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Applied Sciences
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Research and Development of Intelligent Robot
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Research and Development of Intelligent Robot

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 25 August 2024 | Viewed by 6208

Special Issue Editors

Dr. Ruiya Li

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: optical fiber sensing; robotic assembly/disassembly; specialized robot
Prof. Dr. Jun Huang

E-Mail Website
Guest Editor
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: intelligent remanufacturing technology; robotics and automation; human-machine collaboration; optical fiber sensing and intelligent sensing technology; mechanical equipment condition monitoring and fault diagnosis

Special Issue Information

Dear Colleagues,

With the development of enabling technologies such as advanced machine vision and artificial intelligence, modern robots are getting smarter and more dexterous. Whether it is to serve industrial production or special social tasks, or to serve human daily life, researchers are constantly pursuing higher-degree intelligence in robots. The development of intelligent robots is a system engineering, involving advanced mechanical design, multi-body dynamics, advanced sensing, advanced drive, intelligent control, and so on.

This Special Issue provides an opportunity for researchers to present new ideas and experimental results in the field of intelligent robots. The areas relevant to this issue include, but are not limited to, new insights in kinematics and dynamics models and the sensing, perception, decision making, control, interaction, and collaboration of robots.

We are seeking high-quality and innovative research and review papers that cover, but are not limited to, the following topics:

  • Advanced and intelligent sensing technology for robots;
  • Advanced machine vision;
  • Intelligent driver mechanism;
  • Advanced robot dynamics theory;
  • Advanced and intelligent control methods;
  • Flexible robotic joints;
  • Variable stiffness continuum robot;
  • Quadruped robot;
  • Intelligent soft robot;
  • Artificial intelligence;
  • Machine learning;
  • Swarm intelligence;
  • Robot environment interaction;
  • Human–robot collaboration.

Dr. Ruiya Li
Prof. Dr. Jun Huang
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

  • robotics
  • artificial intelligence
  • machine learning
  • advanced and intelligent control
  • machine vision
  • intelligent sensing
  • collaborative robot
  • human robot interaction and collaboration

Published Papers (6 papers)

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Research

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23 pages, 5748 KiB  
Article
Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique
by Xianyan Li, Dongdong Zheng, Kai Guo and Xuemei Ren
Appl. Sci. 2024, 14(10), 4030; https://doi.org/10.3390/app14104030 - 9 May 2024
Viewed by 312
Abstract
This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the [...] Read more.
This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes. Full article
(This article belongs to the Special Issue Research and Development of Intelligent Robot)
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14 pages, 2602 KiB  
Article
Dynamic Modeling and Altitude Control for Flying Cars Based on Active Disturbance Rejection Control
by Jie Xu, Xinjiang Lu, Wei Luo, Hao Sun, Zhenkun Long and Yuteng Xu
Appl. Sci. 2024, 14(7), 2754; https://doi.org/10.3390/app14072754 - 25 Mar 2024
Viewed by 474
Abstract
Flying cars offer huge advantages due to their deformable structure, which can adapt to external environments and mission requirements. They represent a novel system that can realize vertical takeoff and landing. However, the structure of a flying car is complicated, placing higher requirements [...] Read more.
Flying cars offer huge advantages due to their deformable structure, which can adapt to external environments and mission requirements. They represent a novel system that can realize vertical takeoff and landing. However, the structure of a flying car is complicated, placing higher requirements on modeling accuracy and control effectiveness. Thus, in this paper, a dynamic model of a flying car is proposed by combining a car body, motor, and propellers. Then, a double-loop controller based on active disturbance rejection control is proposed to accurately control its flight altitude. Utilizing the extended state observer, external wind and other disturbances are regarded as an extended state, which can be dynamically observed and compensated to significantly improve tracking accuracy. The effectiveness of the proposed controller is validated through detailed simulations and flight experiments. The proposed controller significantly improves control accuracy and disturbance rejection capability. Full article
(This article belongs to the Special Issue Research and Development of Intelligent Robot)
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16 pages, 8873 KiB  
Article
New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
by Ahmad Elwaly, A. Abdellatif and Y. El-Shaer
Appl. Sci. 2024, 14(6), 2374; https://doi.org/10.3390/app14062374 - 12 Mar 2024
Viewed by 840
Abstract
The rapid growth of the elderly population has led to an increased demand for effective and personalized eldercare solutions. In this paper, the design and development of an eldercare robot is presented. This robot is specifically tailored to meet the two specific challenges [...] Read more.
The rapid growth of the elderly population has led to an increased demand for effective and personalized eldercare solutions. In this paper, the design and development of an eldercare robot is presented. This robot is specifically tailored to meet the two specific challenges faced by the elderly. The first is the continuous indoor tracking of the elder, while the second is the fall detection. A comprehensive overview of the hardware and software components, as well as the control architecture of the robot is presented. The hardware design of the robot incorporates a range of features, including a perception system comprising a 2D Lidar, IMU, and camera for environment mapping, localization, and fall detection. The software stack of the robot is explained as consisting of layers for perception, mapping, and localization. The robot is tested experimentally to validate its path planning capability by using Hector SLAM and the RRT* technique. Experimental path planning has shown a positioning accuracy of 93.8% on average. Elderly fall detection is achieved by using the YOLOv7 algorithm at a percentage of 96%. Experimental results have been discussed and evaluated. Full article
(This article belongs to the Special Issue Research and Development of Intelligent Robot)
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15 pages, 4821 KiB  
Article
Trajectory Tracking Control of a Skid-Steer Mobile Robot Based on Nonlinear Model Predictive Control with a Hydraulic Motor Velocity Mapping
by Jian Wang, Zhen Liu, Hongqiang Chen, Yi Zhang, Daqing Zhang and Changfeng Peng
Appl. Sci. 2024, 14(1), 122; https://doi.org/10.3390/app14010122 - 22 Dec 2023
Cited by 3 | Viewed by 818
Abstract
In this study, we address the trajectory tracking control problem of a hydraulic-driven skid-steer mobile robot. A hierarchical control strategy is proposed to simultaneously consider the robot’s position control and the velocity control of the hydraulic motors. At the upper level, a nonlinear [...] Read more.
In this study, we address the trajectory tracking control problem of a hydraulic-driven skid-steer mobile robot. A hierarchical control strategy is proposed to simultaneously consider the robot’s position control and the velocity control of the hydraulic motors. At the upper level, a nonlinear model predictive control (NMPC) method is employed to control the position and heading of the mobile robot. The NMPC controller takes into account the robot’s physical constraints and generates the desired robot motion velocity. Then, to control the hydraulic drive system, a current–velocity mapping-based control method is introduced. By establishing the mapping relationship between the control current applied to the hydraulic motor and its corresponding output velocity, the dynamics of the hydraulic motors are characterized. Consequently, the lower-level controller can directly obtain the control signal for the hydraulic actuator through lookup mappings. Additionally, PID controllers are adopted to compensate for velocity tracking errors. The proposed hierarchical control strategy decouples the robot’s position control and the hydraulic system control, simplifying the overall controller design, leading to improved control performance. To validate the effectiveness of the proposed control strategy, several experiments were conducted on a hydraulic-driven skid-steer mobile robot, and the results demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Research and Development of Intelligent Robot)
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13 pages, 4396 KiB  
Article
Knowledge Graph Construction of End-of-Life Electric Vehicle Batteries for Robotic Disassembly
by Jiangbiao Wang, Jun Huang and Ruiya Li
Appl. Sci. 2023, 13(24), 13153; https://doi.org/10.3390/app132413153 - 11 Dec 2023
Viewed by 1270
Abstract
End-of-life (EoL) electric vehicle (EV) batteries are one of the main fountainheads for recycling rare metal elements like cobalt and lithium. Disassembly is the first step in carrying out a higher level of recycling and processing of EV batteries. This paper presents a [...] Read more.
End-of-life (EoL) electric vehicle (EV) batteries are one of the main fountainheads for recycling rare metal elements like cobalt and lithium. Disassembly is the first step in carrying out a higher level of recycling and processing of EV batteries. This paper presents a knowledge graph of electric vehicle batteries for robotic disassembly. The information extraction of the EV batteries was conducted based on the source data of EV batteries. The semantic ontology structure and the knowledge graph of the EV batteries were constructed. A case study was designed to demonstrate the proposed knowledge graph. The study involved generating a robotic disassembly sequence planning for an EoL EV battery. The results show the feasibility of the constructed knowledge graph. Full article
(This article belongs to the Special Issue Research and Development of Intelligent Robot)
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Review

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24 pages, 2968 KiB  
Review
A Survey on Robot Semantic Navigation Systems for Indoor Environments
by Raghad Alqobali, Maha Alshmrani, Reem Alnasser, Asrar Rashidi, Tareq Alhmiedat and Osama Moh’d Alia
Appl. Sci. 2024, 14(1), 89; https://doi.org/10.3390/app14010089 - 21 Dec 2023
Cited by 4 | Viewed by 1600
Abstract
Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave [...] Read more.
Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave radars to construct an environment map. However, in robot navigation, scene understanding has become essential for comprehending the area of interest and achieving improved navigation results. The semantic model of the indoor environment provides the robot with a representation that is closer to human perception, thereby enhancing the navigation task and human–robot interaction. However, semantic navigation systems require the utilization of multiple components, including geometry-based and vision-based systems. This paper presents a comprehensive review and critical analysis of recently developed robot semantic navigation systems in the context of their applications for semantic robot navigation in indoor environments. Additionally, we propose a set of evaluation metrics that can be considered to assess the efficiency of any robot semantic navigation system. Full article
(This article belongs to the Special Issue Research and Development of Intelligent Robot)
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