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Electronics
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Intelligent Mobile Robotic Systems: Decision, Planning and Control
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Intelligent Mobile Robotic Systems: Decision, Planning and Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 3201

Special Issue Editors

Dr. Dawei Gong

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: fuzzy control; robotics; neural network control; visual serving
Dr. Bonan Huang

E-Mail Website
Guest Editor
Department of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: multi-agent systems; cooperative control; distributed optimization; robotics control; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Deng

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: nonlinear control; time-delay systems; robotics
Dr. Minglei Zhu

E-Mail
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: robotics; neural network control; visual serving

Special Issue Information

Dear Colleagues,

Due to the urgent requirements of environmental exploration, transportation, service industry, and military application, it is crucial to develop intelligent mobile robots to replace humans in completing dangerous tasks and improve efficiency. To attain the objective mentioned above, mobile robots must have the abilities of intelligent decision-making, safe motion planning, and accurate motion control. This session will exhibit and discuss the latest research in advanced decision-making, planning, and control technologies for mobile robots (including, but not limited to, wheeled robots, legged robots, flying robots, underwater robots, etc.) in order to improve the reliability, adaptability, and manoeuvrability of such robots. This session aims to encourage researchers to share new ideas and new methods for enhancing and exploring the potential of mobile robots.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Advanced decision and embodied AI technologies;
  • Fast trajectory planning and collision avoidance for mobile robots;
  • Robust state estimation and filtering for mobile robots;
  • Motion control in an unstructured environment;
  • Learning-based motion control technologies;
  • Human–robot interaction;
  • Other related issues.

Dr. Dawei Gong
Dr. Bonan Huang
Dr. Yang Deng
Dr. Minglei Zhu
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. Electronics 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

  • intelligent mobile robot
  • path planning
  • intelligent control

Published Papers (5 papers)

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Research

17 pages, 3332 KiB  
Article
Trajectory Tracking Control of Car-like Mobile Robots Based on Extended State Observer and Backstepping Control
by Changfu Zhu, Baoquan Li, Chenyang Zhao and Yixin Wang
Electronics 2024, 13(8), 1563; https://doi.org/10.3390/electronics13081563 - 19 Apr 2024
Viewed by 377
Abstract
In this paper, a trajectory tracking control strategy for low-speed car-like mobile robots (CLMRs) based on an extended state observer (ESO) and backstepping control is proposed to address the issue of trajectory tracking accuracy degradation caused by modeling errors and external disturbances. First, [...] Read more.
In this paper, a trajectory tracking control strategy for low-speed car-like mobile robots (CLMRs) based on an extended state observer (ESO) and backstepping control is proposed to address the issue of trajectory tracking accuracy degradation caused by modeling errors and external disturbances. First, modeling errors and external disturbances are introduced into an ideal kinematic model of a CLMR, and a set of output equations is utilized to split the coupled, underdriven disturbance kinematic model into two mutually independent subsystems. Next, disturbances in the subsystems are estimated based on a linear ESO, and the convergence of the proposed observer is proved by the Lyapunov method. Finally, a controller with disturbance compensation is designed using backstepping control to complete the trajectory tracking task of CLMRs. Simulation and experimental results show the effectiveness of the proposed control scheme. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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13 pages, 7236 KiB  
Article
Research on Radiation Damage and Reinforcement of Control and Sensing Systems in Nuclear Robots
by Yinlin Chang, Shuliang Zou, Guang Lin, Dewen Tang, Cuiyue Wei and Shoulong Xu
Electronics 2024, 13(7), 1214; https://doi.org/10.3390/electronics13071214 - 26 Mar 2024
Viewed by 375
Abstract
This study investigates the radiation damage and radiation reinforcement of the control and sensing systems of nuclear robots. Radiation experiments were conducted on key electronic devices to study their radiation resistance, and a shielding structure for radiation reinforcement was designed to meet the [...] Read more.
This study investigates the radiation damage and radiation reinforcement of the control and sensing systems of nuclear robots. Radiation experiments were conducted on key electronic devices to study their radiation resistance, and a shielding structure for radiation reinforcement was designed to meet the radiation resistance performance requirements of the system. The results show that at doses exceeding 1300 Gy, Hall sensors, pressure transducers, and temperature transducers exhibit radiation damage. At doses exceeding 170 Gy, transformers and controllers also show radiation damage. Lithium batteries remain largely unaffected, but packs experience voltage decline. When using Pb and W as shielding materials for Super MC simulation, it was found that at a thickness of 15 mm, the shielding efficiency of the controller and transformer under Pb shielding increased by approximately 84.99% and 52.00%, respectively, compared to 92.23% and 74.47% under W, which had the best shielding effect benefits. By adopting radiation-resistant shielding reinforcement, we can effectively improve the radiation resistance of the controller and transformer. This is crucial for ensuring the reliable operation of nuclear robots in high-radiation environments and providing important data and theoretical support for the development of related technologies. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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15 pages, 2990 KiB  
Article
Pedestrian Trajectory Prediction Based on Motion Pattern De-Perturbation Strategy
by Yingjian Deng, Li Zhang, Jie Chen, Yu Deng, Zhixiang Huang, Yingsong Li, Yice Cao, Zhongcheng Wu and Jun Zhang
Electronics 2024, 13(6), 1135; https://doi.org/10.3390/electronics13061135 - 20 Mar 2024
Viewed by 478
Abstract
Pedestrian trajectory prediction is extremely challenging due to the complex social attributes of pedestrians. Introducing latent vectors to model trajectory multimodality has become the latest mainstream solution idea. However, previous approaches have overlooked the effects of redundancy that arise from the introduction of [...] Read more.
Pedestrian trajectory prediction is extremely challenging due to the complex social attributes of pedestrians. Introducing latent vectors to model trajectory multimodality has become the latest mainstream solution idea. However, previous approaches have overlooked the effects of redundancy that arise from the introduction of latent vectors. Additionally, they often fail to consider the inherent interference of pedestrians with no trajectory history during model training. This results in the model’s inability to fully utilize the training data. Therefore, we propose a two-stage motion pattern de-perturbation strategy, which is a plug-and-play approach that introduces optimization features to model the redundancy effect caused by latent vectors, which helps to eliminate the redundancy effects in the trajectory prediction phase. We also propose loss masks to reduce the interference of invalid data during training to accurately model pedestrian motion patterns with strong physical interpretability. Our comparative experiments on the publicly available ETH and UCY pedestrian trajectory datasets, as well as the Stanford UAV dataset, show that our optimization strategy achieves better pedestrian trajectory prediction accuracies than a range of state-of-the-art baseline models; in particular, our optimization strategy effectively absorbs the training data to assist the baseline models in achieving optimal modeling accuracy. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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17 pages, 15546 KiB  
Article
A Pedestrian Trajectory Prediction Method for Generative Adversarial Networks Based on Scene Constraints
by Zhongli Ma, Ruojin An, Jiajia Liu, Yuyong Cui, Jun Qi, Yunlong Teng, Zhijun Sun, Juguang Li and Guoliang Zhang
Electronics 2024, 13(3), 628; https://doi.org/10.3390/electronics13030628 - 2 Feb 2024
Cited by 2 | Viewed by 717
Abstract
Pedestrian trajectory prediction is one of the most important topics to be researched for unmanned driving and intelligent mobile robots to perform perceptual interaction with the environment. To solve the problem of the SGAN (social generative adversarial networks) model lacking an understanding of [...] Read more.
Pedestrian trajectory prediction is one of the most important topics to be researched for unmanned driving and intelligent mobile robots to perform perceptual interaction with the environment. To solve the problem of the SGAN (social generative adversarial networks) model lacking an understanding of pedestrian interaction and scene constraints, this paper proposes a trajectory prediction method based on a scenario-constrained generative adversarial network. Firstly, a self-attention mechanism is added, which can integrate information at every moment. Secondly, mutual information is introduced to enhance the influence of latent code on the predicted trajectory. Finally, a new social pool is introduced into the original trajectory prediction model, and a scene edge extraction module is added to ensure the final output path of the model is within the passable area in line with the physical scene, which greatly improves the accuracy of trajectory prediction. Based on the CARLA (CAR Learning to Act) simulation platform, the improved model was tested on the public dataset and the self-built dataset. The experimental results showed that the average moving deviation was reduced by 26.4% and the final offset was reduced by 23.8%, which proved that the improved model could better solve the uncertainty of pedestrian turning decisions. The accuracy and stability of pedestrian trajectory prediction are improved while maintaining multiple modes. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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18 pages, 5872 KiB  
Article
Channel Switching Algorithms for a Robust Networked Control System with a Delay and Packet Errors
by Janghoon Yang
Electronics 2024, 13(2), 308; https://doi.org/10.3390/electronics13020308 - 10 Jan 2024
Viewed by 544
Abstract
Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear [...] Read more.
Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear Quadratic Gaussian (LQG) control in environments with multiple channels, delays, and packet errors, we propose channel-switching algorithms. Leveraging the encoder and decoder structure for channel modeling, we derive the decoder estimation error covariance matrix, characterizing LQG control performance with respect to delay. Based on this insight, we develop two threshold-based channel-switching algorithms, proven to ensure finite total decoder estimation error variance under certain conditions. Specific conditions are also identified where the proposed algorithms offer improved probabilistic stability. Numerical simulations confirm the superior performance of the proposed algorithms compared to conventional methods across diverse channel environments. Notably, the proposed algorithms demonstrate near-optimal performance in a practical operational scenario involving multiple channels, specifically 5G cellular link and Starlink. Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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