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Artificial Intelligence Techniques and Robotic Control Systems
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Artificial Intelligence Techniques and Robotic Control 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 (20 December 2022) | Viewed by 10055

Special Issue Editors

Prof. Dr. Hongli Xu

E-Mail Website
Guest Editor
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: cooperative control of multiple underwater vehicles; cluster control of multiple robot systems
Dr. Qichuan Ding

E-Mail Website
Guest Editor
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: robot intelligent control; natural human–computer interaction; computer vision
Dr. Junyi Wang

E-Mail Website
Guest Editor
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: neural networks; machine learning; deep learning
Dr. Hao Wang

E-Mail Website
Guest Editor
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: robot motion control

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to artificial intelligence techniques and robotic control systems.

Robotic control systems are of great significance to improve innovation ability and promote the global economy development. The applications of artificial intelligence include machine vision, voice interaction, intelligent robot and path planning, etc. With the development of artificial intelligence technology, artificial intelligence technology has been widely used in robot control systems. Different from traditional robot systems, intelligent robot control systems based on artificial intelligence technology could better serve humans and society and are attracting increasing interest from the research community. Thus, this Special Issue will serve as an essential and timely venue for sharing research advances in this field and should be of interest to potential readers.

In this Special Issue, the list of possible submissions includes but is not limited to:

  • Multi-robot coordinated control;
  • Robot path planning based on artificial intelligence techniques;
  • Multi-agent system distributed intelligent control;
  • Human-following algorithm for mobile robots; 
  • Natural human computer interaction based on robot systems;
  • Robot arm grasping based on vision;
  • Cooperative control of multiple underwater vehicles;
  • Underwater object recognition of AUV.

Both theoretical and experimental studies are welcome.

Prof. Dr. Hongli Xu
Dr. Qichuan Ding
Dr. Junyi Wang
Dr. Hao 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.

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Published Papers (4 papers)

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Research

11 pages, 3063 KiB  
Article
A Random Sampling-Based Method via Gaussian Process for Motion Planning in Dynamic Environments
by Jing Xu, Jinghui Qiao, Xu Han, Yu He, Hongkun Tian and Zhe Wei
Appl. Sci. 2022, 12(24), 12646; https://doi.org/10.3390/app122412646 - 9 Dec 2022
Cited by 1 | Viewed by 1449
Abstract
Motion planning is widely applied to industrial robots, medical robots, bionic robots, and smart vehicles. Most work environments of robots are not static, which leads to difficulties for robot motion planning. We present a dynamic Gaussian local planner (DGLP) method to solve motion [...] Read more.
Motion planning is widely applied to industrial robots, medical robots, bionic robots, and smart vehicles. Most work environments of robots are not static, which leads to difficulties for robot motion planning. We present a dynamic Gaussian local planner (DGLP) method to solve motion planning problems in dynamic environments. In a dynamic environment, dynamic obstacles sometimes make part of the global path invalid, so the local invalid path needs to be local re-planned online. Compared with the node sampling-based methods building large-scale random trees or roadmaps, the Gaussian random path sampling (GRPS) module integrated in the DGLP directly samples smooth random paths discretized into sparse nodes to improve the local path re-planning efficiency. We also provide the path end orientation constraint (PEOC) method for the local re-planning paths in order to merge them smoothly into the global paths. In the robot experiments, the average planning time of the DGLP is 0.04s, which is at least 92.31% faster than the test methods, and its comprehensive evaluation scores, which consider the consuming time, path quality, and success rate of local re-planning, are at least 44.92% higher than the test methods. The results demonstrate that the proposed DGLP method is able to efficiently provide high-quality local re-planning paths in dynamic environments. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques and Robotic Control Systems)
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14 pages, 5845 KiB  
Article
Research on the Visual Guidance System of Zoning Casting Grinding Based on Feature Points
by Minjian Zhu, Tao Shang, Zelin Jin, Chunshan Liu, Wenbin Deng and Yanli Chen
Appl. Sci. 2022, 12(17), 8771; https://doi.org/10.3390/app12178771 - 31 Aug 2022
Cited by 2 | Viewed by 1474
Abstract
Compared to traditional rough casting grinding (RCG), the individualization of castings is very different, which makes it difficult to realize the automation of casting grinding. At this stage, the primary method is manual grinding. In this study, the regional casting grinding system based [...] Read more.
Compared to traditional rough casting grinding (RCG), the individualization of castings is very different, which makes it difficult to realize the automation of casting grinding. At this stage, the primary method is manual grinding. In this study, the regional casting grinding system based on feature points is adopted to achieve the personalized grinding of castings and improve the grinding efficiency and the automation level of the manufacturing process. After preprocessing the point cloud, the fast point feature histogram (FPFH) descriptor is used to describe the features of each region and construct the local template. The position of the local region is obtained by template matching. The random sample consensus (RANSAC) algorithm is used to calculate the plane and fit the point cloud to obtain the contact point trajectory of the grinding head. Then, according to different polishing methods, different polishing poses are generated. The simulation experimental results show that the system has good adaptability, and the consistency of finished products is good. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques and Robotic Control Systems)
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17 pages, 4913 KiB  
Article
UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
by Yanli Chen, Qiushi Li, Shenghua Gong, Jun Liu and Wenxue Guan
Appl. Sci. 2022, 12(12), 5918; https://doi.org/10.3390/app12125918 - 10 Jun 2022
Cited by 4 | Viewed by 2856
Abstract
With the increasing demand for underwater resource exploration, three-dimensional (3D) reconstruction technology is important for searching for lost underwater civilizations, underwater shipwrecks, or seabed structures. However, faced with the limitations of underwater unmanned systems in terms of energy, bandwidth, and transmission delay, 3D [...] Read more.
With the increasing demand for underwater resource exploration, three-dimensional (3D) reconstruction technology is important for searching for lost underwater civilizations, underwater shipwrecks, or seabed structures. However, faced with the limitations of underwater unmanned systems in terms of energy, bandwidth, and transmission delay, 3D reconstruction technology based on video streams as direct data will not work well. We propose a terminal image processing strategy to save data transmission time and cost and to obtain 3D scene information as soon as possible. Firstly, we propose an adaptive threshold key frame extraction algorithm based on clustering, which extracts key frames from the video stream as structure from motion (SFM) image sequences. On this basis, we enhance the underwater images with sufficient and insufficient illumination to improve the image quality and obtain a better visual effect in the 3D reconstruction step. Additionally, we choose global SFM to construct the scene and propose a faster rotation averaging method, the least trimmed square rotation averaging (LTS-RA) method, based on the least trimmed squares (LTS) and L1RA methods. It is proven to reduce 19.97% of the time through experiments. Finally, our experiments demonstrate that the dense point cloud saves about 70% of the transmission cost compared to video streaming. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques and Robotic Control Systems)
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28 pages, 10145 KiB  
Article
IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain
by Jinyan Yao, Yongbai Sha, Yanli Chen, Guoqing Zhang, Xinyu Hu, Guiqiang Bai and Jun Liu
Appl. Sci. 2022, 12(11), 5634; https://doi.org/10.3390/app12115634 - 1 Jun 2022
Cited by 29 | Viewed by 2839
Abstract
In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader [...] Read more.
In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques and Robotic Control Systems)
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