An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

Abstract

This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

“…Although, ACO has drawbacks of slow convergence and pheromone update. To address this problem, many approaches have been proposed (Stützle and Hoos, 2000;Zeng et al, 2016;Zhao et al, 2016). Pheromone rate has been updated after each successful iteration of ant in ACO to improve the convergence rate (Zhao et al, 2016).…”

“…To address this problem, many approaches have been proposed (Stützle and Hoos, 2000;Zeng et al, 2016;Zhao et al, 2016). Pheromone rate has been updated after each successful iteration of ant in ACO to improve the convergence rate (Zhao et al, 2016). In Zeng et al (2016) convergence rate with search ability has been increased through upgrading pheromone update formula and adaptively varying volatilization rate.…”

“…In set of final trajectories, lateral movements have been unified with longitudinal movements. In this content, Quantic polynomial (Rathgeber et al, 2015) has been used to get smooth lateral deviations suitable to longitudinal terminal manifolds in different Algorithm 1 2 3 1 2 3 1 2 3 1 2 3 Number of sharp bends 10 10 0 12 10 0 -13 0 -10 Zhao et al (2016). 2-described in Long et al (2019).…”

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

“…Since USV global path planning involves optimization algorithms, environmental models, and marine craft hydrodynamics, existing path planning algorithms have difficulty meeting the mission requirements. Intelligent optimization algorithms are widely used in global path planning, such as the genetic algorithm [8], particle swarm algorithm [9], NSGA-II [10], and ant colony algorithm [11]. With the development of quantum technology, the idea of combining quantum computing with intelligent optimization algorithms has been developed.…”

Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

“…In the 1990s, Dorigo et al [1] were inspired by the foraging behavior of ants and proposed an ant colony algorithm (ACA) that had the characteristics of strong robustness, a high degree of parallelism, and positive feedback. The ACA demonstrates high effectiveness and superiority in global optimization and in solving the traveling salesman [2,3], shop scheduling [4][5][6][7], and robot path planning [8][9][10] problems.…”