Citation: Yang, L.; Fu, L.; Li, P.; Mao,
J.; Guo, N. An Effective Dynamic
Path Planning Approach for Mobile
Robots Based on Ant Colony Fusion
Dynamic Windows. Machines 2022,
10, 50. https://doi.org/10.3390/
machines10010050
Academic Editor: Dan Zhang
Received: 7 December 2021
Accepted: 7 January 2022
Published: 9 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An Effective Dynamic Path Planning Approach for Mobile
Robots Based on Ant Colony Fusion Dynamic Windows
Liwei Yang * , Lixia Fu *, Ping Li, Jianlin Mao and Ning Guo
School of Information Engineering and Automation, Kunming University of Science and Technology,
Kunming 650093, China; 20202104039@stu.kust.edu.cn (P.L.); Mao_Jianlin@163.com (J.M.);
x20202104047@stu.kust.edu.cn (N.G.)
* Correspondence: 18916336783ylw@gmail.com (L.Y.); 12309049@kust.edu.cn (L.F.)
Abstract:
To further improve the path planning of the mobile robot in complex dynamic environments,
this paper proposes an enhanced hybrid algorithm by considering the excellent search capability
of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window
approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment
model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO
from the pheromone update and heuristic function and then design a strategy to solve the deadlock
problem. Considering the actual path requirements of the robot, a new path smoothing method is
present. Finally, the robot modeled by DWA obtains navigation information from the global path, and
we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving
the evaluation function. The simulation and experimental results show that our algorithm improves
the robot’s navigation capability, search capability, and dynamic obstacle avoidance capability in
unknown and complex dynamic environments.
Keywords:
mobile robot; path planning; ant colony optimization; dynamic window approach;
deadlock problem; dynamic obstacle avoidance
1. Introduction
Mobile robots have various applications in various fields, and their autonomous
navigation in ambient space is crucial [
1
]. When robots have a priori information about
the environment, they can plan a global path from the starting point to the endpoint and
optimize some certain goals, an ability which has received much attention [
2
]. However,
it is difficult for robots to have a priori environmental information, especially about the
dynamically changing factors, such as climatic conditions [3], unknown obstacles [4], and
unfamiliar terrain [
5
]. The robot needs to detect the surrounding environment in real-time
and make multiple plans to obtain a feasible, safe path. Good results have been achieved in
path planning research for solving unknown static environments [
6
–
9
], while the unknown
dynamic factors [
3
–
5
,
10
] constrain the reliable and robust motion of the robot in general
environments and present a significant challenge.
As the complexity of the environment and the difficulty of robot tasks increase, tradi-
tional path planning methods are challenging to achieve the desired results. Ant colony
optimization (ACO) has strong robustness and adaptability for solving global path planning
problems [
11
]. In recent years, related scholars have proposed many improvement strate-
gies and methods. Luo et al. [
12
] introduced optimal and worst solutions in pheromone
updating to expand the influence of high-quality ants and weaken the power of worst
ants, which accelerates the algorithm’s convergence. Dai et al. [
13
] proposed a smoothing
ACO that optimizes the number of path turns and path length. You et al. [
14
] designed a
new heuristic operator to improve the diversity and convergence of the population search.
To improve the solution accuracy of the algorithm, Xu et al. [
15
] proposed a mutually
Machines 2022, 10, 50. https://doi.org/10.3390/machines10010050 https://www.mdpi.com/journal/machines
