Range Finder Sensor Research Articles

A Smart Real-Time Parking Control and Monitoring System.

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle's license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

Laboratory evaluation of infrared and ultrasonic range-finder sensors for on-the-go measurement of soil surface roughness

Soil surface roughness (SSR) is strongly influenced by agricultural activities and soil properties. However, measuring SSR is tedious and time-consuming, especially under stationary conditions. This work was conducted with laboratory measurements of SSR to investigate how to measure it on-the-go. Four roughness indices were examined to investigate which one provides a more representative value for measuring SSR on-the-go. Out of all indices, random roughness (RR) was selected for the next stage of the study, since it refers to the degree of fineness and pulverization of the soil. Four roughness classes of A, B, C, and D were formed with depressions of 0.5, 1, 2, and 4 cm, respectively, on the soil surface using four corrugated steel sheets. The RR was calculated for these corrugated soil surfaces using two range-finder sensors, an infrared (IR) and an ultrasonic (Ult) type. A carrier was built up to move the range-finder sensors above the corrugated soil surfaces at travel speeds of 2.6, 3.5, and 4.8 km/h. The overall averages of RR determined for IR and Ult sensors were correlated with the reference values measured using a pin-type profiler with correlation coefficients of 0.99 and 0.31, respectively. No significant differences were observed between the calculated RRs at the examined travel speeds. The depression and peaks measured using IR and Ult range-finder sensors were also compared to the reference values measured using the pin-type profiler and the results showed average correlation coefficients of 0.97 and 0.69 for IR and Ult range-finder sensors, respectively. While both range-finder sensors could thoroughly measure the distance under stationary conditions (R2 >0.99), however, only the IR sensor succeeded in measuring RR on-the-go on roughness classes A, B, C, and D. The reason may be attributed to the wider footprint of the Ult sensor. The results of this study can help predict physical properties related to SSR more quickly.

Objective quantification of viewing behaviours during printed and electronic tasks in emmetropic and myopic ultra-Orthodox Jewish men.

Ultra-Orthodox Jewish men are known to have a high prevalence of myopia, which may be due to intense near-work from an early age. This study objectively assessed near-viewing behaviours in ultra-Orthodox and non-ultra-Orthodox men in Israel for different tasks. Ultra-Orthodox (n= 30) and non-ultra-Orthodox (n= 38) men aged 18-33 years participated. Autorefraction, visual acuity, height and Harmon distance were measured. An objective range-finding sensor was mounted on their spectacles while they performed four 10-min tasks in a randomised order: (1) reading printed material, (2) writing printed material, (3) passive electronic and (4) active electronic tasks. Near-viewing distance and the number of viewing breaks were calculated for each task. Statistical analyses included Student t-tests and the Mann-Whitney test between groups and repeated measures ANOVA or Friedman between tasks. For all tasks combined, a significantly shorter viewing distance was observed for the ultra-Orthodox group (36.2± 7.0cm) than for the non-ultra-Orthodox group (39.6± 6.7 cm, p< 0.05). Viewing distances for the passive reading and electronic tasks were shorter for the ultra-Orthodox group (36.9± 7.7cm vs. 41.3± 8.1cm, p< 0.03 and 39.0± 10.1 vs. 43.9± 9.3, p< 0.05, respectively). Viewing distances were significantly different between all four tasks, with writing having the closest distance. No correlation was found between working distance and spherical equivalent or Harmon distance. However, a significant correlation was found in the ultra-Orthodox group between working distance and height for each task (p< 0.04, R< 0.42 for all). There was no difference in the number of viewing breaks between the groups. When reading a book and viewing an iPad, ultra-Orthodox men demonstrated a closer objective working distance than non-ultra-Orthodox men. This shorter viewing distance may contribute to the high prevalence and degree of myopia in this population.

The Use of Hough Transform Method and Knot-Like Turning for Motion Planning and Control of an Autonomous Agricultural Vehicle

This study focuses on motion planning and reference trajectory tracking control of an autonomous agricultural vehicle to achieve precise row following and turning. The smooth time-varying feedback control method was adapted to the system to generate the required control commands. The mathematical representations for motion planning and controllers were constructed based on the car-like robot model. An algorithm to detect trees and rows of trees of an orchard was developed using the Hough transform approach. A new type of turning procedure, called knot-like turning, was proposed to perform turning from one row to another. A simulation environment was created to test and analyze the developed system. To obtain the real data from a field, the trees and rows of trees of a cherry orchard were scanned using a laser scanner rangefinder sensor. Then, the scanned data were moved to the simulation environment to generate the desired trajectory, which was followed by an autonomous agricultural vehicle. The simulation environment made it possible to determine the performance of the proposed motion planning, reference trajectory generation, tracking control and turning procedures. The results presented here indicate that the proposed methodology could be used for desired trajectory tracking tasks for agricultural operations in the case that minimum tracking errors in both straight and turning motions are needed.

ИСПОЛЬЗОВАНИЕ КОМПЛЕКСИРОВАННЫХ ДЕСКРИПТОРОВ В РЕШЕНИИ SLAM-ЗАДАЧИ

The actual problem of determining all six coordinates (three linear and three angular) of thecurrent position of a mobile robot (unmanned aerial vehicle) from video rangefinder images of theexternal environment (volumetric colored point clouds) formed by an onboard integrated visionsystem built on the basis of a 3D rangefinder sensor (lidar) and a color video camera while moving(flying) in an unknown environment is considered. An algorithm of video navigation based onthe use of complexed (video-rangefinder) descriptors is proposed, for the description of whichvisual and geometric parameters are used. The rules for the formation of a complex descriptor areformulated, which ensure the allocation of special (central) points of the descriptor using theSobel operator and the calculation of brightness and geometric parameters in its local area. Theaddition of the brightness parameters of the descriptor provided by the video camera with the geometricparameters provided by the rangefinder sensor removes the problem of invariance of thedescriptor to the scale and thereby significantly reduces the complexity of calculations when selectingit. The rules for finding complexed descriptors corresponding to each other in a sequenceof complexed images are described, based on calculating the difference in brightness and geometricparameters of the compared descriptors. The estimation of the error in solving the navigationproblem using the integrated descriptors was performed depending on the error of the sensors ofthe vision system and the geometric dimensions of the descriptor. By constructing histograms ofthe solution of the navigation problem for each coordinate of the control object for all pairs ofdescriptors corresponding to each other, a statistically stable high reliability of the solution of thecomplete navigation problem has been achieved. At the same time, the error in solving the navigationtask turned out to be an order of magnitude smaller than the error in the formation of compleximages by the technical vision system. The use of complex descriptors made it possible, with arelatively small amount of calculations, to solve the complete navigation problem with acceptableaccuracy, which provides a solution to the SLAM problem on the onboard computations at thepace of movement of the control object. The effectiveness of the proposed algorithmic and developedsoftware and hardware is confirmed by field experiments conducted in real conditions ofvarious environments.

Extended Kalman Filter Sensor Fusion in Practice for Mobile Robot Localization

Self-driving vehicles and autonomously guided robots could be very beneficial to today's civilization. However, the mobile robot's position must be accurately known, which referred as the localization with the task of tracking the dynamic position, in order for the robot to be active and useful. This paper presents a robot localization method with a known starting location by a real-time reconstructed environment model that represented as an occupancy grid map. The extended Kalman filter (EKF) is formulated as a nonlinear model-based estimator for fuse Odometry and a LIDAR range finder sensor. Because the occupancy grid map for the area is provided, just the inaccuracies of the LIDAR range finder will be considered. The experimental results on the “turtlebot” robot using robot operating system (ROS) show a significant improvement in the pose of the robot using the Kalman filter compared with sample Odometry. This paper also establishes the framework for using a Kalman filter for state estimation, providing all relevant mathematical equations for differential drive robot, this technique can be used to a variety of mobile robots.

Accumulator-free Hough Transform for Sequence Collinear Points

The perception, localization, and navigation of its environment are essential for autonomous mobile robots and vehicles. For that reason, a 2D Laser rangefinder sensor is used popularly in mobile robot applications to measure the origin of the robot to its surrounding objects. The measurement data generated by the sensor is transmitted to the controller, where the data is processed by one or multiple suitable algorithms in several steps to extract the desired information. Universal Hough Transform (UHT) is one of the appropriate and popular algorithms to extract the primitive geometry such as straight line, which later will be used in the further step of data processing. However, the UHT has high computational complexity and requires the so-called accumulator array, which is less suitable for real-time applications where a high speed and low complexity computation is highly demanded. In this study, an Accumulator-free Hough Transform (AfHT) is proposed to reduce the computational complexity and eliminate the need for the accumulator array. The proposed algorithm is validated using the measurement data from a 2D laser scanner and compared to the standard Hough Transform. As a result, the extracted value of AfHT shows a good agreement with that of UHT but with a significant reduction in the complexity of the computation and the need for computer memory.

Increasing measurement accuracy of a chickpea pile weight estimation tool using Moore-neighbor tracing algorithm in sphericity calculation

In this study, a new perspective for increasing the measurement accuracy of a chickpea pile volume and weight estimation tool is proposed. The system proposed uses the principles of machine learning methods and focuses on the adaptation of Moore-neighbor tracing algorithm in sphericity calculation of chickpeas. An experimental setup including a two degrees of freedom moving mechanism, a low-cost laser scanning rangefinder sensor, and a vision unit combining a camera and image processing algorithm is constructed. The methodology proposed is tested to estimate the weight of a chickpea pile using this experimental setup. In order to make comparisons, the estimations are also performed by the use of approaches proposed in the literature. The results of the experimental studies show that at least 5% weight estimation error is obtained when the estimation procedures given in the literature are used. On the other hand, the algorithm proposed in this study yields estimation error less than 0.57%. The details of the procedure proposed, the experimental setup designed and built, the computational environment developed and the experiments conducted are presented in this study.

Cemetery Mapping and Digital Data Analysis: A Case Study in Minnesota, USA

This study examines how geospatial technologies can be used in the aid of local-level cemetery management with limited resources using a case study in Woodland Hills Memorial Park Cemetery, Minnesota, USA. The hard-copy records in a handwritten ledger were manually transferred into an Excel table. The spatial data of the gravesites were collected using a Trimble Geo 7X unit with a Zephyr antenna and a Laser Rangefinder sensor over the summer of 2017. A geodatabase was constructed by joining the Excel table with the GPS data in GIS. A procedure was also developed to map the spatial distributions of plots and analyze the demographic data. It was demonstrated that a very high locational accuracy could be achieved based on carefully designed GPS data collection strategies. In addition, the data analysis results revealed that there were 12,190 plots in total, approximately half of which were still available for purchase. Among the 5,906 inhabitants buried at the Woodland Hills, many were ethnically German and Scandinavian, of whom 9.7% were veterans and nearly half were from the Greatest Generation (born between 1901 and 1927). The birth, death, and age distributions are significantly different between the nonveteran and veteran groups. Clustered patterns were identified for the filled plots and all the Generation categories. Such results will be beneficial to local cemetery managers to plan for further development as well as to future historians or individuals interested in the local culture and history. The proposed methods can greatly facilitate local-level cemetery data collection, mapping, query, and analysis.

The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)

The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor is categorized as a low-cost sensor that is widely used in various applications. This sensor has a significant deviation that leads to significant errors in the measurement result. The error produced by this sensor tends to increase proportionally to the measured distance. The implementation of a particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The length of the usage of the sensor leads to sensor fatigue. Sensor fatigue is one of the causes of model state changes. If the drift is still within the tolerance limit, the sensor performance can still be restored using the calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model-making process is not an easy task, time, and cost required, then the Newton polynomial-based (Automatic Model Generation (AMG) has been implemented in this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of a polynomial-based AMG algorithm for sensor calibration has been succeeded in improving the calibrated sensor’s accuracy by 96.4% and reducing the MSE level from 25.6 to 0.914

3-D Dense Rangefinder Sensor With a Low-Cost Scanning Mechanism

LiDAR sensors have been widely applied in autonomous robotics and autonomous systems. High-channel LiDARs or multiple low-channel LiDARs are adopted in these applications to overcome the poor vertical resolution of point clouds, as this scenario can lead to high costs. Here, as a means to improve the vertical resolution of point clouds and lower the cost, we present a 3-D dense rangefinder sensor composed of a low-channel LiDAR, a camera, a brush-less motor, and a crank-link system to replace the traditional LiDAR. A special registration method is designed to register the high-dynamic point cloud. The measurement uncertainty of this method is analyzed. In addition, a 3-D object detection method is used to obtain the 3-D pose of obstacles by combining the dense point cloud and an image-based 2-D object detection algorithm. Finally, several experiments are performed to evaluate the effectiveness of the proposed 3-D rangefinder sensor.

Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way. The artificial intelligent agent, which is equipped with multiple rangefinder sensors for obstacle detection, is trained and evaluated in a challenging, stochastically generated simulation environment based on the OpenAI gym python toolkit. Notably, the agent is provided with real-time insight into its own reward function, allowing it to dynamically adapt its guidance strategy. Depending on its strategy, which ranges from radical path-adherence to radical obstacle avoidance, the trained agent achieves an episodic success rate between 84 and 100%.

Калібрування ультразвукового сенсора-далекоміра з допомогою лазерного інтерферометра

Калібрування ультразвукового сенсора-далекоміра з допомогою лазерного інтерферометра

The Performance Improvement of the Low-Cost Ultrasonic Range Finder (HC-SR04) Using Newton’s Polynomial Interpolation Algorithm

The ultrasonic range finder sensors are widely used sensor in many applications such as computer applications, general purpose applications, medical applications, automotive applications and industrial grade applications. The ultrasonic range finder sensor has many advantages. The advantages are easy to use, fast in measuring process, non-contact measurement and suitable for air and underwater environment. However, the ultrasonic range finder has deviation especially for low-cost sensor. It affects the accuracy level of the measurement result that performed by its sensor directly. The HC-SR04 categorized as a low-cost ultrasonic range finder sensor. This sensor has significant error level. The improvement of the accuracy level of this low-cost ultrasonic sensor is expected to this research. The Newton’s polynomial interpolation algorithm has been used in this research to reduce the error during the measurement process. The implementation of Newton’s polynomial interpolation has succeeded to improve the sensor accuracy. The MSE level of 29,96 is obtained without the Newton’s Polynomial Interpolation implementation. The implementation of the Newton’s Polynomial Interpolation algorithm has succeeded to increase the accuracy level of the sensor by 55,54%. It has been proofed by the decrease of MSE level by 13,32.

Analysis of Mobile Robot Indoor Mapping using GMapping Based SLAM with Different Parameter

Mapping is one of the elemental application of the mobile robot. The map is created using the mobile robot by employing sensors such as camera, sonar and laser sensor. One of the most popular mapping methods is the Simultaneous Localization and Mapping (SLAM). SLAM allows the map to be created while localizing the robot location in the map at the same time. GMapping is one of the widely used algorithms in SLAM which will be used in this project. The mobile robot is equipped with a Hokuyo Laser Range Finder sensor and netbook. The router is used for wireless communication between the mobile robot and the user. The GMapping is done in two different locations of different lab size and amount of features in the area. Three trial is conducted to investigate the effects of different parameters such as robot speed, mapping delay and particle filter on the mapping quality. The results show a significant difference in terms of mapping accuracy and the time taken to complete the process as the parameter changed from the three trial. As a result, the parameter used in the second trial, robot speed 0.1333m/s, mapping delay 1s and particle filter 30 is considered as the best based on the time taken and the map accuracy.

Human-Machine Interface for a Smart Wheelchair

The paper describes the integration of hardware and software with sensor technology and computer processing to develop the next generation intelligent wheelchair. The focus is a computer cluster design to test high performance computing for smart wheelchair operation and human interaction. The LabVIEW cluster is developed for real-time autonomous path planning and sensor data processing. Four small form factor computers are connected over a Gigabit Ethernet local area network to form the computer cluster. Autonomous programs are distributed across the cluster for increased task parallelism to improve processing time performance. The distributed programs operating frequency for path planning and motion control is 50Hz and 12.3Hz for 0.3 megapixel robot vision system. To monitor the operation and control of the distributed LabVIEW code, network automation is integrated into the cluster software along with a performance monitor. A link between the computer motion control program and the wheelchair joystick control of the drive train is developed for the computer control interface. A perception sensor array and control circuitry is integrated with the computer system to detect and respond to the wheelchair environment. Multiple cameras are used for image processing and scanning laser rangefinder sensors for obstacle avoidance in the cluster program. A centralized power system is integrated to power the smart wheelchair along with the cluster and sensor feedback system. The on board computer system is evaluated for cluster processing performance for the smart wheelchair, incorporating camera machine vision and LiDAR perception for terrain obstacle detection, operating in urban scenarios.

Driving Assistant Companion With Voice Interface Using Long Short-Term Memory Networks

In this paper, we propose a driving assistant companion system that provides drivers with useful information using a long short-term memory network in a data-driven fashion. Our system can be viewed as an advanced driver-assistance system for faster learning and better driving. The assistant companion predicts upcoming events on the road by using real-time sensory measurements from range finding sensors and provides informative narrations to enhance learnability and driving performance. In contrast to a conventional navigation system, our system predicts events from the online stream of sensory measurements without resorting to priors and map information. To demonstrate the effectiveness of the proposed system, we implemented our system on The Open Racing Car Simulator and conducted an experimental study with 16 human drivers. Experimental results show that our system enhanced learnability, driving performance, and reliability.

Cooperative Robot Exploration Strategy Using an Efficient Backtracking Method for Multiple Robots

This paper presents a cooperative robot exploration (CRE) strategy that is based on the sensor-based random tree (SRT) star method. The CRE strategy is utilized for a team of mobile robots equipped with range finding sensors. Existing backtracking techniques for frontier-based (FB) exploration involve moving back thorough the previous position where the robot has passed before. However, in some cases, the robot generates inefficient detours to move back to the position that contains frontier areas. In an effort to improve upon movement and energy efficiencies, this paper proposes the use of a hub node that has a frontier arc; thereby, the robots backtrack more directly to hub nodes by using the objective function. Furthermore, each robot cooperatively explores the workspace utilizing the data structure from the entire team of robots, which consists of configuration data and frontier data. Comparative simulations of the proposed algorithm and the existing SRT-star algorithm are implemented and described. The experiment is presented to demonstrate the application of the proposed strategy in real-time. Utilizing the proposed algorithm and exploration strategy, the results indicate that a team of robots can work more efficiently by reducing the distance of exploration and the number of node visited.

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Abstract Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.

Tasarlanmış Engebeli Ortamda Otonom Mobil Robot Gezinimi

Main study areas for robotics research can be given as: mapping, localization, navigation and exploration. Given a robot’s current position, partial map of the environment and a goal position; navigation problem can be defined as optimal path planning and path following. Path planning and path following problem should be handled according to environment being static or dynamic, robot's mobility capabilities, sensors used on the robot and the roughness of the environment. In the study a four wheeled, skid-steering robot with laser range finder and depth sensor is built for Gazebo simulation environment. Also a statically structured labyrinth that consists of 15 degree continuous ramps, 15 degree discontinuous ramps, amorphous holes that robot cannot autonomously escape from if fallen into, walls and discontinuous obstacles that are below the robot laser height. 2D simultaneous localization and mapping, 3D mapping, path planning and path following with respect to the 3D map are implemented on Robot Operating System (ROS). Optimal path planning in rough terrain is accomplished by combining A* heuristic with a function of height difference of the 3D map nodes. Path following is carried out by turning-to and moving-towards actions on each sequential path node pairs. Tests performed on the labyrinth shows that obstacle avoidance, path planning and path following can be carried out successfully with the given implementation.