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Applied Sciences
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Intelligent Robot: Design, Control and Optimization
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Intelligent Robot: Design, Control and Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 1610

Special Issue Editors

Dr. Teng Ma

E-Mail Website
Guest Editor
National Key Laboratory of Autonomous Marine Vehicle Technology, Harbin Engineering University, Harbin 150001, China
Interests: autonomous underwater vehicles; underwater navigation; marine environmental sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ye Li

E-Mail Website
Guest Editor
National Key Laboratory of Autonomous Marine Vehicle Technology, Harbin Engineering University, Harbin 150001, China
Interests: autonomous underwater vehicles; autonomous control; multi-agent cooperation
Dr. Rupeng Wang

E-Mail Website
Guest Editor
College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing 210024, China
Interests: autonomous underwater vehicles; marine robotic dynamics and control; marine environmental sensing

Special Issue Information

Dear Colleagues,

In light of the growing necessity for enhanced production efficiency, the assurance of secure operations in extreme environments, and the improvement of the quality of human life, intelligent robots, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned marine vehicles, play a pivotal role in industry, agriculture, healthcare, and scientific research. Characterized by high production efficiency and accuracy, long operational range, low operating costs (both equipment and personnel costs), and high levels of personnel safety, intelligent robots with suitable sensory modules have great potential in almost all areas of human society. The use and deployment of intelligent robots have presented challenges in design, sensing, control, optimization, navigation, and communication.

This Special Issue is dedicated to recent advances and future implications in intelligent robot technology. Your contributions can address current and emerging research and development issues, approaches, techniques, or applications; community, state, and/or international initiatives; and other topics related to intelligent robots. We seek to publish the original and novel research in the following areas:

  • Design of intelligent robots for specific application scenarios;
  • Navigation, path planning, and control of intelligent robots;
  • Multivehicle systems;
  • Vehicle power management and control;
  • Vehicle modeling and simulation;
  • Sensor systems for intelligent robots;
  • Artificial intelligence/machine learning methods.

Dr. Teng Ma
Prof. Dr. Ye Li
Dr. Rupeng Wang
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. Applied Sciences 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

  • intelligent robots
  • design
  • control
  • navigation
  • sensing

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Published Papers (1 paper)

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Research

15 pages, 2524 KiB  
Article
A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
by Jianqiao Chen and Guofu Tian
Appl. Sci. 2024, 14(22), 10209; https://doi.org/10.3390/app142210209 - 7 Nov 2024
Cited by 1 | Viewed by 1103
Abstract
Autonomous driving technology, as a key component of intelligent transportation systems, has gained considerable attention in recent years. While significant progress has been made in areas such as path planning, obstacle detection, and navigation, relatively less focus has been placed on vehicle speed [...] Read more.
Autonomous driving technology, as a key component of intelligent transportation systems, has gained considerable attention in recent years. While significant progress has been made in areas such as path planning, obstacle detection, and navigation, relatively less focus has been placed on vehicle speed control, which plays a critical role in ensuring safe and efficient operation, especially in complex and dynamic road environments. This paper addresses the challenge of speed tracking by proposing a genetic algorithm-based optimization method for PID controller parameters. Traditional PID controllers often struggle with maintaining accuracy and response time in highly variable conditions, but by optimizing these parameters through the genetic algorithm, substantial improvements in speed control precision and adaptability can be achieved, enhancing the vehicle’s ability to navigate real-world driving scenarios with greater stability. The experimental results clearly indicate that the autonomous vehicle, after PID parameter optimization using a genetic algorithm, demonstrated the following speed tracking errors: when the road surface adhesion coefficient was 0.5, the maximum speed tracking error was 0.22, the average value was 0.063, and the standard deviation was 0.124; when the adhesion coefficient was 0.6, the maximum speed tracking error was 0.180, the average value was 0.056, and the standard deviation was 0.099; when the adhesion coefficient was 0.8, the maximum speed tracking error was 0.179, the average value was 0.056, and the standard deviation was 0.098. This method significantly improved the controller’s performance in maintaining the desired speed, even under challenging conditions. These findings highlight the potential of genetic algorithms in supporting the future development of autonomous driving technology, ensuring its successful integration into intelligent transportation systems. Full article
(This article belongs to the Special Issue Intelligent Robot: Design, Control and Optimization)
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