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Drones
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Unmanned Surface Vehicle
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Unmanned Surface Vehicle

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 8281

Special Issue Editor

Prof. Dr. Yanchao Sun

E-Mail Website
Guest Editor
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 15001, China
Interests: intelligent control; marine robot; formation system; distributed control technology; ship and ocean engineering; modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, unmanned surface vessels (USVs) have been developed and widely applied as a tool for human use.  USVs can replace human's personal operation and effectively complete a series of tasks on the water surface, such as marine environment monitoring, marine patrol and exploration, and so on. The marine environment is very complex. In order to ensure that USVs can navigate as required to complete relevant tasks, a variety of technologies, such as path planning technology, trajectory tracking control technology, and navigation and positioning technology, need to be applied to enhance their autonomous ability and level. In this context, we invite papers focusing on the current progress in the field of USV to this Special Issue. Papers in all fields directly related to these topics include, but are not limited to:

  • Mathematical modelling and analysis of USV systems;
  • Motion control technology of USV;
  • Path planning and control technology of USV;
  • Trajectory tracking control technology of USV;
  • Navigation and positioning technology of USV;
  • Formation control technology of multi-USV;
  • Control strategy of heterogeneous USV formation.

Dr. Yanchao Sun
Guest Editor

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. Drones is an international peer-reviewed open access monthly 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 2600 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

  • USV
  • mathematical modeling
  • motion control
  • path planning
  • navigation and positioning
  • formation control
  • heterogeneous system
  • simulation

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Published Papers (3 papers)

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Research

18 pages, 9741 KiB  
Article
Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter
by Jincheng Wang, Lei Xia, Lei Peng, Huiyun Li and Yunduan Cui
Drones 2023, 7(4), 228; https://doi.org/10.3390/drones7040228 - 24 Mar 2023
Cited by 2 | Viewed by 2112
Abstract
This article tackles the computational burden of propagating uncertainties in the model predictive controller-based policy of the probabilistic model-based reinforcement learning (MBRL) system for an unmanned surface vehicles system (USV). We proposed filtered probabilistic model predictive control using the unscented Kalman filter (FPMPC-UKF) [...] Read more.
This article tackles the computational burden of propagating uncertainties in the model predictive controller-based policy of the probabilistic model-based reinforcement learning (MBRL) system for an unmanned surface vehicles system (USV). We proposed filtered probabilistic model predictive control using the unscented Kalman filter (FPMPC-UKF) that introduces the unscented Kalman filter (UKF) for a more efficient uncertainty propagation in MBRL. A USV control system based on FPMPC-UKF is developed and evaluated by position-keeping and target-reaching tasks in a real USV data-driven simulation. The experimental results demonstrate a significant superiority of the proposed method in balancing the control performance and computational burdens under different levels of disturbances compared with the related works of USV, and therefore indicate its potential in more challenging USV scenarios with limited computational resources. Full article
(This article belongs to the Special Issue Unmanned Surface Vehicle)
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22 pages, 4102 KiB  
Article
A Path-Planning Method Considering Environmental Disturbance Based on VPF-RRT*
by Zhihao Chen, Jiabin Yu, Zhiyao Zhao, Xiaoyi Wang and Yang Chen
Drones 2023, 7(2), 145; https://doi.org/10.3390/drones7020145 - 20 Feb 2023
Cited by 9 | Viewed by 2636
Abstract
In the traditional rapidly exploring random tree (RRT) algorithm, the planned path is not smooth, the distance is long, and the fault tolerance rate of the planned path is low. Disturbances in an environment can cause unmanned surface vessels (USVs) to deviate from [...] Read more.
In the traditional rapidly exploring random tree (RRT) algorithm, the planned path is not smooth, the distance is long, and the fault tolerance rate of the planned path is low. Disturbances in an environment can cause unmanned surface vessels (USVs) to deviate from their planned path during navigation. Therefore, this paper proposed a path-planning method considering environmental disturbance based on virtual potential field RRT* (VPF-RRT*). First, on the basis of the RRT* algorithm, a VPF-RRT* algorithm is proposed for planning the planning path. Second, an anti-environmental disturbance method based on a deep recurrent neural networks PI (DRNN-PI) controller is proposed to allow the USV to eliminate environmental disturbance and maintain its track along the planning path. Comparative simulation experiments between the proposed algorithm and the other algorithms were conducted within two different experimental scenes. In the path-planning simulation experiment, the VPF-RRT* algorithm had a shorter planning path and a smaller total turning angle when compared to the RRT* algorithm. In the path-tracking simulation experiment, when using the proposed algorithm, the USV could effectively compensate for the impact of environmental disturbance and maintain its navigation along the planning path. In order to avoid the contingency of the experiment and verify the effectiveness and generality of the proposed algorithm, three experiments were conducted. The simulation results verify the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Unmanned Surface Vehicle)
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19 pages, 4121 KiB  
Article
Constrained Predictive Tracking Control for Unmanned Hexapod Robot with Tripod Gait
by Yong Gao, Dongliang Wang, Wu Wei, Qiuda Yu, Xiongding Liu and Yuhai Wei
Drones 2022, 6(9), 246; https://doi.org/10.3390/drones6090246 - 9 Sep 2022
Cited by 8 | Viewed by 2468
Abstract
Since it is difficult to accurately track reference trajectories under the condition of stride constraints for an unmanned hexapod robot moving with rhythmic gait, an omnidirectional tracking strategy based on model predictive control and real-time replanning is proposed in this paper. Firstly, according [...] Read more.
Since it is difficult to accurately track reference trajectories under the condition of stride constraints for an unmanned hexapod robot moving with rhythmic gait, an omnidirectional tracking strategy based on model predictive control and real-time replanning is proposed in this paper. Firstly, according to the characteristic that the stride dominates the rhythmic motion of an unmanned multi-legged robot, a body-level omnidirectional tracking model is established. Secondly, a quantification method of limb’s stretch and yaw constraints described by motion stride relying on a tripod gait is proposed, and then, a body-level accurate tracking controller based on constrained predictive control is designed. Then, in view of the low tracking efficiency of the robot under the guidance of common reference stride, a solution strategy of variable stride period and a real-time replanning scheme of reference stride are proposed based on the limb constraints and the integral mean, which effectively avoid the tracking deviation caused by the guidance of constant reference strides. Finally, the effectiveness and practicability of the proposed control strategy are demonstrated through the comparative analysis and simulation test of a hexapod robot WelCH with omnidirectional movement ability to continuously track the directed curve and the undirected polyline trajectory. Full article
(This article belongs to the Special Issue Unmanned Surface Vehicle)
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