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Trajectory Planning and Object Recognition for Robot Sensing and Control

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 14843

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Dr. Gang Peng

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Guest Editor

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: intelligent robotics; machine vision; multi-sensor fusion SALM; environment perception; machine learning; artificial intelligence

Prof. Dr. Zhun Fan

E-Mail Website
Guest Editor

College of Engineering, Shantou University, Shantou 515063, China
Interests: artificial intelligence and robotics; swarm intelligence; computational intelligence; design automation; computer vision
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Dear Colleagues,

An increasing number of robots are entering our lives and industry production. Trajectory planning and object recognition are thus becoming important research hotspots, with tasks such as 3D grasping, flexible operation, and navigation depending on machine vision and multi-sensor fusion. This Special Issue aims to publish sensor-based robot control theory and engineering applications, trajectory planning and target tracking methods, and relative techniques for manipulator motion planning, mobile robot path planning, and navigation systems.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of intelligent robot systems. Potential topics include but are not limited to:

Sensor-based robot control for intelligent manufacture
Manipulator motion planning
Mobile robot navigation
Trajectory planning and control
Target tracking for intelligent transportation
Environment perception
SLAM Based on Multi-sensor fusion
object recognition based on deep learning
Sensing and control in underwater robot system
Sensing and control for UAV
Autonomous driving and unmanned system
3D object grasping planning
Reinforcement learning for intelligent robot
Intelligent agricultural machinery
Path planning and flexible operation for picking robot
Intelligent construction and automation system
Inspection robot and application system in Specific scenarios

Dr. Gang Peng
Prof. Dr. Zhun Fan
Guest Editors

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Keywords

sensor-based robot control
trajectory planning
object recognition
target tracking
pose estimation machine vision and multi-sensor fusion
3D grasping planning
flexible operation

Published Papers (14 papers)

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Research

16 pages, 4288 KiB

Open AccessArticle

Reprojection Error Analysis and Algorithm Optimization of Hand–Eye Calibration for Manipulator System

by Gang Peng, Zhenyu Ren, Qiang Gao and Zhun Fan

Sensors 2024, 24(1), 113; https://doi.org/10.3390/s24010113 - 25 Dec 2023

Viewed by 738

Abstract

The Euclidean distance error of calibration results cannot be calculated during the hand–eye calibration process of a manipulator because the true values of the hand–eye conversion matrix cannot be obtained. In this study, a new method for error analysis and algorithm optimization is presented. An error analysis of the method is carried out using a priori knowledge that the location of the augmented reality markers is fixed during the calibration process. The coordinates of the AR marker center point are reprojected onto the pixel coordinate system and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling to obtain the Euclidean distance between the two coordinates as the basis for the error analysis. We then fine-tune the results of the hand–eye calibration algorithm to obtain the smallest reprojection error, thereby obtaining higher-precision calibration results. The experimental results show that, compared with the Tsai–Lenz algorithm, the optimized algorithm in this study reduces the average reprojection error by 44.43% and the average visual positioning error by 50.63%. Therefore, the proposed optimization method can significantly improve the accuracy of hand–eye calibration results. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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11 pages, 5071 KiB

Open AccessCommunication

Tool Frame Calibration for Robot-Assisted Ultrasonic Testing

by Hanming Zhang, Jingpin Wang and Canzhi Guo

Sensors 2023, 23(21), 8820; https://doi.org/10.3390/s23218820 - 30 Oct 2023

Cited by 2 | Viewed by 683

Abstract

Tool frame calibration has been widely used in robot-assisted printing, welding, and grinding, but it is not suitable for ultrasonic testing because the robot is submerged in water. The purpose of this paper is to present a tool frame calibration method, which is suitable for improving the precision of ultrasonic testing. In uniform mediums, sound travels along a straight line like ray. A reflector is fixed in water to reflect ultrasound, which makes it possible to measure distances between incidence points on a reflector and tool center point (TCP) on an ultrasound transducer. In addition, the positions and poses of the end flange are recorded through a robot controller. Finally, an optimization method is applied to calculate the position and pose errors of the tool frame relative to the end flange according to such records. The presented method was implemented in an ultrasonic testing system. We selected 100 incidence points on the reflector to calculate the assembly errors of the transducer. The pulse amplitude rose obviously after calibration, which verifies that this is an effective method. Considering that ultrasonic transducers can be used as a measuring tool, this paper proposes a tool frame calibration method for ultrasonic testing robots without introducing other measuring devices, which draws the conclusion that tool frame can be calibrated through ultrasound. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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18 pages, 5974 KiB

Open AccessArticle

An Infrared Small Target Detection Method Based on Attention Mechanism

by Xiaotian Wang, Ruitao Lu, Haixia Bi and Yuhai Li

Sensors 2023, 23(20), 8608; https://doi.org/10.3390/s23208608 - 20 Oct 2023

Cited by 2 | Viewed by 1100

Abstract

The human visual attention system plays an important role in infrared target recognition because it can quickly and accurately recognize infrared small targets and has good scene adaptability. This paper proposes an infrared small target detection method based on an attention mechanism, which consists of three modules: a bottom-up passive attention module, a top-down active attention module, and decision feedback equalization. In the top-down active attention module, given the Gaussian characteristics of infrared small targets, the idea of combining knowledge-experience Gaussian shape features is applied to implement feature extraction, and quaternion cosine transform is performed to achieve multi-dimensional fusion of Gaussian shape features, thereby achieving complementary fusion of multi-dimensional feature information. In the bottom-up passive attention module, considering that the difference in contrast and motion between the target and the background can attract attention easily, an optimal fast local contrast algorithm and improved circular pipeline filtering are adopted to find candidate target regions. Meanwhile, the multi-scale Laplacian of the Gaussian filter is adopted to estimate the optimal size of the infrared small target. The fast local contrast algorithm based on box filter acceleration and structure optimization is employed to extract local contrast features, and candidate target regions can be obtained by using an adaptive threshold. Besides, the mean gray, target size, Gaussian consistency, and circular region constraint are used in pipeline filtering to extract motion regions, and the false-alarm rate is reduced effectively. Finally, decision feedback equalization is adopted to obtain real targets. Experiments are conducted on some real infrared images involving complex backgrounds with sea, sky, and ground clutters, and the experimental results indicate that the proposed method can achieve better detection performance than conventional baseline methods, such as RLCM, ILCM, PQFT, MPCM, and ADMD. Also, mathematical proofs are provided to validate the proposed method. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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25 pages, 34588 KiB

Open AccessArticle

An Improved Genetic Algorithm for the Recovery System of USVs Based on Stern Ramp Considering the Influence of Currents

by Lulu Zhou, Xiaoming Ye, Zehao Huang, Pengzhan Xie, Zhenguo Song and Yanjia Tong

Sensors 2023, 23(19), 8075; https://doi.org/10.3390/s23198075 - 25 Sep 2023

Cited by 3 | Viewed by 825

Abstract

With the progression of marine exploration and exploitation, as well as the advancements in mechanical intelligence, the utilization of the unmanned surface vehicle (USV) and the design of their guidance system have become prominent areas of focus. However, the stern ramp recovery of the USV is still in its infancy due to its unique attitude requirements and automation design. Furthermore, few studies have addressed the impact of maritime disturbances, with most research limited to simulations. To enhance the efficiency and accuracy of stern ramp recovery, this paper presents the development and construction of a novel recovery system. By incorporating physical modeling of disturbance forces acting on USVs at sea, the practicality of the system is improved. Additionally, an optimized genetic algorithm is introduced in the navigation module to improve convergence rates and subsequently enhance recovery efficiency. A line-of-sight (LOS) algorithm based on average velocity is proposed in this paper to ensure the attainment of unique attitude requirements and to improve the effectiveness of stern chute recovery. This paper provides a detailed description of the independently designed USV hardware system. Moreover, simulations and practical experiments conducted using this experimental platform are presented, offering a new solution for the USV’s stern ramp recovery. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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14 pages, 1131 KiB

Open AccessArticle

Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network

by Juncao Hong, Diquan Chen, Wenji Li and Zhun Fan

Sensors 2023, 23(18), 7982; https://doi.org/10.3390/s23187982 - 20 Sep 2023

Viewed by 698

Abstract

Quadrotor unmanned aerial vehicles (UAVs) often encounter intricate environmental and dynamic limitations in real-world applications, underscoring the significance of proficient trajectory planning for ensuring both safety and efficiency during flights. To tackle this challenge, we introduce an innovative approach that harmonizes sophisticated environmental insights with the dynamic state of a UAV within a potential field framework. Our proposition entails a quadrotor trajectory planner grounded in a kinodynamic gene regulation network potential field. The pivotal contribution of this study lies in the amalgamation of environmental perceptions and kinodynamic constraints within a newly devised gene regulation network (GRN) potential field. By enhancing the gene regulation network model, the potential field becomes adaptable to the UAV’s dynamic conditions and its surroundings, thereby extending the GRN into a kinodynamic GRN (K-GRN). The trajectory planner excels at charting courses that guide the quadrotor UAV through intricate environments while taking dynamic constraints into account. The amalgamation of environmental insights and kinodynamic constraints within the potential field framework bolsters the adaptability and stability of the generated trajectories. Empirical results substantiate the efficacy of our proposed methodology. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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17 pages, 2892 KiB

Open AccessArticle

An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal

by Xuchao Huang, Shigang Wang, Xueshan Gao, Dingji Luo, Weiye Xu, Huiqing Pang and Ming Zhou

Sensors 2023, 23(18), 7937; https://doi.org/10.3390/s23187937 - 16 Sep 2023

Viewed by 730

Abstract

In the context of predicting pedestrian trajectories for indoor mobile robots, it is crucial to accurately measure the distance between indoor pedestrians and robots. This study aims to address this requirement by extracting pedestrians as regions of interest and mitigating issues related to inaccurate depth camera distance measurements and illumination conditions. To tackle these challenges, we focus on an improved version of the H-GrabCut image segmentation algorithm, which involves four steps for segmenting indoor pedestrians. Firstly, we leverage the YOLO-V5 object recognition algorithm to construct detection nodes. Next, we propose an enhanced BIL-MSRCR algorithm to enhance the edge details of pedestrians. Finally, we optimize the clustering features of the GrabCut algorithm by incorporating two-dimensional entropy, UV component distance, and LBP texture feature values. The experimental results demonstrate that our algorithm achieves a segmentation accuracy of 97.13% in both the INRIA dataset and real-world tests, outperforming alternative methods in terms of sensitivity, missegmentation rate, and intersection-over-union metrics. These experiments confirm the feasibility and practicality of our approach. The aforementioned findings will be utilized in the preliminary processing of indoor mobile robot pedestrian trajectory prediction and enable path planning based on the predicted results. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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12 pages, 1928 KiB

Open AccessArticle

Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field

by Ke Li, Chen Huang, Jiaping Liang, Yanbin Zou, Biao Xu, Yao Yao, Yang Zhang and Dandan Liu

Sensors 2023, 23(18), 7664; https://doi.org/10.3390/s23187664 - 05 Sep 2023

Viewed by 586

Abstract

High-rise building fires pose a serious threat to the lives and property safety of people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescuers. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of the autonomous optimal deployment of base stations in high-rise building fire environments based on a UAV group. A novel problem formulation is proposed that solves the non-line-of-sight (NLOS) positioning problem in complex and unknown environments. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environments. The NLOS positioning problem in the fire field environment is turned into the line-of-sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby at any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations’ deployment goals and to avoid collision. Finally, the positioning accuracy of our method and that of the common method were compared under many different cases. The simulation result showed that the positioning error of a simulated firefighter in the fire field environment was improved from more than 10 m (the positioning error of the traditional method) to less than 1 m. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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20 pages, 2378 KiB

Open AccessArticle

Unmanned Ariel Vehicle (UAV) Path Planning for Area Segmentation in Intelligent Landmine Detection Systems

by Ahmed Barnawi, Krishan Kumar, Neeraj Kumar, Nisha Thakur, Bander Alzahrani and Amal Almansour

Sensors 2023, 23(16), 7264; https://doi.org/10.3390/s23167264 - 18 Aug 2023

Cited by 1 | Viewed by 1176

Abstract

Landmine contamination is a significant problem that has devastating consequences worldwide. Unmanned aerial vehicles (UAVs) can play an important role in solving this problem. The technology has the potential to expedite, simplify, and improve the safety and efficacy of the landmine detection process prior to physical intervention. Although the process of detecting landmines in contaminated environments is systematic, it is proven to be rather costly and overwhelming, especially if prior information about the location of the lethal objects is unknown. Therefore, automation of the process to orchestrate the search for landmines has become necessary to utilize the full potential of system components, particularly the UAV, which is the enabling technology used to airborne the sensors required in the discovery stage. UAVs have a limited amount of power at their disposal. Due to the complexity of target locations, the coverage route for UAV-based surveys must be meticulously designed to optimize resource usage and accomplish complete coverage. This study presents a framework for autonomous UAV-based landmine detection to determine the coverage route for scanning the target area. It is performed by extracting the area of interest using segmentation based on deep learning and then constructing the coverage route plan for the aerial survey. Multiple coverage path patterns are used to identify the ideal UAV route. The effectiveness of the suggested framework is evaluated using several target areas of differing sizes and complexities. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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15 pages, 4806 KiB

Open AccessArticle

Path Following and Collision Avoidance of a Ribbon-Fin Propelled Underwater Biomimetic Vehicle-Manipulator System

by Yanbing He, Xiang Dong, Yu Wang and Shuo Wang

Sensors 2023, 23(16), 7061; https://doi.org/10.3390/s23167061 - 09 Aug 2023

Viewed by 820

Abstract

This paper addresses the problem of path following and dynamic obstacle avoidance for an underwater biomimetic vehicle-manipulator system (UBVMS). Firstly, the general kinematic and dynamic models of underwater vehicles are presented; then, a nonlinear model predictive control (NMPC) scheme is employed to track a reference path and collision avoidance simultaneously. Moreover, to minimize the tracking error and for a higher degree of robustness, improved extended state observers are used to estimate model uncertainties and disturbances to be fed into the NMPC prediction model. On top of this, the proposed method also considers the uncertainty of the state estimator, while combining a priori estimation of the Kalman filter to reasonably predict the position of dynamic obstacles during short periods. Finally, simulations and experimental results are carried out to assess the validity of the proposed method in case of disturbances and constraints. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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16 pages, 2336 KiB

Open AccessArticle

The Objective Dementia Severity Scale Based on MRI with Contrastive Learning: A Whole Brain Neuroimaging Perspective

by Yike Zhang, Wenliang Fan, Xi Chen, Wei Li and on behalf of the for Alzheimer’s Disease Neuroimaging Initiative

Sensors 2023, 23(15), 6871; https://doi.org/10.3390/s23156871 - 02 Aug 2023

Viewed by 1227

Abstract

In the clinical treatment of Alzheimer’s disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. To address these issues, we propose an objective dementia severity scale based on MRI (ODSS-MRI) using contrastive learning to automatically evaluate the neurological function of patients. The approach utilizes a deep learning framework and a contrastive learning strategy to mine relevant information from structural magnetic resonance images to obtain the patient’s neurological function level score. Given that the model is driven by the patient’s whole brain imaging data, but without any possible biased manual intervention or instruction from the physician or patient, it provides a comprehensive and objective evaluation of the patient’s neurological function. We conducted experiments on the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset, and the results showed that the proposed ODSS-MRI was correlated with the stages of AD 88.55% better than all existing methods. This demonstrates its efficacy to describe the neurological function changes of patients during AD progression. It also outperformed traditional psychiatric rating scales in discriminating different stages of AD, which is indicative of its superiority for neurological function evaluation. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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16 pages, 7031 KiB

Open AccessArticle

Enhancement and Optimization of Underwater Images and Videos Mapping

by Chengda Li, Xiang Dong, Yu Wang and Shuo Wang

Sensors 2023, 23(12), 5708; https://doi.org/10.3390/s23125708 - 19 Jun 2023

Cited by 1 | Viewed by 1166

Abstract

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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18 pages, 1528 KiB

Open AccessArticle

Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks

by Biaozhang Huang and Xinde Li

Sensors 2023, 23(12), 5653; https://doi.org/10.3390/s23125653 - 16 Jun 2023

Cited by 1 | Viewed by 1002

Abstract

Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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16 pages, 2504 KiB

Open AccessArticle

VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR

by Gang Peng, Yicheng Zhou, Lu Hu, Li Xiao, Zhigang Sun, Zhangang Wu and Xukang Zhu

Sensors 2023, 23(10), 4588; https://doi.org/10.3390/s23104588 - 09 May 2023

Cited by 2 | Viewed by 1691

Abstract

For the existing visual–inertial SLAM algorithm, when the robot is moving at a constant speed or purely rotating and encounters scenes with insufficient visual features, problems of low accuracy and poor robustness arise. Aiming to solve the problems of low accuracy and robustness of the visual inertial SLAM algorithm, a tightly coupled vision-IMU-2D lidar odometry (VILO) algorithm is proposed. Firstly, low-cost 2D lidar observations and visual–inertial observations are fused in a tightly coupled manner. Secondly, the low-cost 2D lidar odometry model is used to derive the Jacobian matrix of the lidar residual with respect to the state variable to be estimated, and the residual constraint equation of the vision-IMU-2D lidar is constructed. Thirdly, the nonlinear solution method is used to obtain the optimal robot pose, which solves the problem of how to fuse 2D lidar observations with visual–inertial information in a tightly coupled manner. The results show that the algorithm still has reliable pose-estimation accuracy and robustness in many special environments, and the position error and yaw angle error are greatly reduced. Our research improves the accuracy and robustness of the multi-sensor fusion SLAM algorithm. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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23 pages, 6007 KiB

Open AccessArticle

A Novel Motor Structure with Extended Particle Swarm Optimization for Space Robot Control

by Hongwei Gao, Zide Liu, Xuna Wang, Dongyu Li, Tian Zhang, Jiahui Yu and Jianbin Wang

Sensors 2023, 23(8), 4126; https://doi.org/10.3390/s23084126 - 20 Apr 2023

Cited by 2 | Viewed by 1068

Abstract

This paper studies motor structures and optimization methods for space robots, proposing an optimized stepped rotor bearingless switched reluctance motor (BLSRM) to solve the poor self-starting ability and significant torque fluctuation issues in traditional BLSRMs. Firstly, the advantages and disadvantages of the 12/14 hybrid stator pole type BLSRM were analyzed, and a stepped rotor BLSRM structure was designed. Secondly, the particle swarm optimization (PSO) algorithm was improved and combined with finite element analysis for motor structure parameter optimization. Subsequently, a performance analysis of the original and new motors was conducted using finite element analysis software, and the results showed that the stepped rotor BLSRM had an improved self-starting ability and significantly reduced torque fluctuation, verifying the effectiveness of the proposed motor structure and optimization method. Full article

(This article belongs to the Special Issue Trajectory Planning and Object Recognition for Robot Sensing and Control)

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