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Unmanned Ground Vehicle and Flying Cars Motion Planning and Control in Complex Environment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4538

Special Issue Editors

Dr. Hongbin Ren

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: vehicle system dynamics; unmanned ground vehicle planning and control; behavior prediction
Dr. Yang Wang

E-Mail Website
Guest Editor
China North Vehicle Research Institute, Beijing 100072, China
Interests: optimal and adaptive control; intelligent control; motion planning and control problems related to unmanned aerial/ground vehicles
Dr. Jianbo Feng

E-Mail Website
Guest Editor
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: autonomous vehicle path planning and tracking; vehicle platoon coordinated control
Dr. Mingtao Yao

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: land-and-air three-dimensional transportation mobile platform; flying car; triphibious vehicle

Special Issue Information

Dear Colleagues,

Connected, autonomous and flying vehicle technology offers promising benefits for traffic congestion, energy conservation, reducing pollutants, emergency rescue, etc. However, the complexity of the working environment (including complex terrain and unreliable communication network) and the uncertainty of potential risk restrict the rapid development of both ground and aerial unmanned systems. In recent years, with the continuous development of artificial intelligence and the application of unmanned systems, new research results have emerged regarding maneuvering planning and control technology of connected, autonomous and flying vehicles.

Numerous researches have been devoted to addressing challenges such as perception and cognition in a denied environment, ground–air cooperative positioning and navigation, among others. Furthermore, various planning and control approaches have been proposed for the so-called “intrinsic limits” (such as robustness, stability, adaptability and feasibility).

This Special Issue will highlight articles and reviews advancing our exploration of autonomous driving and flying cars. It aims to showcase state-of-art research on motion planning and control as well as overall structural design for unmanned vehicles and flying cars in complex environments. The papers in this Special Issue focus on these systems, presenting relevant results and progress through thematic exchanges.

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Multi-platform coordination, motion planning and control;
  • Complex environment perception, cognition and decision making;
  • Robust and resilient coordinated vehicle control subject to sensor failure;
  • Ground–air cooperative positioning and navigation;
  • Real-time motion planning and control;
  • Design and control of flying cars / triphibious vehicles.

I look forward to receiving your contributions.

Dr. Hongbin Ren
Dr. Yang Wang
Dr. Jianbo Feng
Dr. Mingtao Yao
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. Sustainability 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

  • motion planning and control
  • complex environment
  • multi-platform coordination

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Published Papers (2 papers)

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Research

18 pages, 6333 KiB  
Article
A Real-Time Negative Obstacle Detection Method for Autonomous Trucks in Open-Pit Mines
by Shunling Ruan, Shaobo Li, Caiwu Lu and Qinghua Gu
Sustainability 2023, 15(1), 120; https://doi.org/10.3390/su15010120 - 21 Dec 2022
Cited by 4 | Viewed by 2052
Abstract
Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the [...] Read more.
Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds. Full article
(This article belongs to the Special Issue Unmanned Ground Vehicle and Flying Cars Motion Planning and Control in Complex Environment)
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18 pages, 4456 KiB  
Article
Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction
by Zepeng Gao, Jianbo Feng, Chao Wang, Yu Cao, Bonan Qin, Tao Zhang, Senqi Tan, Riya Zeng, Hongbin Ren, Tongxin Ma, Youshan Hou and Jie Xiao
Sustainability 2023, 15(1), 114; https://doi.org/10.3390/su15010114 - 21 Dec 2022
Viewed by 1709
Abstract
The controller design of vehicle systems depends on accurate reference index input. Considering information fusion and feature extraction based on existing data settings in the time domain, if reasonable input is selected for prediction to obtain accurate information of future state, it is [...] Read more.
The controller design of vehicle systems depends on accurate reference index input. Considering information fusion and feature extraction based on existing data settings in the time domain, if reasonable input is selected for prediction to obtain accurate information of future state, it is of great significance for control decision-making, system response, and driver’s active intervention. In this paper, the nonlinear dynamic model of the four-wheel steering vehicle system was built, and the Long Short-Term Memory (LSTM) network architecture was established. On this basis, according to the real-time data under different working conditions, the information correction calculation of variable time-domain length was carried out to obtain the real-time state input length. At the same time, the historical state data of coupled road information was adopted to train the LSTM network offline, and the acquired real-time data state satisfying the accuracy was used as the LSTM network input to carry out online prediction of future confidence information. In order to solve the problem of mixed sensitivity of the system, a robust controller for vehicle active steering was designed with the sideslip angle of the centroid of 0, and the predicted results were used as reference inputs for corresponding numerical calculation verification. Finally, according to the calculated results, the robust controller with information prediction can realize the system stability control under coupling conditions on the premise of knowing the vehicle state information in advance, which provides an effective reference for controller response and driver active manipulation. Full article
(This article belongs to the Special Issue Unmanned Ground Vehicle and Flying Cars Motion Planning and Control in Complex Environment)
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