Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm

Abstract

Aiming at the problems of slow convergence and easy fall into local optimal solution of the classic ant colony algorithm in path planning, an improved ant colony algorithm is proposed. Firstly, the Floyd algorithm is introduced to generate the guiding path, and increase the pheromone content on the guiding path. Through the difference in initial pheromone, the ant colony is guided to quickly find the target node. Secondly, the fallback strategy is applied to reduce the number of ants who die due to falling into the trap to increase the probability of ants finding the target node. Thirdly, the gravity concept in the artificial potential field method and the concept of distance from the optional node to the target node are introduced to improve the heuristic function to make up for the fallback strategy on the convergence speed of the algorithm. Fourthly, a multi-objective optimization function is proposed, which comprehensively considers the three indexes of path length, security, and energy consumption and combines the dynamic optimization idea to optimize the pheromone update method, to avoid the algorithm falling into the local optimal solution and improve the comprehensive quality of the path. Finally, according to the connectivity principle and quadratic B-spline curve optimization method, the path nodes are optimized to shorten the path length effectively.

Learning-Based Neural Ant Colony Optimization

“…In addition, they utilized the optimal and worst solutions to improve the method for updating global pheromones. Other approaches aim to integrate ACO with other traditional methods [8,20,25]. However, in all of these works, information learned from solving one instance cannot be re-used on new instances.…”

Scheme design of intelligent vehicle auto drive system

“…Track tracking control of intelligent vehicle is an important part of intelligent vehicle system [17][18] . The accuracy and stability of trajectory tracking control directly determine the control performance of intelligent vehicle system 19 . Typical trajectory tracking algorithms include model prediction algorithm, PID algorithm, Pure Pursuit algorithm, Stanley algorithm, etc.…”

Research on Global Path Planning of Robot Based on Ant Colony Algorithm and Gaussian Sampling

“…Specifcally, robot path planning focuses on designing a collision-free and low-cost shortest path from the start point to the end point to achieve efcient obstacle avoidance and low cost for the robot. Algorithms commonly used in path planning include Dijkstra algorithm [1], Floyd algorithm [2], and A * algorithm [3], but they are all traditional search methods and have disadvantages such as low efciency. With the deepening of research, intelligent algorithms with high efciency such as genetic algorithm [4], simulated annealing method [5], neural network algorithm [6], and ant colony optimization algorithm (ACO) are developing rapidly.…”