that the traditional ant colony algorithm has the disadvantages of slow convergence and low efficiency when implementing path planning.Then by dynamically adjusting the pheromone enhancement coefficient and pheromone volatility factor,the interlocking relationship between the pheromone factor and the heuristic factor is established,and the global search ability of the ant colony algorithm is enhanced.Then take advantage of the ant colony algorithm's easy to combine with other algorithms.In order to further optimize the path,the improved ant colony algorithm and the improved A* algorithm are combined to enhance the orientation of the algorithm.In order to improve the smoothness of the generated path,the B-spline algorithm is introduced into the fusion algorithm.Simulation analysis shows that the fusion algorithm has the advantages of faster convergence and higher efficiency.Finally,the fusion algorithm is combined with the rolling window algorithm for dynamic obstacle avoidance.The fusion algorithm is used for path planning,and the rolling window algorithm is used for collision detection.If the robot and the moving obstacle will collide,perform local path planning,and use the intersection formed by the rolling window and the robot's position to the end line as the local target point;if the robot and the moving obstacle will not collide,continue Use fusion algorithm for path planning.