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Recent Advances in Robotics and Intelligent Mechatronics Systems

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 18837

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Prof. Dr. JunZhi Yu

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Guest Editor

Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
Interests: intelligent robots; advanced robot control; embedded vision
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hesheng Wang

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Guest Editor

Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: visual servoing; medical robotics; service robots; autonomous driving; robot control; computer vision; deep learning

Prof. Dr. Ning Sun

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Guest Editor

Institute of Robotics and Automatic Information Systems, College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Interests: vehicle control; mobile robotics; flying robotics; motion control; robot control; motion/trajectory planning; dynamics analysis and control of mechatronic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With a wide range of applications for robots in industry and service sectors, real-time computing plays one of the major roles in various topics in robotics including real-time control, human–robot interactions, sensor perception and fusion, robot intelligence, etc.

This Special Issue covers research, development, and applications in the dynamic and exciting areas of real-time computing and robotics, with an emphasis on both theoretical and application results. The topic is open, but special focus will be given to, but is not limited to, robotics, mechatronics, automation and control systems, artificial intelligence, and sensor perception and fusion techniques, and so on.

Robotics;
Mechatronics;
Sensor perception and fusion;
Control systems;
Automation;
Perception, planning and control of robots;
Artificial intelligence.

Prof. Dr. JunZhi Yu
Prof. Dr. Hesheng Wang
Prof. Dr. Ning Sun
Guest Editors

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Published Papers (7 papers)

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Research

18 pages, 7446 KiB

Open AccessArticle

Variable Time-Step Physics Engine with Continuous Compliance Contact Model for Optimal Robotic Grinding Trajectory Planning

by Yongcan Zhou, Yang Pan, Junpeng Chen and Tianjian Lei

Sensors 2024, 24(5), 1415; https://doi.org/10.3390/s24051415 - 22 Feb 2024

Cited by 2 | Viewed by 1414

Abstract

In the transition from virtual environments to real-world applications, the role of physics engines is crucial for accurately emulating and representing systems. To address the prevalent issue of inaccurate simulations, this paper introduces a novel physics engine uniquely designed with a compliant contact model designed for robotic grinding. It features continuous and variable time-step simulations, emphasizing accurate contact force calculations during object collision. Firstly, the engine derives dynamic equations considering spring stiffness, damping coefficients, coefficients of restitution, and external forces. This facilitates the effective determination of dynamic parameters such as contact force, acceleration, velocity, and position throughout penetration processes continuously. Secondly, the approach utilizes effective inertia in developing the contact model, which is designed for multi-jointed robots through pose transformation. The proposed physics engine effectively captures energy conversion in scenarios with convex contact surface shapes through the application of spring dampers during collisions. Finally, the reliability of the contact solver in the simulation was verified through bouncing ball experiments and robotic grinding experiments under different coefficients of restitution. These experiments effectively recorded the continuous variations in parameters, such as contact force, verifying the integral stability of the system. In summary, this article advances physics engine technology beyond current geometrically constrained contact solutions, enhancing the accuracy of simulations and modeling in virtual environments. This is particularly significant in scenarios wherein there are constant changes in the outside world, such as robotic grinding tasks. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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32 pages, 13945 KiB

Open AccessArticle

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

by Gokhan Kucukayan and Hacer Karacan

Sensors 2024, 24(3), 922; https://doi.org/10.3390/s24030922 - 31 Jan 2024

Cited by 9 | Viewed by 3520

Abstract

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as “You Only Look Once” (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model’s accuracy and the reliability of indoor human detection in real-time drone applications. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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16 pages, 2049 KiB

Open AccessArticle

Learning and Reusing Quadruped Robot Movement Skills from Biological Dogs for Higher-Level Tasks

by Qifeng Wan, Aocheng Luo, Yan Meng, Chong Zhang, Wanchao Chi, Shenghao Zhang, Yuzhen Liu, Qiuguo Zhu, Shihan Kong and Junzhi Yu

Sensors 2024, 24(1), 28; https://doi.org/10.3390/s24010028 - 20 Dec 2023

Cited by 2 | Viewed by 2019

Abstract

In the field of quadruped robots, the most classic motion control algorithm is based on model prediction control (MPC). However, this method poses challenges as it necessitates the precise construction of the robot’s dynamics model, making it difficult to achieve agile movements similar to those of a biological dog. Due to these limitations, researchers are increasingly turning to model-free learning methods, which significantly reduce the difficulty of modeling and engineering debugging and simultaneously reduce real-time optimization computational burden. Inspired by the growth process of humans and animals, from learning to walk to fluent movements, this article proposes a hierarchical reinforcement learning framework for the motion controller to learn some higher-level tasks. First, some basic motion skills can be learned from motion data captured from a biological dog. Then, with these learned basic motion skills as a foundation, the quadruped robot can focus on learning higher-level tasks without starting from low-level kinematics, which saves redundant training time. By utilizing domain randomization techniques during the training process, the trained policy function can be directly transferred to a physical robot without modification, and the resulting controller can perform more biomimetic movements. By implementing the method proposed in this article, the agility and adaptability of the quadruped robot can be maximally utilized to achieve efficient operations in complex terrains. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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12 pages, 1138 KiB

Open AccessArticle

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation

by Tongrui Zhang and Yunsheng Fan

Sensors 2023, 23(24), 9630; https://doi.org/10.3390/s23249630 - 5 Dec 2023

Viewed by 1565

Abstract

Radar data can be presented in various forms, unlike visible data. In the field of radar target recognition, most current work involves point cloud data due to computing limitations, but this form of data lacks useful information. This paper proposes a semantic segmentation network to process high-dimensional data and enable automatic radar target recognition. Rather than relying on point cloud data, which is common in current radar automatic target recognition algorithms, the paper suggests using a radar heat map of high-dimensional data to increase the efficiency of radar data use. The radar heat map provides more complete information than point cloud data, leading to more accurate classification results. Additionally, this paper proposes a dimension collapse module based on a vision transformer for feature extraction between two modules with dimension differences during dimension changes in high-dimensional data. This module is easily extendable to other networks with high-dimensional data collapse requirements. The network’s performance is verified using a real radar dataset, showing that the radar semantic segmentation network based on a vision transformer has better performance and fewer parameters compared to segmentation networks that use other dimensional collapse methods. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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17 pages, 9894 KiB

Open AccessArticle

An Online 3D Modeling Method for Pose Measurement under Uncertain Dynamic Occlusion Based on Binocular Camera

by Xuanchang Gao, Junzhi Yu and Min Tan

Sensors 2023, 23(5), 2871; https://doi.org/10.3390/s23052871 - 6 Mar 2023

Cited by 2 | Viewed by 2236

Abstract

3D modeling plays a significant role in many industrial applications that require geometry information for pose measurements, such as grasping, spraying, etc. Due to random pose changes in the workpieces on the production line, demand for online 3D modeling has increased and many researchers have focused on it. However, online 3D modeling has not been entirely determined due to the occlusion of uncertain dynamic objects that disturb the modeling process. In this study, we propose an online 3D modeling method under uncertain dynamic occlusion based on a binocular camera. Firstly, focusing on uncertain dynamic objects, a novel dynamic object segmentation method based on motion consistency constraints is proposed, which achieves segmentation by random sampling and poses hypotheses clustering without any prior knowledge about objects. Then, in order to better register the incomplete point cloud of each frame, an optimization method based on local constraints of overlapping view regions and a global loop closure is introduced. It establishes constraints in covisibility regions between adjacent frames to optimize the registration of each frame, and it also establishes them between the global closed-loop frames to jointly optimize the entire 3D model. Finally, a confirmatory experimental workspace is designed and built to verify and evaluate our method. Our method achieves online 3D modeling under uncertain dynamic occlusion and acquires an entire 3D model. The pose measurement results further reflect the effectiveness. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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16 pages, 5162 KiB

Open AccessArticle

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

by Ye Wang, Gongbing Shan, Hua Li and Lin Wang

Sensors 2023, 23(1), 425; https://doi.org/10.3390/s23010425 - 30 Dec 2022

Cited by 7 | Viewed by 3723

Abstract

Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches’ experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs’ measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs’ measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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28 pages, 13529 KiB

Open AccessArticle

Design of Transverse Brachiation Robot and Motion Control System for Locomotion between Ledges at Different Elevations

by Chi-Ying Lin and Yong-Jie Tian

Sensors 2022, 22(11), 4031; https://doi.org/10.3390/s22114031 - 26 May 2022

Cited by 4 | Viewed by 2621

Abstract

Bio-inspired transverse brachiation robots mimic the movement of human climbers as they traverse along ledges on a vertical wall. The constraints on the locomotion of these robots differ considerably from those of conventional brachiation robots due primarily to the need for robust hand-eye coordination. This paper describes the development of a motion control strategy for a brachiation robot navigating between wall ledges positioned on a level plane or at different elevations. We based our robot on a four-link arm-body-tail system performing a four-phase movement, including a release phase, body reversal phase, swing-up phase, and grasping phase. We designed a gripper that uses passive wrist joint motion to grasp the ledge during the tail swing. We also developed a dynamic model by which to coordinate the swing-up movement, define the phase switching conditions, and time the grasping action of the grippers. In experiments, the robot proved highly effective in traversing between wall ledges of the same or different elevations. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)

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