Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment.

Ni Xiong,Junyong Ma,Xiuqing Yang,Yong Xiang,Xinzhi Zhou

doi:10.3389/fnbot.2021.642733

Abstract

This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment.

Highlights

In mobile robot navigation, global path planning has always been one of the research hotspots
Algorithm time taboo ant colony optimization (TTACO) Begin Create grid environment Adaptive initial pheromone distribution according to formula (6) Repeat for each ant k do Trigger the three-step arbitration taboo method based on probability Add the grids involved in the Occupancy grid prediction model and three-step arbitration taboo method to TABUs if grid i ǫ allowk if grid i ǫ TABUlock Rollback end if According to formula (1) and (5) select grid j Update taboo end if Pheromone preferential limited update for each iteration Until Meet the iteration end condition Return best grid serial number END
When the distance between mobile robot and dynamic obstacle is less than safety distance

Summary

INTRODUCTION

Global path planning has always been one of the research hotspots. Algorithm TTACO Begin Create grid environment Adaptive initial pheromone distribution according to formula (6) Repeat for each ant k do Trigger the three-step arbitration taboo method based on probability Add the grids involved in the Occupancy grid prediction model and three-step arbitration taboo method to TABUs if grid i ǫ allowk if grid i ǫ TABUlock Rollback end if According to formula (1) and (5) select grid j Update taboo end if Pheromone preferential limited update for each iteration Until Meet the iteration end condition Return best grid serial number END when the distance between mobile robot and dynamic obstacle is less than safety distance. By comparing with other algorithms, it can be concluded that both the length of the path and the time of iteration in TTACO are better than those in similar literatures, and the algorithm of this article can get a better path in a shorter time than the ACO

CONCLUSION

DATA AVAILABILITY STATEMENT