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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 15093

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Special Issue Editors

Prof. Dr. Tianhong Yan

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Guest Editor

School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Interests: autonomous system; robotics; high-speed and high-precision system

Prof. Dr. Bo He

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Guest Editor

School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China
Interests: system platform of advanced marine robots; marine sensors; towed sensor array equipment; robot target recognition; path planning and decision; underwater image processing; sonar signal analysis and information processing; electromagnetic compatibility and reliability of complex electronic systems
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Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to report on the theory and applications of all aspects of autonomous robotic systems. Thus, this Special Issue seeks to publish articles that deal with the theory, design, and applications of intelligence and autonomous robotics in any fields (e.g., space, sea/underwater, farming, and land), ranging from software to hardware technologies. The scope of this Special Issue includes but is not limited to the following:

Computational architectures and AI algorithms for autonomous systems;
Learning and adaptation in robots;
Manipulation and locomotion;
Motion planning and navigation;
Studies of autonomous robot systems;
Intelligent sensing systems (e.g., spectral, thermal, and other environmental parameters);
Long-term autonomous observation;
Sensing and perception;
Multi-agent systems and swarm robotics;
Situational awareness and safety in a co-robotic environment, etc.

This Special Issue aims to publish advances in robotics toward adaptation, autonomy, interaction, intelligence, manipulation, mobility, formation, and cooperation in unstructured environments. It is interested in distributed advances, as well as the development and maintenance of real-world intelligent autonomous robotic systems by multi-field and multidisciplinary teams of scientists and researchers.

Prof. Dr. Tianhong Yan
Prof. Dr. Bo He
Guest Editors

Manuscript Submission Information

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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

computational architectures and AI algorithms for autonomous systems
learning and adaptation in robots
manipulation and locomotion
motion planning and navigation
studies of autonomous robot systems
intelligent sensing systems (e.g., spectral, thermal, and other environmental parameters)
long-term autonomous observation
sensing and perception
multi-agent systems and swarm robotics
situational awareness and safety in a co-robotic environment

Published Papers (10 papers)

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Research

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19 pages, 10844 KiB

Open AccessArticle

Solid-State-LiDAR-Inertial-Visual Odometry and Mapping via Quadratic Motion Model and Reflectivity Information

by Tao Yin, Jingzheng Yao, Yan Lu and Chunrui Na

Electronics 2023, 12(17), 3633; https://doi.org/10.3390/electronics12173633 - 28 Aug 2023

Cited by 2 | Viewed by 1352

Abstract

This paper proposes a solid-state-LiDAR-inertial-visual fusion framework containing two subsystems: the solid-state-LiDAR-inertial odometry (SSLIO) subsystem and the visual-inertial odometry (VIO) subsystem. Our SSLIO subsystem has two novelties that enable it to handle drastic acceleration and angular velocity changes: (1) the quadratic motion model is adopted in the in-frame motion compensation step of the LiDAR feature points, and (2) the system has a weight function for each residual term to ensure consistency in geometry and reflectivity. The VIO subsystem renders the global map in addition to further optimizing the state output by the SSLIO. To save computing resources, we calibrate our VIO subsystem’s extrinsic parameter indirectly in advance, instead of using real-time estimation. We test the SSLIO subsystem using publicly available datasets and a steep ramp experiment, and show that our SSLIO exhibits better performance than the state-of-the-art LiDAR-inertial SLAM algorithm Point-LIO in terms of coping with strong vibrations transmitted to the sensors due to the violent motion of the crawler robot. Furthermore, we present several outdoor field experiments evaluating our framework. The results show that our proposed multi-sensor fusion framework can achieve good robustness, localization and mapping accuracy, as well as strong real-time performance. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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16 pages, 7677 KiB

Open AccessArticle

DPNet: Dual-Pyramid Semantic Segmentation Network Based on Improved Deeplabv3 Plus

by Jun Wang, Xiaolin Zhang, Tianhong Yan and Aihong Tan

Electronics 2023, 12(14), 3161; https://doi.org/10.3390/electronics12143161 - 21 Jul 2023

Cited by 2 | Viewed by 1694

Abstract

Semantic segmentation finds wide-ranging applications and stands as a crucial task in the realm of computer vision. It holds significant implications for scene comprehension and decision-making in unmanned systems, including domains such as autonomous driving, unmanned aerial vehicles, robotics, and healthcare. Consequently, there is a growing demand for high precision in semantic segmentation, particularly for these contents. This paper introduces DPNet, a novel image semantic segmentation method based on the Deeplabv3 plus architecture. (1) DPNet utilizes ResNet-50 as the backbone network to extract feature maps at various scales. (2) Our proposed method employs the BiFPN (Bi-directional Feature Pyramid Network) structure to fuse multi-scale information, in conjunction with the ASPP (Atrous Spatial Pyramid Pooling) module, to handle information at different scales, forming a dual pyramid structure that fully leverages the effective features obtained from the backbone network. (3) The Shuffle Attention module is employed in our approach to suppress the propagation of irrelevant information and enhance the representation of relevant features. Experimental evaluations on the Cityscapes dataset and the PASCAL VOC 2012 dataset demonstrate that our method outperforms current approaches, showcasing superior semantic segmentation accuracy. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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12 pages, 2976 KiB

Open AccessArticle

Grasping Unstructured Objects with Full Convolutional Network in Clutter

by Tengteng Zhang and Hongwei Mo

Electronics 2023, 12(14), 3100; https://doi.org/10.3390/electronics12143100 - 17 Jul 2023

Viewed by 977

Abstract

Grasping objects in cluttered environments remains a significant challenge in robotics, particularly when dealing with novel objects that have not been previously encountered. This paper proposes a novel approach to address the problem of robustly learning object grasping in cluttered scenes, focusing on scenarios where the objects are unstructured and randomly placed. We present a unique Deep Q-learning (DQN) framework combined with a full convolutional network suitable for the end-to-end grasping of multiple adhesive objects in a cluttered environment. Our method combines the depth information of objects with reinforcement learning to obtain an adaptive grasping strategy to enable a robot to learn and generalize grasping skills for novel objects in the real world. The experimental results demonstrate that our method significantly improves the grasping performance on novel objects compared to conventional grasping techniques. Our system demonstrates remarkable adaptability and robustness in cluttered scenes, effectively grasping a diverse array of objects that were previously unseen. This research contributes to the advancement of robotics with potential applications, including, but not limited to, redundant manipulators, dual-arm robots, continuum robots, and soft robots. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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11 pages, 1406 KiB

Open AccessArticle

Research on Perception and Control Technology for Dexterous Robot Operation

by Tengteng Zhang and Hongwei Mo

Electronics 2023, 12(14), 3065; https://doi.org/10.3390/electronics12143065 - 13 Jul 2023

Cited by 1 | Viewed by 853

Abstract

Robotic grasping in cluttered environments is a fundamental and challenging task in robotics research. The ability to autonomously grasp objects in cluttered scenes is crucial for robots to perform complex tasks in real-world scenarios. Conventional grasping is based on the known object model in a structured environment, but the adaptability of unknown objects and complicated situations is constrained. In this paper, we present a robotic grasp architecture of attention-based deep reinforcement learning. To prevent the loss of local information, the prominent characteristics of input images are automatically extracted using a full convolutional network. In contrast to previous model-based and data-driven methods, the reward is remodeled in an effort to address the sparse rewards. The experimental results show that our method can double the learning speed in grasping a series of randomly placed objects. In real-word experiments, the grasping success rate of the robot platform reaches 90.4%, which outperforms several baselines. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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14 pages, 3385 KiB

Open AccessArticle

UGV Coverage Path Planning: An Energy-Efficient Approach through Turn Reduction

by Nikolaos Baras and Minas Dasygenis

Electronics 2023, 12(13), 2959; https://doi.org/10.3390/electronics12132959 - 5 Jul 2023

Cited by 4 | Viewed by 1139

Abstract

With the advent and rapid growth of automation, unmanned ground vehicles (UGVs) have emerged as a crucial technology, with applications spanning various domains, from agriculture to surveillance, logistics, and military operations. Alongside this surge in the utilization of robotics, novel complications inevitably emerge, posing intriguing questions and challenges to the academic and technological sectors. One such pressing challenge is the coverage path planning (CPP) problem, particularly the notion of optimizing UGV energy utilization during path planning, a significant yet relatively unexplored aspect within the research landscape. While numerous studies have proposed solutions to CPP with a single UGV, the introduction of multiple UGVs within a single environment reveals a unique set of challenges. A paramount concern in multi-UGV CPP is the effective allocation and division of the area among the UGVs. To address this issue, we propose an innovative approach that first segments the area into multiple subareas, which are then allocated to individual UGVs. Our methodology employs fine-tuned spanning trees to minimize the number of turns during navigation, resulting in more efficient and energy-aware coverage paths. As opposed to existing research focusing on models that allocate without optimization, our model utilizes a terrain-aware cost function, and an adaptive path replanning module, leading to a more flexible, effective, and energy-efficient path-planning solution. A series of simulations demonstrated the robustness and efficacy of our approach, highlighting its potential to significantly improve UGV endurance and mission effectiveness, even in challenging terrain conditions. The proposed solution provides a substantial contribution to the field of UGV path planning, addressing a crucial gap and enhancing the body of knowledge surrounding energy-efficient CPP for multi-UGV scenarios. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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25 pages, 9420 KiB

Open AccessArticle

Adaptive SLAM Methodology Based on Simulated Annealing Particle Swarm Optimization for AUV Navigation

by Liqian Zhou, Meng Wang, Xin Zhang, Ping Qin and Bo He

Electronics 2023, 12(11), 2372; https://doi.org/10.3390/electronics12112372 - 24 May 2023

Cited by 4 | Viewed by 1266

Abstract

Simultaneous localization and mapping (SLAM) is crucial and challenging for autonomous underwater vehicle (AUV) autonomous navigation in complex and uncertain ocean environments. However, inaccurate time-varying observation noise parameters may lead to filtering divergence and poor mapping accuracy. In addition, particles are easily trapped in local extrema during the resampling, which may lead to inaccurate state estimation. In this paper, we propose an innovative simulated annealing particle swarm optimization-adaptive unscented FastSLAM (SAPSO-AUFastSLAM) algorithm. To cope with the unknown observation noise, the maximum a posteriori probability estimation algorithm is introduced into SLAM to recursively correct the measurement noise. Firstly, the Sage–Husa (SH) based unscented particle filter (UPF) algorithm is proposed to estimate time-varying measurement noise adaptively in AUV path estimation for improving filtering accuracy. Secondly, the SH-based unscented Kalman filter (UKF) algorithm is proposed to enhance mapping accuracy in feature estimation. Thirdly, SAPSO-based resampling is proposed to optimize posterior particles. The random judgment mechanism is used to update feasible solutions iteratively, which makes particles disengage local extreme values and achieve optimal global effects. The effectiveness and accuracy of the proposed algorithm are evaluated through simulation and sea trial data. The average AUV navigation accuracy of the presented SAPSO-AUFastSLAM method is improved by 18.0% compared to FastSLAM, 6.5% compared to UFastSLAM, and 5.9% compared to PSO-UFastSLAM. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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24 pages, 8488 KiB

Open AccessArticle

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

by Yi Shen, Yunqiang Gao, Mingxin Yuan, Hongwei Sun and Zhenjie Guo

Electronics 2023, 12(9), 2040; https://doi.org/10.3390/electronics12092040 - 28 Apr 2023

Cited by 2 | Viewed by 1312

Abstract

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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18 pages, 3445 KiB

Open AccessArticle

Apple-Picking Robot Picking Path Planning Algorithm Based on Improved PSO

by Ruilong Gao, Qiaojun Zhou, Songxiao Cao and Qing Jiang

Electronics 2023, 12(8), 1832; https://doi.org/10.3390/electronics12081832 - 12 Apr 2023

Cited by 3 | Viewed by 1782

Abstract

To solve the problem that the robot often collides with the obstacles such as branches around the fruit during picking due to its inability to adapt to the fruit growing environment, this paper proposes an apple-picking robot picking path planning algorithm based on the improved PSO. The main contents of the algorithm are: firstly, the fruit and its surrounding branches are extracted from the 3D point cloud data, and the picking direction of the fruit is calculated; then the point cloud on the surface of the fruit and branches is used to establish the spatial model of obstacles; finally, an improved particle swarm optimization (PSO) algorithm is proposed to plan the obstacle avoidance trajectory of the end-effector in space, which can dynamically adjust the velocity weights according to the trend of the particle fitness value and the position of the particle swarm center of mass. The experimental results show that the improved PSO has faster convergence speed than the standard PSO, and the path planning method proposed in this paper improves the fruit-picking success rate to 85.93% and reduces the picking cycle to 12 s. This algorithm can effectively reduce the collision between the manipulator and branches during apple picking and improve the picking success rate and picking efficiency. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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14 pages, 3285 KiB

Open AccessArticle

An Integrated Motion Planning Scheme for Safe Autonomous Vehicles in Highly Dynamic Environments

by Cong Phat Vo and Jeong hwan Jeon

Electronics 2023, 12(7), 1566; https://doi.org/10.3390/electronics12071566 - 26 Mar 2023

Cited by 3 | Viewed by 1723

Abstract

This study proposes a new integrated approach to the motion control of autonomous vehicles, which differs from the conventional method of treating planning and tracking tasks as separate or hierarchical components. By means of the proposed approach we can reduce the side effects on the performance of autonomous vehicles under challenging driving circumstances. To this end, our approach processes both of the aforementioned tasks asynchronously and simultaneously utilizes a multi-threaded architecture to enhance control performance. Meanwhile, the behavior planning feature is integrated into the path-tracking module. Then, a linear parameter-varying model predictive control is deployed for trajectory tracking of autonomous vehicles and compared with the linear model predictive control method. Finally, the control performance of the proposed approach was evaluated through simulation trials on urban roads with placed obstacles. The outcomes revealed that the suggested framework satisfies the processing rate and high-precision criteria, while safely avoiding obstacles, indicating that it is a promising control strategy for real-world applications. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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Review

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22 pages, 1728 KiB

Open AccessReview

Constructing Maps for Autonomous Robotics: An Introductory Conceptual Overview

by Peteris Racinskis, Janis Arents and Modris Greitans

Electronics 2023, 12(13), 2925; https://doi.org/10.3390/electronics12132925 - 3 Jul 2023

Cited by 2 | Viewed by 1625

Abstract

Mapping the environment is a powerful technique for enabling autonomy through localization and planning in robotics. This article seeks to provide a global overview of actionable map construction in robotics, outlining the basic problems, introducing techniques for overcoming them, and directing the reader toward established research covering these problem and solution domains in more detail. Multiple levels of abstraction are covered in a non-exhaustive vertical slice, starting with the fundamental problem of constructing metric occupancy grids with Simultaneous Mapping and Localization techniques. On top of these, topological meshes and semantic maps are reviewed, and a comparison is drawn between multiple representation formats. Furthermore, the datasets and metrics used in performance benchmarks are discussed, as are the challenges faced in some domains that deviate from typical laboratory conditions. Finally, recent advances in robot control without explicit map construction are touched upon. Full article

(This article belongs to the Special Issue Autonomous Robots and Systems)

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