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Electronics
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Autonomous Vehicles: Path Planning and Navigation
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Autonomous Vehicles: Path Planning and Navigation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 5043

Special Issue Editors

Dr. Huan Yin

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong
Interests: autonomous navigation; mobile robots; localization and mapping; path planning; deep learning
Dr. Yue Wang

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: navigation; localization and mapping; learning based control
Dr. Yanmei Jiao

E-Mail Website
Guest Editor
School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China
Interests: visual localization; robust pose estimation; multi-sensor fusion

Special Issue Information

Dear Colleagues,

We are pleased to organize a Special Issue on “Autonomous Vehicles: Path Planning and Navigation”. With the growing interest and investment in autonomous driving technology, there has been a significant surge in research in this field. The articles in this Special Issue will present new ideas, methodologies, and applications that address the various challenges faced in this area. Topics will include but are not limited to, sensor fusion, machine learning, computer vision, state estimation, vehicle planning, and vehicular communication.

The Special Issue will serve as a valuable platform for academics, researchers, and industry professionals to exchange ideas and discuss the challenges and opportunities in autonomous navigation. It will also provide insights into the latest research directions, potential applications, and implications for the future of autonomous vehicles. Overall, this Special Issue is meant to provide a comprehensive overview of the latest research in the field of autonomous navigation, highlight key achievements, and identify future directions for research and development.

Dr. Huan Yin
Dr. Yue Wang
Dr. Yanmei Jiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous navigation
  • sensors and perception
  • localization and mapping
  • motion planning
  • multi-vehicle navigation
  • end-to-end autonomous driving
  • trustworthy autonomous vehicles

Published Papers (4 papers)

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Research

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23 pages, 4817 KiB  
Article
Multi-Vehicle Navigation Using Cooperative Localization
by Juan Carlos Oliveros and Hashem Ashrafiuon
Electronics 2023, 12(24), 4945; https://doi.org/10.3390/electronics12244945 - 08 Dec 2023
Viewed by 862
Abstract
This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then [...] Read more.
This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then presented based on the dynamic model. Relative position measurement equations and their linearized forms are introduced. The state and measurement equations are then employed for the propagation and update steps of an EKF-based cooperative localization algorithm. Initially, a series of experiments with networks of quadcopters and mobile robots are presented to validate the performance of cooperative localization for state estimation with the continuous or intermittent presence of absolute measurements or their complete absence. Finally, the performance of the control algorithm is evaluated with and without cooperative localization to demonstrate its effectiveness for improving performance. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Path Planning and Navigation)
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22 pages, 13667 KiB  
Article
Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning
by Hongda Guo, Youchun Xu, Yulin Ma, Shucai Xu and Zhixiong Li
Electronics 2023, 12(23), 4759; https://doi.org/10.3390/electronics12234759 - 23 Nov 2023
Viewed by 787
Abstract
Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. [...] Read more.
Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Path Planning and Navigation)
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16 pages, 6998 KiB  
Article
Obstacle Avoidance for Automated Guided Vehicles in Real-World Workshops Using the Grid Method and Deep Learning
by Xiaogang Li, Wei Rao, Dahui Lu, Jianhua Guo, Tianwen Guo, Darius Andriukaitis and Zhixiong Li
Electronics 2023, 12(20), 4296; https://doi.org/10.3390/electronics12204296 - 17 Oct 2023
Cited by 1 | Viewed by 1205
Abstract
An automated guided vehicle (AGV) obstacle avoidance system based on the grid method and deep learning algorithm is proposed, aiming at the complex and dynamic environment in the industrial workshop of a tobacco company. The deep learning object detection is used to detect [...] Read more.
An automated guided vehicle (AGV) obstacle avoidance system based on the grid method and deep learning algorithm is proposed, aiming at the complex and dynamic environment in the industrial workshop of a tobacco company. The deep learning object detection is used to detect obstacles in real-time for the AGV, and feasible paths are generated by the grid method, which ultimately finds an AGV obstacle avoidance solution in complex dynamic environments. The experimental results showed that the proposed system can effectively identify and avoid obstacles in a simulated tobacco production workshop environment, resulting in the average obstacle avoidance success rate of 98.67%. The transportation efficiency of cigarette factories is significantly improved with the proposed system, reducing the average execution time of handing tasks by 27.29%. This paper expects to provide a reliable and efficient solution for AGV obstacle avoidance in real-world industrial workshops. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Path Planning and Navigation)
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Review

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22 pages, 2745 KiB  
Review
Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning
by Yiqi Xu, Qiongqiong Li, Xuan Xu, Jiafu Yang and Yong Chen
Electronics 2023, 12(15), 3263; https://doi.org/10.3390/electronics12153263 - 29 Jul 2023
Cited by 5 | Viewed by 1629
Abstract
The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize [...] Read more.
The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Path Planning and Navigation)
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