Laser Range Sensor Research Articles

Automated Pose Measurement Method Based on Multivision and Sensor Collaboration for Slice Microdevice

In this article, an automated method based on the multivision and sensor collaboration is proposed to achieve the precision pose measurement of microdevice with a slice micropart embedded inside. This method consists of three modules. First, the invisible attitude component measurement module based on a laser range sensor and the top view is designed to acquire the initial attitude component determined by the inside slice micropart. Second, the attitude component measurement module based on the contour primitives of interest extraction method abbreviated as contour primitives of interest extraction (CPIE) is utilized to obtain the other attitude components by extracting features from images directly. Third, an attitude controller based on the multivision is presented to achieve automated attitude component restriction ensuring that an exact attitude can be obtained when occlusion occurred. A series of experiments including entire assembly experiments are conducted and experimental results reveal that the proposed methods are effective and the attitude error is less than 0.6° meeting the requirement of assembly.

A Neural Network Approach to Navigation of a Mobile Robot and Obstacle Avoidance in Dynamic and Unknown Environments

Mobile robot navigation and obstacle avoidance in dynamic and unknown environments is one of the most challenging problems in the field of robotics. Considering that a robot must be able to interact with the surrounding environment and respond to it in real time, and given the limited sensing range, inaccurate data, and noisy sensor readings, this problem becomes even more acute. In this paper, we attempt to develop a neural network approach equipped with statistical dimension reduction techniques to perform exact and fast robot navigation, as well as obstacle avoidance in such a manner. In order to increase the speed and precision of the network learning and reduce the noise, kernel principal component analysis is applied to the training patterns of the network. The proposed method uses two feed-forward neural networks based on function approximation with a back-propagation learning algorithm. Two different data sets are used for training the networks. In order to visualize the robot environment, 180◦ laser range sensor (SICK) readings are employed. The method is tested on real-world data and experimental results are included to verify the effectiveness of the proposed method.

Human Leg Tracking System Based on Fusion of Laser Range Sensor and Instrumented Insoles Using Learning-based Occlusion Compensation

Human Leg Tracking System Based on Fusion of Laser Range Sensor and Instrumented Insoles Using Learning-based Occlusion Compensation

Design and Research of Active Heave Compensation System Based on Laser Ranging Sensor

Marine resource exploitation and marine cargo transportation were increasingly frequent. Due to the impact of the marine environment, ships or platforms were affected. In this paper, a servo electric cylinder was used as a wave compensation actuator to design a wave compensation system. The laser sensor was used to measure the displacement in the direction of the heave platform, and the obtained displacement was applied to the wave compensation in the heave direction to verify the feasibility of the compensation system.

People Detection and Tracking Using LIDAR Sensors

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.

Preview Control Approach for Laser-Range-Finder-Based Terrain Following

This paper addresses the design and performance analysis for a preview control laser range finder (LRF) based terrain-following system for fixed-wing unmanned aerial vehicles. The complexity and performance of such systems depend predominantly on the chosen concept and the layout of its hardware components, mainly its sensors. The related estimation and control algorithms designed for the terrain-following task have to further address system uncertainties and disturbances. In this paper, a laser range sensor based terrain-following system is proposed. The various error factors are discussed and the error propagation through the system is analyzed. The resulting error model is first verified using a numerical simulation, Subsequently, it is utilized to quantify the system tracking error, outline the preferred values for various system parameters in order to achieve higher terrain-following performance, and finally suggest the optimal pointing angle for the LRF. The suggested analysis approach and results grant an effective analytical method for designing such terrain-following system and evaluating its performance.

High Spatial Resolution Three-Dimensional Imaging Static Unitary Detector-Based Laser Detection and Ranging Sensor

This paper presents a static unitary detector (STUD)-based laser detection and ranging (LADAR) sensor with a 16-to-1 transimpedance-combining amplifier for high spatial resolution three-dimensional (3-D) applications. In order to readout the large size of a photodetector for better results of 3-D information without any reduction of the bandwidth, the partitioning photosensitive cell method is embedded in a 16-to-1 transimpedance-combining amplifier. The effective number of partitioning photosensitive cells and signal-combining stages are selected based on the analysis of the partitioning photosensitive cell method for the optimum performance of a transimpedance-combining amplifier. A prototype chip is fabricated in a 0.18-μm CMOS technology. The input referred noise is 41.9 pA/√Hz with a bandwidth of 230 MHz and a transimpedance gain of 70.4 dB·Ω. The total power consumption of the prototype chip is approximately 86 mW from a 1.8-V supply, and the TICA consumes approximately 15.4 mW of it.

High precision assembly and efficient dispensing approaches for millimeter objects

In this article, high precision alignment and efficient dispensing approaches for millimeter objects are proposed. Firstly, a thin component and a cylindrical component are assembled together based on microscopic vision and laser range sensor. If the normal vector of upper surface of thin component is not perpendicular to the optical axis of side camera, the region around the lower edge line will be blurred easily in image. In order to measure the orientation of the thin component with high precision, a novel method based on laser range sensor is proposed. The two components are assembled after orientation and position alignment. In order to bond the two components, the glue is dispensed into all the 16 holes on the components. For keeping the two components firm after being dispensed, the volume of glue dispensed into the 16 holes should be controlled precisely. A laser range sensor is employed to measure the volume of fluid glue. The dispensing process should be finished in a limited time for the characteristics of fluid glue that will change with time. The fluid glue should be dispensed into the hole for some times to reach the expected volume. However, it is necessary to wait a little time in order to obtain stable level of glue after the glue is dispensed into the hole. Therefore, a dual dispensing strategy with higher efficiency is developed which fully utilizes the waiting time. Experimental results validate the effectiveness of the proposed system and methods.

Information Correlated Lévy Walk Exploration and Distributed Mapping Using a Swarm of Robots

In this work, we present a novel distributed method for constructing an occupancy grid map of an unknown environment using a swarm of robots with global localization capabilities and limited inter-robot communication. The robots explore the domain by performing Levy walks in which their headings are defined by maximizing the mutual information between the robot's estimate of its environment in the form of an occupancy grid map and the distance measurements that it is likely to obtain when it moves in that direction. Each robot is equipped with laser range sensors, and it builds its occupancy grid map by repeatedly combining its own distance measurements with map information that is broadcast by neighboring robots. Using results on average consensus over time-varying graph topologies, we prove that all robots' maps will eventually converge to the actual map of the environment. In addition, we demonstrate that a technique based on topological data analysis, developed in our previous work for generating topological maps, can be readily extended for adaptive thresholding of occupancy grid maps. We validate the effectiveness of our distributed exploration and mapping strategy through a series of 2D simulations and multi-robot experiments.

A High-Multi Target Resolution Focal Plane Array-Based Laser Detection and Ranging Sensor.

This paper introduces a digital-assisted multiple echo detection scheme, which utilizes the waste time of the full serial data readout period in a focal plane array (FPA)-based laser detection and ranging (LADAR) receiver. With the support of an external digital signal processor (DSP) and additional analog memory inserted into the receiver, the proposed readout scheme can effectively enhance multi-target resolution (MTR) three times higher than the conventional FPA-based LADAR, while maintaining low power consumption and a small area. A prototype chip was fabricated in a 0.18-μm CMOS process with an 8 × 8 FPA configuration, where each single receiver pixel occupied an area of 100 μm × 100 μm. The single receiver achieved an MTR of 20 ns with 7.47 mW power dissipation, an input referred noise current of 4.48 pA/√Hz with a bandwidth 530 MHz, a minimum detectable signal (MDS) of 340 nA, a maximum walk error of 2.2 ns, and a maximum non-linearity of 0.05% among the captured multiple echo images.

The validity of spatiotemporal gait analysis using dual laser range sensors: a cross-sectional study

BackgroundThe spatiotemporal parameters were used for sophisticated gait analysis in widespread clinical use. Recently, a laser range sensor has been proposed as a new device for the spatiotemporal gait measurement. However, measurement using a single laser range sensor can only be used for short-range gait measurements because the device irradiates participants with lasers in a radial manner. For long-range gait measurement, the present study uses a modified method using dual laser range sensors installed at opposite ends of the walking path. The aim of present study was to investigate the concurrent validity of the proposed method for spatiotemporal gait measurement by comparison to a computer-based instrumented walkway system.MethodsTen healthy participants were enrolled in this study. Ten-meter walking tests at 100, 75, and 50% of the comfortable speed were conducted to determine the concurrent validity of the proposed method compared to instrumented walkway measurements. Frequency distributions of errors for foot-contact (FC) and foot-off (FO) estimated times between the two systems were also calculated to determine the adequacy of estimation of FC and FO from three perspectives: accuracy (smallness of mean error), precision (smallness of variability), and unambiguity (monomodality of histogram). Intra-class correlation coefficient (2,1) was used to determine the concurrent validity of spatiotemporal parameters between the two systems.ResultThe results indicate that the detection times for FC and FO estimated by the proposed method did not differ from those measured by the instrumented walkway reference system. In addition, histogram for FC and FO showed monomodality. Intra-class correlation coefficients of the spatiotemporal parameters (stance time: 0.74; double support time: 0.56; stride time: 0.89; stride length: 0.83; step length: 0.71; swing time: 0.23) were not high enough. The mean errors of all spatiotemporal parameters were small.ConclusionsThese results suggest that the proposed lacks sufficient concurrent validity for spatiotemporal gait measurement. Further improvement of this proposed system seems necessary.Trial registrationUMIN000032710. Registered 24 May 2018. Retrospectively registered.

Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments.

Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.

Improved leg tracking considering state transition of gait phase and leg observation using a laser range sensor

Improved leg tracking considering state transition of gait phase and leg observation using a laser range sensor

Experimental Results of Testing a Direct Monocular Visual Odometry Algorithm Outdoors on Flat Terrain under Severe Global Illumination Changes for Planetary Exploration Rovers

We present the experimental results obtained by testing a monocular visual odometry algorithm on a real robotic platform outdoors, on flat terrain, and under severe changes of global illumination. The algorithm was proposed as an alternative to the long-established feature based stereo visual odometry algorithms. The rover's 3D position is computed by integrating the frame to frame rover's 3D motion over time. The frames are taken by a single video camera rigidly attached to the rover looking to one side tilted downwards to the planet's surface. The frame to frame rover's 3D motion is directly estimated by maximizing the likelihood function of the intensity differences at key observation points, without establishing correspondences between features or solving the optical flow as an intermediate step, just directly evaluating the frame to frame intensity differences measured at key observation points. The key observation points are image points with high linear intensity gradients. Comparing the results with the corresponding ground truth data, which was obtained by using a robotic theodolite with a laser range sensor, we concluded that the algorithm is able to deliver the rover's position in average of 0.06 seconds after an image has been captured and with an average absolute position error of 0.9% of distance traveled. These results are quite similar to those reported in scientific literature for traditional feature based stereo visual odometry algorithms, which were successfully used in real rovers here on Earth and on Mars. We believe that they represent an important step towards the validation of the algorithm and make us think that it may be an excellent tool for any autonomous robotic platform, since it could be very helpful in situations in which the traditional feature based visual odometry algorithms have failed. It may also be an excellent candidate to be merged with other positioning algorithms and/or sensors.

Real-time gait event detection in a real-world environment using a laser-ranging sensor and gyroscope fusion method

Objective: Wearable gait event detection (GED) techniques have great potential for clinical applications by aiding the rehabilitation of individuals in their daily living environment. Unlike previous wearable GED techniques, which have been proposed for offline detection or laboratory settings, we aimed to develop a real-time GED system adapted for utilization in the daily living environment. Approach: This study presents a novel GED system in which foot clearance and sagittal angular velocity were incorporated to realize real-time GED in a real-world environment. The accuracy and robustness of the proposed system were validated in a real-world scenario that consisted of complex ground surfaces, i.e. varying inclinations. Forty-three subjects (23–83 years) were included in this study, and a total of 8866 gait cycles were recorded for analysis. Main results: The proposed system demonstrated consistently high performance in detecting toe off (TO) and heel strike (HS) events in indoor and outdoor walking data which was supported by high performance scores. The detection accuracy of the walking data reached 2.59 ± 13.26 ms (indoor) and 3.31 ± 14.78 ms (outdoor) for TO events, 3.36 ± 15.92 ms (indoor) and 3.77 ± 16.99 ms (outdoor) for HS events. The proposed system showed better performance in detection precision than state-of-the-art real-time GED methods. Significance: The proposed system will benefit the development of long-term analysis and intervention techniques for use in clinics and daily living environments.

A control method for hydraulic manipulators in automatic emulsion filling

Abstract For autonomous operation of hydraulic manipulators, environmental perception and autonomous control are two research priorities. In this paper, our research focuses on emulsion explosive self-filling, including self-identification of boreholes and autonomous control of manipulators. In an effort to achieve that purpose, we establish the kinematics model of the 5-DOF hydraulic manipulator which is transformed from a crane. Vision and laser range sensors are employed to measure the positions and orientations of boreholes. Additionally, a novel inverse kinematics numerical method of the 5-DOF manipulator is presented that only needs information of Z-axis direction vector and the position of end effector. Several experiments are performed to verify the feasibility of the control method for unmanned filling of emulsion explosives by the hydraulic manipulator.

Design of Semi-Automatic Banana Bud Removal Machine

The banana bud removal machine which can adapt to different types of fields was designed in this paper, it can not only cut the bud but also broke its root to prevent the buds growing again. Furtherly, in order to reduce the probability of accidentally injuring the main stem during the machine operating, the banana bud removal controller was designed too. An infrared proximity sensor and a laser range sensor are connected to the controller. Based on the two sensors, controller can control the cutting bud operation without injuring to the banana main stem and control broking root operation to avoid destroying the root of the banana main stem. Experiment results showed that the machine can effectively complete the task of removing the banana bud and achieve effective detection of the main stem radius and depth of the drill. And the biggest error is 8mm, which ensures the safety of the banana main stem.

Effects of tool helix angles on machined surface morphology in tilt side milling of cantilever thin-walled plates

For milling the side wall surfaces of cantilever thin-walled plates, to tilt the tool to a proper angle could decrease the cutting force component in the direction of the lowest stiffness of the plates, and then mitigate the chatter, resulting in the improvement of the machined surface finish. The tool helix angle and tilt angle are the two major variables determining the cutting force component, accordingly having significant effects on the machined surface morphology. In this paper, these effects are investigated through experiments with solid end mills of different helix angles.With the surface roughness Ra as the quantification assessment parameter for machined surface morphologies, variations of surface morphologies not only with the tool tilt angles but also with the tool helix angles are investigated. The mathematical model expressing the relation between the tool helix angle and the corresponding optimal tilt angle is built when the quality of the machined surfaces is the best. For better understanding the effects of these two variables on the dynamic vibrations of the workpiecethat directly contributes to the formation of surface morphologies, especially chatter marks, the instantaneous vibration displacements of plates during the milling process are also measured by a laser range sensor, and the effects on the surface roughness are analyzed. This study could provide guidance for process parameters optimization in milling the sidewall surfaces of thin-walled components.

Improved leg tracking based on estimated body direction using a laser range sensor

Improved leg tracking based on estimated body direction using a laser range sensor

Development of a Dexterous Dual-Arm Omnidirectional Mobile Manipulator

Abstract This paper presents an experimental mobile manipulator composed of a dual-arm torso with a human-like structure assembled on an omnidirectional platform. The dual-arm system integrates several commercial devices: two arms UR5 with 6 degrees of freedom, each one equipped with an Allegro Hand with four fingers and a total of 16 degrees of freedom, and each fingertip provided with a tactile sensor. The omnidirectional platform has a circular shape and three wheels, with an original special design, whose coordinated movements allow omnidirectional displacements of the platform. It is equipped with laser-range sensors, a radio positioning system and an RGB-D camera to detect potential obstacles and navigate safely. The paper describes the main aspects of the mechanical structure of the mobile manipulator and the software framework developed to control the whole device.