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Processes
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Intelligent Techniques Used for Robotics
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Intelligent Techniques Used for Robotics

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 9916

Special Issue Editors

Dr. Tao Zhao

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: intelligent control; machine learning; advanced control; intelligent robot
Prof. Dr. Xiangpeng Xie

E-Mail Website
Guest Editor
Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: fuzzy modeling; fuzzy control and filtering; networked control system; industrial automation
Prof. Dr. Songyi Dian

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: advanced control; intelligent perception; intelligent robots

Special Issue Information

Dear Colleagues,

Intelligent technologies include machine learning, fuzzy logic systems, neural networks, reinforcement learning, evolutionary algorithms, etc. In recent years, these intelligent technologies have been widely applied to various robots, such as industrial robots, medical robots, special robots, entertainment robots, etc. However, in order to adapt to the rapid development of robots, engineers have higher and higher requirements for intelligent technologies, including reliability, efficiency, autonomy, perception, controllability, etc. Although intelligent robots have developed rapidly in the past several decades, there are still some new technologies and application problems that need to be solved. Therefore, it is urgently needed for innovative intelligent algorithms, advanced modeling strategies, practical intelligent robot technologies, etc.

The main objective of this Special Issue is, through scientific researchers and technical engineers, to introduce the latest studies in the field of intelligent technologies for robotics, including intelligent algorithms, robotic systems, human–machine collaboration, etc. Furthermore, intelligent solutions for robotic engineering and future research prospects will also be provided. Authors are encouraged to submit their original contributions, and there should be intelligent technology in the robotic systems. Potential topics include, but are not limited to, the following:

  • Intelligent control algorithms applied to robots (fuzzy control, neural network control, reinforcement learning control, etc.).
  • Artificial intelligence technologies applied to robots (cognitive mechanisms, machine vision, machine hearing, pattern recognition, machine reasoning, intelligent decision-making, etc.).
  • Examples of intelligent technologies applied to various robots (industrial robots, medical robots, special robots, entertainment robots, etc.).
  • Practical application examples of intelligent robots in automatic production processes (chemistry, biology, materials, energy, environment, food, pharmacy, manufacturing, etc.).

Dr. Tao Zhao
Prof. Dr. Xiangpeng Xie
Prof. Dr. Songyi Dian
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. Processes 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 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

  • fuzzy logic systems
  • neural networks
  • reinforcement learning
  • evolutionary algorithms

Published Papers (5 papers)

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Research

27 pages, 20265 KiB  
Article
Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR
by Yong Zhang, Feng Gao and Fengkui Zhao
Processes 2023, 11(6), 1841; https://doi.org/10.3390/pr11061841 - 19 Jun 2023
Cited by 7 | Viewed by 2992
Abstract
Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding [...] Read more.
Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink–Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework. Full article
(This article belongs to the Special Issue Intelligent Techniques Used for Robotics)
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18 pages, 3406 KiB  
Article
Ground Risk Assessment for Unmanned Aircraft Focusing on Multiple Risk Sources in Urban Environments
by Qiyang Li, Qinggang Wu, Haiyan Tu, Jianping Zhang, Xiang Zou and Shan Huang
Processes 2023, 11(2), 542; https://doi.org/10.3390/pr11020542 - 10 Feb 2023
Cited by 3 | Viewed by 1670
Abstract
This paper investigates the risk quantification for Unmanned Aircraft (UA) in urban environments, focusing on the safety of ground people. An assessment methodology is proposed to quantify the flying risk, which indicates the ground fatalities resulted from different potential risk sources. With the [...] Read more.
This paper investigates the risk quantification for Unmanned Aircraft (UA) in urban environments, focusing on the safety of ground people. An assessment methodology is proposed to quantify the flying risk, which indicates the ground fatalities resulted from different potential risk sources. With the knowledge of UA’s specifications and ground environments, the flying risk of the target UA flying in the target area could be evaluated from the combination of results from independent assessment procedures focusing on multiple potential risk sources with specific safety metrics. A study case to assess the flying risk of the Talon and the DJI Inspire 2 flying in one piece of the region in Chengdu is presented. From the assessment result, the airspace management strategies for both Talon and DJI Inspires 2 could be easily developed to guarantee the safety of ground people, therefore, this risk quantification method could be a general tool to support decision-making in safety work. Full article
(This article belongs to the Special Issue Intelligent Techniques Used for Robotics)
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23 pages, 3600 KiB  
Article
S-Velocity Profile of Industrial Robot Based on NURBS Curve and Slerp Interpolation
by Guirong Wang, Fei Xu, Kun Zhou and Zhihui Pang
Processes 2022, 10(11), 2195; https://doi.org/10.3390/pr10112195 - 26 Oct 2022
Cited by 4 | Viewed by 1518
Abstract
This paper presents a novel algorithm for industrial robot trajectory planning based on the NURBS(Non-Uniform Rational B-Spline) curve and Slerp interpolation aiming at the problems that the trajectory of a six-axis industrial robot is not smooth enough in the operation process, the posture [...] Read more.
This paper presents a novel algorithm for industrial robot trajectory planning based on the NURBS(Non-Uniform Rational B-Spline) curve and Slerp interpolation aiming at the problems that the trajectory of a six-axis industrial robot is not smooth enough in the operation process, the posture planning process is non-uniform, and the six-axis industrial robot starts and stops frequently. Firstly, aiming at the first problem, the trajectory planning algorithm based on the NURBS curve is presented to improve the smoothness of the trajectory curve. Combined with Slerp posture planning based on quaternion description, which realizes the uniform change of posture on the robot’s end-effector. Secondly, aiming at the second problem, the S-velocity planning algorithm is presented in the interpolation interval of the robot, which realizes the operation process of complex curves continuously, and improves the operation quality. Finally, this paper uses Bernoulli’s lemniscate as the incentive trajectory, and the contrast experiment of trajectory planning between two incentive profiles is designed, which are the NURBS curve and the five-order polynomial curve. The result of the experiment indicates that the planning algorithm proposed in this paper could effectively improve the smoothness of trajectory in a Cartesian workspace, decrease the impact and tremulous in a Cartesian workspace, and effectively improve the performance of the robot working process. The results drawn from this paper lay a certain foundation for the future high-precision control of industrial robots. Full article
(This article belongs to the Special Issue Intelligent Techniques Used for Robotics)
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17 pages, 1154 KiB  
Article
A T-S Fuzzy Quaternion-Value Neural Network-Based Data-Driven Generalized Predictive Control Scheme for Mecanum Mobile Robot
by Congjun Ma, Xiaoying Li, Guofei Xiang and Songyi Dian
Processes 2022, 10(10), 1964; https://doi.org/10.3390/pr10101964 - 29 Sep 2022
Cited by 3 | Viewed by 1443
Abstract
Four-mecanum-wheeled omnidirectional mobile robots (FMOMR) are widely used in many practical scenarios because of their high mobility and flexibility. However, the performance of trajectory tracking would be degenerated largely due to various reasons. To deal with this issue, this paper proposes a data-driven [...] Read more.
Four-mecanum-wheeled omnidirectional mobile robots (FMOMR) are widely used in many practical scenarios because of their high mobility and flexibility. However, the performance of trajectory tracking would be degenerated largely due to various reasons. To deal with this issue, this paper proposes a data-driven algorithm by using the T-S fuzzy quaternion-value neural network (TSFQVNN). TSFQVNN is tailored to obtain the controlled autoregressive integral moving average (CARIMA) model, and then the generalized predictive controller (GPC) is designed based on the CARIMA model. In this way, the spatial relationship between the three-dimensional pose coordinates can be preserved and training times can be reduced. Furthermore, the convergence of the proposed algorithm is verified by the Stone–Weierstrass theorem, and the convergence conditions of the algorithm are discussed. Finally, the proposed control scheme is applied to the three-dimensional (3D) trajectory tracking problem on the arc surface, and the simulation results prove the necessity and feasibility of the algorithm. Full article
(This article belongs to the Special Issue Intelligent Techniques Used for Robotics)
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17 pages, 3430 KiB  
Article
Motion Planning of an Inchworm Robot Based on Improved Adaptive PSO
by Binrui Wang, Jianxin Wang, Zhenhai Huang, Weiyi Zhou, Xiaofei Zheng and Shunan Qi
Processes 2022, 10(9), 1675; https://doi.org/10.3390/pr10091675 - 23 Aug 2022
Cited by 4 | Viewed by 1334
Abstract
Focusing on the motion energy consumption of a self-developed inchworm robot’s peristaltic gait, based on the “error tracking” of cubic polynomial programming in Cartesian space and seventh polynomial programming in joint space, we propose an optimal motion planning method of energy consumption considering [...] Read more.
Focusing on the motion energy consumption of a self-developed inchworm robot’s peristaltic gait, based on the “error tracking” of cubic polynomial programming in Cartesian space and seventh polynomial programming in joint space, we propose an optimal motion planning method of energy consumption considering both kinematic and dynamic constraints. Firstly, we offer a mathematical description of the energy consumption and space curve similarity operator. Secondly, we describe the mathematical models of the robot trajectory and path that were established in terms of their dynamics and kinematics. Then, we propose a motion planning method based on improved adaptive particle swarm optimization (PSO) to accelerate the convergence speed of the algorithm and ensure the accuracy of the model calculation. Finally, we outline the simulation test carried out to measure the inchworm-like robot’s creeping gait. The results show that the motion path obtained by using the planning method proposed in this paper is the one with the least energy consumption by the robot among all the comparison paths. Moreover, compared with other algorithms, it was found that the result obtained by using the algorithm proposed in this paper is the one with the shortest solution time and the lowest energy consumption under the same iteration times. The calculation results verify the feasibility and effectiveness of the planning method. Full article
(This article belongs to the Special Issue Intelligent Techniques Used for Robotics)
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