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Applied Sciences
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State-of-the-Art of Intelligent Unmanned Systems
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State-of-the-Art of Intelligent Unmanned Systems

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 2022) | Viewed by 2483

Special Issue Editors

Dr. Chengchao Bai

E-Mail Website
Guest Editor
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: perception and decision-making
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Quanbo Ge

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: engineering information fusion theory and methods; collaborative optimization of unmanned systems; intelligent evaluation of man-machine hybrid systems; big data analysis of smart grids
Prof. Dr. Hongbo Gao

E-Mail Website
Guest Editor
Department of Automation, University of Science and Technology of China, Hefei 230052, China
Interests: intelligent driving vehicles; machine learning; deep learning; autonomous decision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ming He

E-Mail Website
Guest Editor
School of Command and Control Engineering, Army Engineering University of the PLA, Nanjing 210042, China
Interests: swarm intelligence; UAV swarm; swarm control and coordination; unmanned combat; unmanned command and control

Special Issue Information

Dear Colleagues,

As the application of robots, unmanned aerial vehicles, unmanned vehicles, and other unmanned systems expands, the requirements for their level of intelligence progressively increase. At the same time, intelligent unmanned systems are at the forefront of a new generation of artificial intelligence. This Special Issue, a collaboration between Applied Science and the IEEE International Conference on Unmanned Systems (ICUS), will discuss recent findings and best practices in the fields of unmanned systems, robotics, automation, and intelligent systems. Topics of interest include algorithms, devices, and systems of intelligent unmanned systems; the intelligent control, motion planning, mechanical design, and communications engineering of unmanned systems; and intelligent unmanned system technology applications in robotics and automation, aerospace science and engineering, marine science and engineering, and transportation and future mobility.

Dr. Chengchao Bai
Prof. Dr. Quanbo Ge
Prof. Dr. Hongbo Gao
Prof. Dr. Ming He
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

  • unmanned system
  • artificial intelligence
  • machine learning
  • motion planning
  • mechanical design
  • communications engineering
  • robotics and automation
  • aerospace science and engineering
  • marine science and engineering
  • transportation and future mobility

Published Papers (1 paper)

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Research

19 pages, 13214 KiB  
Article
A Multi-Stage Deep Reinforcement Learning with Search-Based Optimization for Air–Ground Unmanned System Navigation
by Xiaohui Chen, Yuhua Qi, Yizhen Yin, Yidong Chen, Li Liu and Hongbo Chen
Appl. Sci. 2023, 13(4), 2244; https://doi.org/10.3390/app13042244 - 9 Feb 2023
Cited by 7 | Viewed by 1990
Abstract
An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is essential for them to accomplish various tasks in unknown environments. This paper proposes an end-to-end framework for solving air–ground unmanned system navigation using deep reinforcement learning (DRL) while optimizing by [...] Read more.
An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is essential for them to accomplish various tasks in unknown environments. This paper proposes an end-to-end framework for solving air–ground unmanned system navigation using deep reinforcement learning (DRL) while optimizing by using a priori information from search-based path planning methods, which we call search-based optimizing DRL (SO-DRL) for the air–ground unmanned system. SO-DRL enables agents, i.e., an unmanned aerial vehicle (UAV) or an unmanned ground vehicle (UGV) to move to a given target in a completely unknown environment using only Lidar, without additional mapping or global planning. Our framework is equipped with Deep Deterministic Policy Gradient (DDPG), an actor–critic-based reinforcement learning algorithm, to input the agents’ state and laser scan measurements into the network and map them to continuous motion control. SO-DRL draws on current excellent search-based algorithms to demonstrate path planning and calculate rewards for its behavior. The demonstrated strategies are replayed in an experienced pool along with the autonomously trained strategies according to their priority. We use a multi-stage training approach based on course learning to train SO-DRL on the 3D simulator Gazebo and verify the robustness and success of the algorithm using new test environments for path planning in unknown environments. The experimental results show that SO-DRL can achieve faster algorithm convergence and a higher success rate. We piggybacked SO-DRL directly onto a real air–ground unmanned system, and SO-DRL can guide a UAV or UGV for navigation without adjusting any networks. Full article
(This article belongs to the Special Issue State-of-the-Art of Intelligent Unmanned Systems)
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