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Abstract Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.

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Based on the tunnel crack width identification, there are operating time constraints, limited operating space, high equipment testing costs, and other issues. In this paper, a large subway tunnel is a research object, and the tunnel rail inspection car is an operating platform equipped with industrial cameras in order to meet the requirements of the tunnel tube sheet crack width recognition of more than 0.2 mm, with the measuring instrument to verify that the tunnel rail inspection car in the state of uniform motion camera imaging quality has the reliability through the addition of laser rangefinders, the accurate measurement of the object distance and the calculation of the imaging plane and the angle of the plane to be measured, to amend the three-dimensional cracks. The pixel resolution of the image is corrected, the images imaged by the industrial camera are preprocessed, the YOLOv8 algorithm is used for the intelligent extraction of crack morphology, and finally, the actual width is calculated from the spacing between two points of the crack. The crack detection width obtained by image processing using the YOLOv8 algorithm is basically the same as the value of crack width obtained by manual detection, and the error rate of crack width detection ranges from 0% to 11%, with the average error rate remaining below 4%. Compared with the crack detection error rate of the Support Vector Machine (SVM), the crack extraction model is reduced by 1%, so using the tunnel inspection vehicle as a platform equipped with an industrial camera, YOLOv8 is used to realize the recognition of the shape and width of the cracks on the surface of the tunnel tube sheet to meet the requirements of a higher degree of accuracy. The number of pixels and the detection error rate are inversely proportional to each other. The angle between the imaging plane and the plane under test is directly proportional to the detection error rate. The angle between the vertical axis where the lens midpoint is located and the line connecting the shooting target and the lens center point is α i and the angle θ i between the measured plane and the imaging plane is reciprocal, i.e., α i + θ i = 90°. Therefore, using the inspection vehicle as a mobile platform equipped with an industrial camera and based on the YOLOv8 algorithm, the crack recognition of the tunnel tube sheet has the feasibility and the prospect of wide application, which provides a reference method for the detection of cracks in the tunnel tube sheet. [ABSTRACT FROM AUTHOR]

This paper presents an innovative multi-sensor relative navigation module conceived to enable close-proximity operations for on-orbit servicing of small satellites. The module is composed of a Near-Infrared Laser Range Finder, acting both as range measurer and illuminator, and a monocular camera able to detect a set of reflecting fiducial markers installed on the target for pose estimation. Sensor fusion is performed in several points of the proposed processing pipeline, first following a cross-cueing logic in which the range aids the pose determination process, and later through the integration of both measurements into a Multiplicative Extended Kalman Filter for relative state estimation. The entire relative navigation architecture is first tested by numerically reproducing a close-range rendezvous trajectory of a chaser spacecraft moving toward the target, and using the open-source software Blender for synthetic image generation. Simulations are aimed at determining nominal relative state estimation accuracy (also considering variable sensors' acquisition rates) as well as performance under non-nominal conditions (e.g., due to a temporary loss of measurements). Focusing on the image processing and pose estimation pipeline, an in-depth analysis of the computational efficiency is then carried out by running the proposed algorithms on an embedded processing unit (i.e., the Raspberry Pi 4 Model B) using both synthetic data and real images collected within a dedicated experimental testbed. Numerical and experimental results demonstrate capability to attain pose and relative state estimates in line with accuracy requirements to ensure autonomous and safe close-proximity operations, e.g., up to sub-cm and sub-degree error levels in relative position and attitude respectively. In addition, the algorithm's computational runtime has shown to be compatible with real-time implementation at an acquisition frequency of 2 Hz. • An original architecture for spacecraft relative navigation in close proximity is proposed. • It relies on optical measurements from a monocular camera and a laser range finder. • Circular retroreflective markers are mounted on the target satellite. • Numerical and experimental tests show sub-cm and sub-degree error levels in pose estimation. • Computational efficiency suitable for real time implementation on embedded hardware is proven. [ABSTRACT FROM AUTHOR]

Although laser scanning ideas and hardware solutions are well-known to experts in the field, there is still a large area for optimization. Especially, if long-range and high-resolution scanning is considered, the smallest defects in optical quality should be perfected. On the other hand, the simplicity, reliability, and finally the cost of the solution plays an important role, too. In this paper, a very simple but efficient method of optical correction is presented. It is dedicated to laser scanners operating from inside cylindrical glass domes. Such covers normally introduce aberrations into both the laser beam and receiving optics. If these effects are uncorrected, the laser scanner performance is degraded both in terms of angular resolution and maximum range of operation. It may not be critical for short-range scanning applications; however, if more challenging concepts are considered, this issue becomes crucial. The proposed method does not require sophisticated optical solutions based on aspheric or freeform components, which are frequently used for similar purposes in imaging-through-dome correction but is based on a simple cylindrical refractive correction plate. [ABSTRACT FROM AUTHOR]

Based on the tunnel crack width identification, there are operating time constraints, limited operating space, high equipment testing costs, and other issues. In this paper, a large subway tunnel is a research object, and the tunnel rail inspection car is an operating platform equipped with industrial cameras in order to meet the requirements of the tunnel tube sheet crack width recognition of more than 0.2 mm, with the measuring instrument to verify that the tunnel rail inspection car in the state of uniform motion camera imaging quality has the reliability through the addition of laser rangefinders, the accurate measurement of the object distance and the calculation of the imaging plane and the angle of the plane to be measured, to amend the three-dimensional cracks. The pixel resolution of the image is corrected, the images imaged by the industrial camera are preprocessed, the YOLOv8 algorithm is used for the intelligent extraction of crack morphology, and finally, the actual width is calculated from the spacing between two points of the crack. The crack detection width obtained by image processing using the YOLOv8 algorithm is basically the same as the value of crack width obtained by manual detection, and the error rate of crack width detection ranges from 0% to 11%, with the average error rate remaining below 4%. Compared with the crack detection error rate of the Support Vector Machine (SVM), the crack extraction model is reduced by 1%, so using the tunnel inspection vehicle as a platform equipped with an industrial camera, YOLOv8 is used to realize the recognition of the shape and width of the cracks on the surface of the tunnel tube sheet to meet the requirements of a higher degree of accuracy. The number of pixels and the detection error rate are inversely proportional to each other. The angle between the imaging plane and the plane under test is directly proportional to the detection error rate. The angle between the vertical axis where the lens midpoint is located and the line connecting the shooting target and the lens center point is αi and the angle θi between the measured plane and the imaging plane is reciprocal, i.e., αi + θi = 90°. Therefore, using the inspection vehicle as a mobile platform equipped with an industrial camera and based on the YOLOv8 algorithm, the crack recognition of the tunnel tube sheet has the feasibility and the prospect of wide application, which provides a reference method for the detection of cracks in the tunnel tube sheet.

Although laser scanning ideas and hardware solutions are well-known to experts in the field, there is still a large area for optimization. Especially, if long-range and high-resolution scanning is considered, the smallest defects in optical quality should be perfected. On the other hand, the simplicity, reliability, and finally the cost of the solution plays an important role, too. In this paper, a very simple but efficient method of optical correction is presented. It is dedicated to laser scanners operating from inside cylindrical glass domes. Such covers normally introduce aberrations into both the laser beam and receiving optics. If these effects are uncorrected, the laser scanner performance is degraded both in terms of angular resolution and maximum range of operation. It may not be critical for short-range scanning applications; however, if more challenging concepts are considered, this issue becomes crucial. The proposed method does not require sophisticated optical solutions based on aspheric or freeform components, which are frequently used for similar purposes in imaging-through-dome correction but is based on a simple cylindrical refractive correction plate.

The sit-to-stand (STS) motion evaluates physical functions in frail older adults. Mounting sensors or using a camera is necessary to measure trunk movement during STS motion. Therefore, we developed a simple measurement method by embedding laser range finders in the backrests and seats of chairs that can be used in daily life situations. The objective of this study was to validate the performance of the proposed measurement method in comparison with that of the optical motion capture (MoCap) system during STS motion. The STS motions of three healthy young adults were simultaneously measured under seven conditions using a chair with embedded sensors and the optical MoCap system. We evaluated the waveform similarity, absolute error, and relationship of the trunk joint angular excursions between these measurement methods. The experimental results indicated high waveform similarity in the trunk flexion phase regardless of STS conditions. Furthermore, a strong relationship was observed between the two measurement methods with respect to the angular excursion of the trunk flexion. Although the angular excursion of the trunk extension exhibited a large error, the developed chair with embedded sensors evaluated trunk flexion during the STS motion, which is a characteristic of frail older adults. [ABSTRACT FROM AUTHOR]

Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only. [ABSTRACT FROM AUTHOR]

In order to simulate the target echo laser signal, we designed a novel laser target simulator with high precision and high attenuation rate used for sensitivity test, function test or tracking mode test of laser receiving equipment, such as Lidar and Laser Range Finder. Regarding the laser receiving equipment on the motion platform, the model of target echo power was established based on the characteristics of laser atmospheric transmissions in the application environment. The simulation shows the model is consistent with the test data. Additionally, the MEMS ESVOA (Micro-Electro Mechanical Systems Electro-Static Variable Optical Attenuator) device was used in the overall solution of the laser target simulator to realize the target echo signal simulation with miniaturization, high attenuation rate and high precision. Finally, a laser target simulator test system was built and its high rate of attenuation, high precision and stability of laser power tested. The test results show that the change rate of target echo signal energy is more than 7 × 104 dB/s, the power simulation accuracy is better than 0.1 dB and the laser pulse power stability is better than 0.61%.

ABSTRAK Automated Guided Vehicle (AGV) merupakan salah satu jenis mobile robot yang digunakan untuk mengangkut barang menuju tempat tujuan. AGV mampu bekerja pada lingkungan yang dinamis tanpa menggunakan garis pemandu. Namun sebelumnya harus mempunyai informasi yang cukup terhadap lingkungan kerjanya. Teknik ini dikenal dengan Simulataneous Localization and Mapping (SLAM) yang digunakan robot untuk menggambar peta sekaligus mengetahui posisi robot di dalam peta. Pada penelitian ini, metode yang digunakan yaitu SLAM berbasis Iterative Closest Point (ICP) dengan algoritma K-Means yang menggunakan kumpulan titik dari sensor laser range finder (LRF) untuk membangun peta lingkungan. Pemetaan SLAM menggunakan algoritma K-Means memiliki error hasil scan jarak 77,69% lebih kecil dan waktu eksekusi 0,18% lebih cepat dibandingkan dengan KD-Tree. Peta yang dihasilkan dengan algoritma KMeans pada ICP-SLAM memberikan hasil yang lebih baik & mendekati keadaan ruangan sebenarnya dibandingkan menggunakan algoritma KD-Tree. Kata kunci: ICP-SLAM, K-Means, Laser Range Finder. ABSTRACT Automated Guided Vehicle (AGV) is a type of mobile robot that is used to transport goods to destination. AGV is able to work in a dynamic environment without guidelines. However, it must have sufficient information about its working environment beforehand. This technique is known as Simultaneous Localization and Mapping (SLAM) which is used by a robot to be able to draw a map as well as to determine its position on the map. In this research, the method used is SLAM based on Iterative Closest Point (ICP) with the K-Means algorithm that uses a collection of points from the Laser Range Finder (LRF) sensor to build an environmental map. SLAM using the K-Means algorithm has 77,69% smaller distance error and 0,18% faster execution time than KD-Tree. The map generated by the K-Means algorithm on an ICP-SLAM gives better results & closer to the actual state than using the KD-Tree. Keywords: ICP-SLAM, K-Means, Laser Range Finder.

A high-precision relative navigation system with applications in the aerospace industry is proposed and validated for the accurate autonomous landing of drones. It relays on the generation of an optical grid from two dual optical comb generators that projects that grid into space where an object, such as an unmanned aerial vehicle, detects its relative position related to the origin of the grid to aid in the high resolution obtaining of the positioning and subsequent guidance of the object. A prototype of the proposed system has been developed, characterized, and tested in a highly relevant environment. The results of flight tests using the proposed system for the high-precision relative positioning of a drone are also presented. [ABSTRACT FROM AUTHOR]

The sit-to-stand (STS) motion evaluates physical functions in frail older adults. Mounting sensors or using a camera is necessary to measure trunk movement during STS motion. Therefore, we developed a simple measurement method by embedding laser range finders in the backrests and seats of chairs that can be used in daily life situations. The objective of this study was to validate the performance of the proposed measurement method in comparison with that of the optical motion capture (MoCap) system during STS motion. The STS motions of three healthy young adults were simultaneously measured under seven conditions using a chair with embedded sensors and the optical MoCap system. We evaluated the waveform similarity, absolute error, and relationship of the trunk joint angular excursions between these measurement methods. The experimental results indicated high waveform similarity in the trunk flexion phase regardless of STS conditions. Furthermore, a strong relationship was observed between the two measurement methods with respect to the angular excursion of the trunk flexion. Although the angular excursion of the trunk extension exhibited a large error, the developed chair with embedded sensors evaluated trunk flexion during the STS motion, which is a characteristic of frail older adults.

Fusion of laser range finder (LRF) and camera is widely used in the field of intelligent mobile robots for performing various tasks, such as autonomous navigation, 2D or 3D mapping, object classification and obstacle avoidance. However, data misregistrations between both the sensors occur during installation. The extrinsic calibration (rotation and translation parameters) of these two different sensor modalities is an important prerequisite for robust data fusion. There are numerous techniques available for calibrating 2D-LRF and 3D-LRF with cameras, which can be methodologically distinguished based on target-based and target-less approaches, and these techniques can be further classified based on human interaction level into manual and automatic approaches. This paper presents a systematic literature review of existing methods and techniques for estimating extrinsic calibration parameters of LRF and camera relative to each other and also compares them. In addition to this, it also discusses various robot applications based on the fusion of LRF and camera, where calibration of these two sensors improves the fusion results and makes the robotic system reliable and efficient in accomplishing several challenging tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a lane detection algorithm using a laser range finder (LRF) for the autonomous navigation of a mobile robot. There are many technologies for ensuring the safety of vehicles, such as airbags, ABS, and EPS. Further, lane detection is a fundamental requirement for an automobile system that utilizes the external environment information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. In the case of a vision-based system, the recognition of the environment of a three-dimensional space becomes excellent only in good conditions for capturing images. However, there are so many unexpected barriers, such as bad illumination, occlusions, vibrations, and thick fog, that the vision-based method cannot be used for satisfying the abovementioned fundamental requirement. In this paper, a three-dimensional lane detection algorithm using LRF that is very robust against illumination is proposed. For the three-dimensional lane detection, the laser reflection difference between the asphalt and the lane according to color and distance has been utilized with the extraction of feature points. Further, a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been experimentally verified. [ABSTRACT FROM AUTHOR]

This paper introduces a mobile robot system that follows a human target, in particular, a robot system designed to follow a smartphone user. The proposed system integrates a global following method based on Wi-Fi radio intensity maps and a local following method based on matching walking states.“ Global following ”means that the robot follows the global trajectory of the human target. Because it is important to keep following when the target is lost in a crowded environment, we propose a global following system that uses Wi-Fi radio intensity and occupancy grid maps. This paper presents some experimental results for a robot being called to follow a human target using a Wi-Fi radio intensity map. “ Local following ”means that the robot always keeps close to the human target and keeps tracking the target without losing it. The human target is detected by cross-correlating the signals indicating the target ’s walking motion, including vertical acceleration data measured by the target user’s smartphone and the horizontal velocity of the legs measured by an LRF installed in the robot. The results show that the system produces a higher cross-correlation coefficient for the human target than for other pedestrians in the vicinity. The paper introduces a method for calculating the cross-correlation coefficients online and presents experimental results which show that the target can be identified from an online calculation of the cross-correlation coefficients.

Abstract We are developing a robot that can play an outdoor game with children. In realizing such a robot, the person detection and tracking methods play an important role. In this paper, we propose methods for improving person detection and tracking using a laser range finder (LRF). A problem facing player detection using an LRF during play is that detection of a player’s body sometimes fails because of the posture of the player. Thus, we propose two improvements of the conventional person detection algorithm. One is to determine the body boundary when an arm overlaps the body, and the other is to choose the most probable object as the body among the detected objects. Another problem is how to track players who are hidden by other players. We introduce a new algorithm to robustly track a player even when the player is behind other players. We conducted three experiments. In the first experiment, we investigate the performance improvement by the proposed player detection algorithm. The second experiment proves that the proposed player-tracking algorithm can robustly track a player even after the LRF has lost that player. In the last experiment, we conduct the game Darumasan ga Koronda using the developed system and confirm that the system can perform the game without large problems.

Various researches on mobile robot have been conducted and this has led to target tracking being a part of the mainstream based on mobile robot. Target tracking is the process of target recognition and data association. Target recognition is to make the feature data from a target, and it is coming from the analysis of the target data. Raw data from sensors make the target data and it could be the first recognition step for the target. Data association is about the way to track the target. It is the continuous comparison and association between previous and present target data with time. In this paper, we focus on improving target recognition performance by filtering data received from laser sensors. The key idea to the filtering process is extracting data consisting of hips from three-dimensional data. This process provides a clearer standard of distinction between people. The tracking people algorithm is based on SJPDAF to manage the occlusion situation. Experimental results from our study show that the new proposed tracking method is robust in practical environments. [ABSTRACT FROM AUTHOR]

Environment maps must first be generated to drive mobile robots automatically. Path planning is performed based on the information given in an environment map. Various types of sensors, such as ultrasonic and laser sensors, are used by mobile robots to acquire data on its surrounding environment. Among these, the laser sensor, which has the property of being able to go straight and high accuracy, is used most often. However, the beams from laser sensors are refracted and reflected when it meets a transparent obstacle, thus generating noise. Therefore, in this paper, a state-of-the-art algorithm was proposed to detect transparent obstacles by analyzing the pattern of the reflected noise generated when a laser meets a transparent obstacle. The experiment was carried out using the environment map generated by the aforementioned method and gave results demonstrating that the robot could avoid transparent obstacles while it was moving towards the destination. [ABSTRACT FROM AUTHOR]

In order to enable an autonomous mobile robot to travel over rough terrain, it necessitates the capability to detect self‐position accurately even when the odometry errors are increased in traveling. The conventional method can keep high speed and precise localization using iterative closest point algorithms or feature matching techniques. However, effects of steep changes of a mobile robot position are not considered when it travels over rough terrain. In this article, we propose the method for efficient real‐time 6D pose tracking using a rotating 2D laser scanner in traveling over rough terrain. For adaptation to steep changes of the position, weighted point clouds are generated based on the angular and the linear velocity measured by sensors mounted on the robot. And the position and posture of the robot are sequentially optimized by the scan matching in increments of 10 scans. In indoor experiments, we evaluated accuracy of our method when the robot passes on rugged floor. As a result, our method was performed with less than 0.078 m RMS positional error in real time. [ABSTRACT FROM AUTHOR]

This paper investigates the light propagation through several types of water by experimental and simulation. The Zemax-ray tracing software allowed to simulate the propagation of light in water and to observe the receiver response by reproducing the real conditions of propagation. The underwater environment has been reproduced by a 1.2 m long water tube and 20 cm in diameter with a glass window fitted on one side. The use of tap water with different amounts of sand leads toward three types of water with different attenuation coefficients (0.133, 0.343, 0.580 m−1). The light transmission in the three types of water was experimentally evaluated using a doubled Nd:YAG laser with energy of 4.3 mJ and a pulse width of 20 ns. Comparisons were done between simulation and experimental results. [ABSTRACT FROM AUTHOR]