Laser Range Sensor Research Articles

3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems.

Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, which is challenging in outdoor environments because of uneven light variations when using 3D cameras for the localization of apples. Therefore, this research attempted to overcome the effect of light variations for the 3D cameras during outdoor apple harvesting operations. Thus, integrated single-point laser sensors for the localization of apples using a state-of-the-art model, the EfficientDet object detection algorithm with an mAP@0.5 of 0.775 were used in this study. In the experiments, a RealSense D455f RGB-D camera was integrated with a single-point laser ranging sensor utilized to obtain precise apple localization coordinates for implementation in a harvesting robot. The single-point laser range sensor was attached to two servo motors capable of moving the center position of the detected apples based on the detection ID generated by the DeepSORT (online real-time tracking) algorithm. The experiments were conducted under indoor and outdoor conditions in a spindle-type apple orchard artificial architecture by mounting the combined sensor system behind a four-wheel tractor. The localization coordinates were compared between the RGB-D camera depth values and the combined sensor system under different light conditions. The results show that the root-mean-square error (RMSE) values of the RGB-D camera depth and integrated sensor mechanism varied from 3.91 to 8.36 cm and from 1.62 to 2.13 cm under 476~600 lx to 1023~1100 × 100 lx light conditions, respectively. The integrated sensor system can be used for an apple harvesting robotic manipulator with a positional accuracy of ±2 cm, except for some apples that were occluded due to leaves and branches. Further research will be carried out using changes in the position of the integrated system for recognition of the affected apples for harvesting operations.

Prototype of Check the Class Individually by Making Our Rounds in the Classroom Trajectory Measurement System Using Laser Range Sensor

Prototype of Check the Class Individually by Making Our Rounds in the Classroom Trajectory Measurement System Using Laser Range Sensor

Research on the Surface-State Parameterization of a Refill Friction Stir Spot Welding Joint Made of Aluminum Alloy and Its Connection to the Fracture Mode.

Surface features are crucial for assessing welding quality because they serve as an intuitive depiction of the quality of the joint and have a major influence on welding strength. According to the characteristics of the refill friction stir spot welding (RFSSW) process and an analysis of the surface-state and internal morphology of RFSSW joints, a method of predicting the mechanical properties of RFSSW joints based on surface-state characteristics was proposed. In this paper, a laser-ranging sensor was used to characterize the surface state of RFSSW joints, and parametric characterization methods of the surface-state features of RFSSW joints were proposed. On this basis, a support vector machine was used to predict and analyze the fracture mode of RFSSW joints. The accuracy of the analysis of the test samples reached 95.8%. This paper provides a more efficient and convenient new method for the quality evaluation of RFSSW joints.

A novel BPM mechanical center calibration method based on laser ranging

Determining the mechanical center of the beam position monitor (BPM) has been a difficulty for BPM calibration. To solve this problem, a method of positioning the BPM mechanical center based on laser ranging is proposed. This method uses high-precision antenna support as the core locating datum, and high-precision laser ranging sensor (LRS) as the detection tool. By detecting the distances from the LRSs to the antenna support and the distances from the LRSs to the BPM, the mechanical center of the BPM can be indirectly determined. The theoretical system error of this method is within 20 μm, and the experimental results show that the measurement repeatability is 15 μm, This method has low cost and fast speed, which can be used for large-scale calibration.

Research and Experiment on Soybean Plant Identification Based on Laser Ranging Sensor

When endeavoring to study the complex growth conditions of soybean plants under natural conditions, a problem arises due to the similar appearances of both soybean plants and weeds. To address this issue, a soybean plant recognition model based on a laser ranging sensor is proposed. To demonstrate the applicability of the soybean plant recognition model, experiments are conducted using ultrasonic sensors and laser ranging sensors to analyze the diameter, height, and spacing conditions in the model. A test environment is built, and during the pre-test, the laser range sensor detects objects with diameters of 3 mm and 5 mm with two and three measurement points, respectively, at a speed of 0.2 m/s. At a speed of 0.3 m/s, there is one measurement point for objects with 3 mm diameter and two measurement points for objects with 5 mm diameter. At 0.4 m/s, there are also one and two measurement points for objects with diameters of 3 mm and 5 mm, respectively. These results demonstrate that the laser range sensor can more accurately recognize the diameter conditions of soybean plants and weeds and can distinguish between the diameters of soybean plants and weeds. Subsequently, the recognition rate of the model is evaluated by observing whether the weeding mechanism can synchronize seedling avoidance after the soybean plant passes through the sensor. The recognition rates of the optimized model at speeds of 0.2 m/s, 0.3 m/s, and 0.4 m/s are 100%, 98.75%, and 93.75%, respectively. Upon comprehensive analysis, the soybean plant recognition model is determined to achieve a recognition rate of 98.75% at a speed of 0.3 m/s, which is considered a moderate speed, and demonstrates more stable recognition of plant diameters. The test further verifies the reliability and effectiveness of the method for distinguishing between soybean plants and weeds. The research results can serve as a reference for recognizing soybean plants based on the use of laser ranging sensors.

A constant plunge depth control strategy for robotic FSW based on online trajectory generation

Robotic friction stir welding (RFSW) usually comes with a huge upsetting force, and the stiffness of the welding system distributes unevenly over the position, which leads to a large deviation of the plunge depth of the tool at the end of the robot. The conventional constant distance tracking control suffers from the problem of unsmooth compensation leading to the vibration of the robot and thus degrading the weld quality. For this problem, a constant plunge depth control based on online trajectory generation for RFSW is studied, which can generate an accurate welding trajectory according to the rough initial reference path and smoothly compensate for the plunge deviation. Initially, three laser-ranging sensors are utilized to measure the pose deviation of the tool in real-time and generate the ideal welding trajectory according to the projection vector method. Then, a deformation compensation model is established to realize the real-time prediction of the correct value. To ensure the smoothness and rapidity of the dynamic tracking process of displacement deviation, we adopt an online trajectory generator as the core of optimization control to meet the process constraints such as speed, acceleration, and jerk during the compensation process. Finally, simulation and experiment are carried out. The results show that the proposed method can effectively reduce the vibration caused by compensation during the welding process and reduce flash, which can improve the welding quality.

Development of Three-Dimensional Breast Scan and Measurement Application Using Laser Imaging Detection and Ranging Sensor on iPhone.

Laser imaging detection and ranging (LiDAR) is a modern three-dimensional (3D) technology that uses a time-of-flight method based on the round-trip time of an infrared laser beam to detect the presence and features of objects. The iPhone 12 Pro is the first smart mobile device with built-in LiDAR sensors. The authors' team developed a software application based on iOS devices with built-in LiDAR sensors for 3D breast scanning and automatically analyzing the breast's geometric measurement. Breast geometry, including midclavicle-to-nipple distance, sternal notch-to-nipple distance, nipple-to-inframammary fold (IMF) distance, distance between nipples, and body circumference on nipple and IMF level were measured using the software application and tapeline. The relative technical error of measurement (rTEM) value was used to calculate the error ratios between the measurements acquired by the software application and those of the tapeline. Good rTEM values ranging from 2.99% to 5.19% were found in the midclavicle-to-nipple distance, sternal notch-to-nipple distance, distance between nipples, nipple-level circumference, and IMF-level circumference. However, there was a poor rTEM value greater than 10% in the nipple-to-IMF distance. The proposed software application using current iOS devices with built-in LiDAR sensors can provide an ideal 3D scanning system that has a low cost burden, good accuracy, portability, and ease of use.

Modeling and Parameter Identification of a 3D Measurement System Based on Redundant Laser Range Sensors for Industrial Robots.

The low absolute positioning accuracy of industrial robots is one of the bottlenecks preventing industrial robots from precision applications. Kinematic calibration is the main way to improve the absolute positioning accuracy of industrial robots, which greatly relies on three-dimensional (3D) measurement instruments, including laser trackers and pull rope mechanisms. These instruments are costly, and their required intervisibility space is large. In this paper, a precision 3D measurement instrument integrating multiple laser range sensors is designed, which fuses the information of multiple redundant laser range sensors to obtain the coordinates of a 3D position. An identification model of laser beam position and orientation parameters based on redundant distance information and standard spherical constraint is then developed to reduce the requirement for the assembly accuracy of laser range sensors. A hybrid identification algorithm of PSO-LM (particle swarm optimization Levenberg Marquardt) is designed to solve the high-order nonlinear problem of the identification model, where PSO is used for initial value identification, and LM is used for final value identification. Experiments of identification of position and orientation, verifications of the measuring accuracy, and the calibration of industrial robots are conducted, which show the effectiveness of the proposed 3D measurement instrument and identification methods. Moreover, the proposed instrument is small in size and can be used in narrow industrial sites.

Safe physical human-robot interaction: A quasi whole-body sensing method based on novel laser-ranging sensor ring pairs

Non-contact detection is a competitive method for ensuring safety in close human-robot interaction (HRI). However, existing sensor configuration methods have difficulty satisfying the requirements for covering a wide range of sensing zones with fewer spots and a low-cost, low-complexity processing system. In this study, we developed a novel laser-ranging sensor configuration comprising sensors mounted on ring pairs, thereby implementing quasi whole-body sensing and cooperative location with fewer sensors. Each sensor ring pair was composed of double sensor rings mounted at opposite ends of robot arms. Based on the cone detection zone of each laser-ranging sensor, an optimisation of the configuration parameters showed that the maximum sensing volume coverage could be obtained when the laser-ranging sensors on each sensor ring were mounted in a tilted and biased form at certain angles. According to detection data and robot arm kinematics, a cooperative location model was also established to estimate the intervention position. Furthermore, we presented a self-occlusion geometric detection method for the robot, so as to identify cases in which the robot body entered the detection zone of the sensor ring pairs. To validate the efficacy of the proposed methods, experiments on quasi whole-body sensing, cooperative location, and self-occlusion detection were conducted with a UR10 robot. The experimental results show that the proposed sensor ring pair and detection method can effectively detect an approaching human and effectively estimate the intervention position and self-occlusion, allowing for efficient and safe cooperative performance with humans.

Accurate and efficient surface profile measurement of the airport runway

ABSTRACT The roughness of the airfield runway is significant for the stability and safety of aircraft taking-off and landing. In view of the problem that the airfield runway roughness measuring instrument cannot accurately measure the large wavelength uneven information, an accurate and efficient method of the airfield pavement roughness measurement is proposed in this paper. Using data collector and data transfer unit control the robotic total station to track and capture the prism, then record the elevation between the prism and the robotic total station. At the same time, the data collector and data transfer unit also control the laser-ranging sensors to measure the elevation between the prism and the measuring points on the pavement. Knowing the elevation of the robotic total station, the elevation of the measuring points can be calculated using the data of the elevation. The curve of the runway surface profile can be measured. Considering the deviation caused by the roll or pitch angle of the vehicle while driving, a revised elevation method is proposed and deduced in this paper. It is verified by experiments that using the new device and the method mentioned in this paper is simple and efficient with high precision.

Analysis of Spastic Gait in Patients With Cervical Myelopathy Using the Timed Up and Go Test With a Laser Range Sensor

Cross-sectional study. This study aimed to objectively evaluate spastic gait and reveal its novel characteristics via analysis of gait in patients with cervical myelopathy (CM) using the Timed Up and Go (TUG) test with a laser range sensor. Among patients with CM, spastic gait is a common diagnostic symptom; thus, objective assessments of spastic gait would be useful for the diagnosis of CM and recognition of disease status. Although spastic gait has been objectively evaluated in previous studies, the methods employed in those studies are not suitable for clinical settings. In total, 37 and 24 participants were recruited for a control group and CM group, respectively. CM was diagnosed by spine surgeons. We developed a laser TUG test, in which the position and velocity of both the legs were captured. The parameter values for both groups were statistically compared, and odds ratios were calculated using logistic regression analyses. The total TUG-test time, time to stand up, time to first step, number of steps, and trajectory error for the CM group were significantly higher than those for the control group, whereas the average velocity and average stride length for the CM group were significantly lower than those for the control group. There was a significant independent association between the total TUG-test time and CM. The optimal cutoff point of the total test time for CM risk was approximately 9 seconds. Through the use of the laser TUG test, we were able to identify characteristics of spastic gait, which leads to difficulty in standing and taking the first step, wobbling while walking, and an increased risk of falling. We found that the risk of CM was higher if the individual took longer than 9 seconds to complete the TUG test.Level of Evidence: 4.

Footsteps and walking trajectories during the Timed Up and Go test in young, older, and Parkinson's disease subjects.

Footsteps and walking trajectories during the Timed Up and Go test (TUG), and their relationships with spatiotemporal gait parameters during turning in people with Parkinson's disease (PD) and older people have not been clarified. We investigated the footsteps, walking trajectories, and spatiotemporal parameters during the TUG in PD, healthy older (HO), and healthy young (HY) subjects and the associations between the parameters of footsteps or walking trajectories and the spatiotemporal parameters during turning. Twenty-six PD, 20 HO, and 20 HY subjects participated. They performed the TUG at self-selected speed and underwent evaluations of spatiotemporal parameters in the forward, turning, and return phases and parameters of footsteps and walking trajectories by a system using laser range sensors to measure the two-dimensional distance data of the legs. Foot placement for each foot was measured as the estimated position when the leg speed of movement reached the minimum between foot-contact and foot-off. We calculated the minimum distance from a TUG marker to footsteps and the maximum anterior distance from the start to the footsteps. Step length was calculated using a method for non-linear walking. The PD subjects showed significantly smaller step lengths in all phases. The minimum distance from the marker to the footsteps in PD subjects was significantly smaller than in HO subjects and was significantly positively correlated only with the turning-phase step length. The maximum anterior distance from the start to footsteps in HO subjects was significantly larger than in PD and HY subjects and was not correlated with any spatiotemporal parameters. This study demonstrated that people with PD turn close to the TUG marker, and this turning strategy may be associated with the decreased step length when turning. These findings could help in providing instructions which prevent the exaggeration of step-length reduction when turning.

Laser scanner for 3D reconstruction of a wound's edge and topology.

Robotic systems have the potential to overcome inherent limitations of humans and offer substantial advantages to patients including reduction in surgery time. Our group has undertaken the challenge of developing autonomous wound closure system. One of the initial steps is to allow accurate assessment of wound skin topology and wound edge location. We present a vision-laser scanner to generate 3D point cloud for 3D reconstruction of wound's edge and topology. When the laser range sensor measures Z coordinate, two encoders installed on the actuators of the gantry robot provide the precision values of X, Y coordinates simultaneously. The 3D point cloud of the wound skin is generated by recordings of X, Y and Z during scanning is performed over wound skin surface. To reduce the scanning time, we exploit a supplementary laser LED to project a regular laser spot on the wound skin surface, which can provide an additional measurement point by incorporating artificial neural network estimation approach. In the meantime, the point cloud of the wound edge can be extracted by detecting if the laser spot is located on the wound edge in the image from 2D camera. The mean absolute error (MAE) and standard deviation (σ) of wound edge are measured in MeshLab environment. The MAE (σ) in X (tangent), Y (tangent), and Z (normal) are 0.32 (0.22) mm, 0.37 (0.34) mm, and 0.61 (0.29) mm, respectively. The experimental results demonstrate that the vision-laser scanner attains high accuracy in determining wound edge location along the tangent of the wound skin. A vision-laser scanner is developed for 3D reconstruction of wound's edge and topology. The experimental tests on the different wound models revealed the effectiveness of the vision-laser scanner. The proposed scanner can generate 3D point cloud of the wound skin and its edge simultaneously, and thus significantly improve the accuracy of wound closure in clinical applications.

An Object Localization System Using Monocular Camera and Two-Axis-Controlled Laser Ranging Sensor for Mobile Robot

Aiming at object grasping of indoor mobile robots, this paper develops an object localization system based on the fusion of a monocular camera and a two-axis-controlled laser ranging sensor. The system can locate the specified object in space without obtaining the background depth information of the object, which reduces the consumption of computational resources and has a low cost. According to the coordinate mapping of the object in the image, the measurement point of the laser ranging sensor is controlled to coincide with the target point by two servo motors in perpendicular axes. Thus, the spatial location of the target point can be determined from the rotation angles of the cradle head motors and the laser-measured distance together. To accurately control the rotation angles, a compound strategy combining feedback and feedforward is proposed. On the one hand, the feedback of the laser measurement point is introduced in the image coordinate to realize the close-loop control of the measurement point. On the other hand, a feedforward compensator based on disturbance observation is designed to reduce the influence of robot motion. An experimental setup is developed and substantial experiments are implemented to evaluate the performance of the proposed system. The results show that object localization can be effectively achieved with good accuracy and dynamic response.

Random Target Localization for an Upper Limb Prosthesis

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5‐DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor

Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor

Estimation of body direction based on gait for service robot applications

Recently, there have been several studies on the research and development of service robots, such as reception or waiter robots for facilities and companion robots for support of baggage transportation or guidance in public spaces. Several experimental results in real environments have been reported. To realize socially acceptable human–robot interaction for service robots, human recognition, including not only position but also body direction, around the robot is important. Using an RGB-D camera, it is possible to detect the posture of a person. However, because the viewing angle of the camera is narrow, it is difficult to recognize the environment around the robot with a single device. This study proposes the estimation of the body direction based on the gait, that is, not only the position and velocity, but also the state of the legs (stance or swing phase), using laser range sensors installed at shin height. We verify the effectiveness of the proposed method for several patterns of movement, which are seen when a person interacts with the service robot and evaluate measurement accuracy.

Human-friendly control system design for two-wheeled service robot with optimal control approach

This paper proposes a novel control system design for a two-wheeled service robot that follows a person as an assistant without knowing the person’s destination. For this kind of service robot, the key skill is to realize human-friendly movement. However, appropriate motion always changed depending on the situation. For instance, when the robot is close and person turns toward it, it is important to suppress the robot’s acceleration. Likewise, if the person turns away from the robot, the robot should maintain its position within an appropriate area. Therefore, to deal with various required movements, our control system is able to change its properties automatically and suitably depending on the situation by using weights of the cost function in nonlinear model predictive control (NMPC) as a function of the relative distance between the person and the robot. Unlike previous methods, our design includes only one controller. Consequently, we are able to take into account system stability. Moreover, owing to proposing in NMPC framework, it is easy to extend our method by adopting other recognition or goal-setting methods. We conducted simulations using actual human walking data taken by the robot’s laser range sensors. The experiments demonstrate that the robot can follow a person who performs U-turn, confirming that our method can produce human-friendly robot movement in a practical scene.

Multi-sensor data fusion for accurate surface modeling

Multi-sensor data fusion is advantageous while fusing data from heterogeneous range sensors, for scanning a scene containing both fine and coarse details. This paper presents a new multi-sensor range data fusion method with the aim to increase the descriptive contents of the entire generated surface model. First, a new training framework of the scanned range dataset to solve the relaxed Gaussian mixture model-based method by applying the convex relaxation technique is presented. The classification of the range data is based on a trained statistical model. In the data fusion experiments, a laser range sensor and Kinect (V1) are used. Based on the segmentation criterion, the range data fusion is performed by integration of the finer regions range data obtained from a laser range sensor with the coarser regions of the Kinect range data. The fused range information overcomes the weaknesses of the respective range sensors, i.e., the laser scanner is accurate but takes time while the Kinect is fast but not very accurate. The surface model of the fused range dataset generates a highly accurate, realistic surface model of the scene. The experimental results demonstrate robustness of the proposed approach.

Laser Ranging-Assisted Binocular Visual Sensor Tracking System.

Aimed at improving the low measurement accuracy of the binocular vision sensor along the optical axis in the process of target tracking, we proposed a method for auxiliary correction using a laser-ranging sensor in this paper. In the process of system measurement, limited to the mechanical performance of the two-dimensional turntable, the measurement value of a laser-ranging sensor is lagged. In this paper, the lag information is updated directly to solve the time delay. Moreover, in order to give full play to the advantages of binocular vision sensors and laser-ranging sensors in target tracking, federated filtering is used to improve the information utilization and measurement accuracy and to solve the estimated correlation. The experimental results show that the real-time and measurement accuracy of the laser ranging-assisted binocular visual-tracking system is improved by the direct update algorithm and the federal filtering algorithm. The results of this paper are significant for binocular vision sensors and laser-ranging sensors in engineering applications involving target tracking systems.