Laser-scanning rangefinder fixed on a gimbal with two degrees of freedom

A laser-scanning rangefinder mounted on a gimbal with two degrees of freedom is presented. The rangefinder can be used as part of a navigation system of an unmanned aerial vehicle to avoid obstacles or prevent collisions. Compared to stereo cameras, the device requires significantly less computing resources and is less dependent on lighting conditions. Compared to integrated lidars, the cost of the device is by an order lower. А model of the device was developed and an obstacle avoidance flight was simulated in the Gazebo simulator. The PX4/Avoidance software was used as an autopilot. As a result of a model experiment, we found that a scanning laser rangefinder can provide autonomous navigation with obstacle avoidance.

Hybrid Control of a Segway Platform Developed in MRDS

<p>A Segway RMP200 has been bought by Victoria University for the purpose of making an autonomous robot. The focus of this project was to create reusable services that use existing navigation algorithms to control the Segway within an indoor environment. A SICK LMS100 laser rangefinder was added to detect obstacles and allow localization of the Segway within a known map. A hybrid navigation algorithm consisting of an A* path planner with a dynamic window is used for motion planning and obstacle avoidance. The control system followed a Service Oriented Architecture implemented in Microsoft Robotics Studio using the C# .NET programming language. Four services were created during the project to interface with the SICK LMS100 scanner, control the Segway RMP200, implement the hybrid navigation algorithm and provide a graphic user interface for the system. Tests show that the Segway is able to navigate and maintain localisation within the operating environment by identifying and associating corner and door landmarks within the environment.</p>

Hybrid Control of a Segway Platform Developed in MRDS

<p>A Segway RMP200 has been bought by Victoria University for the purpose of making an autonomous robot. The focus of this project was to create reusable services that use existing navigation algorithms to control the Segway within an indoor environment. A SICK LMS100 laser rangefinder was added to detect obstacles and allow localization of the Segway within a known map. A hybrid navigation algorithm consisting of an A* path planner with a dynamic window is used for motion planning and obstacle avoidance. The control system followed a Service Oriented Architecture implemented in Microsoft Robotics Studio using the C# .NET programming language. Four services were created during the project to interface with the SICK LMS100 scanner, control the Segway RMP200, implement the hybrid navigation algorithm and provide a graphic user interface for the system. Tests show that the Segway is able to navigate and maintain localisation within the operating environment by identifying and associating corner and door landmarks within the environment.</p>

Development of a Mobile Platform with IoT for LIDAR

Nowadays, interior scanning is very significant in mobile robot applications because it helps humans to monitor, study and investigate the environments, especially the challenging ones with clutter, hazardous, unexplored, dynamic, and others. A LIDAR can be utilised to perform the task; however, suitable mobile platform should be accompanying it to allow mobility while collecting data representing the environment. This paper shows the process of developing a suitable mobile platform for a low-cost laser rangefinder, RP LIDAR from SLAMTEC with its scanning performance. The mobile platform used in this project is an Arduino Uno R3 4WD mobile robot. L298 H-Bridge IC and HC-05 Bluetooth module is implemented on the robot to make the user able to control its movement through a mobile application. MIT app inventor is used to develop the mobile application. Through the application, user can control the mobile robot with the LIDAR to move around the selected indoor area to scan its environment. To show the scanning results, MATLAB will be used to plot the map. Analysis from the plotting show good mapping results and similar to the real environment, thus presenting its potential to be used in the above-mentioned situations.

Method of automatic correction of neutron monitor data for precipitation in the form of snow in real time

We have carried out an experimental study of the influence of precipitation in the form of snow on measurements of the neutron flux intensity near Earth's surface. We have examined the state of the snow cover and its density, and found out that the density depends on the depth of the snow cover. Using the experimental results, we estimate the neutron absorption path in the snow. Changes in snow cover by 10–12 cm at a depth of 80 cm are shown to cause variations in the monitor count rate with an amplitude of 0.9 %. At the snow depth of 80 cm, the neutron monitor count rate decreases by about 8 %. The observed variations should be attributed to the meteorological effects of cosmic rays. The absorption coefficient of neutrons in the snow was also found from the correlation between the count rate of the neutron monitor and the amount of snow above the detector. We propose a real-time correction of the neutron monitor data for precipitation in the form of snow. For this purpose, we implement continuous monitoring of the amount of snow cover. The monitoring is provided by a snow meter made using a laser rangefinder module. We discuss the results obtained.

Method of automatic correction of neutron monitor data for precipitation in the form of snow in real time

We have carried out an experimental study of the influence of precipitation in the form of snow on measurements of the neutron flux intensity near Earth's surface. We have examined the state of the snow cover and its density, and found out that the density depends on the depth of the snow cover. Using the experimental results, we estimate the neutron absorption path in the snow. Changes in snow cover by 10–12 cm at a depth of 80 cm are shown to cause variations in the monitor count rate with an amplitude of 0.9 %. At the snow depth of 80 cm, the neutron monitor count rate decreases by about 8 %. The observed variations should be attributed to the meteorological effects of cosmic rays. The absorption coefficient of neutrons in the snow was also found from the correlation between the count rate of the neutron monitor and the amount of snow above the detector. We propose a real-time correction of the neutron monitor data for precipitation in the form of snow. For this purpose, we implement continuous monitoring of the amount of snow cover. The monitoring is provided by a snow meter made using a laser rangefinder module. We discuss the results obtained.

Methods of Precise Distance Measurements for Laser Rangefinders with Digital Acquisition of Signals

The article presents methods of long range distance measurements using pulsed lasers and the Time of Flight principle. Various algorithms of laser distance measurements with digital acquisition of echo pulses (acquisition of a signal’s full waveform) are presented. The main focus of work is concentrated on the method of distance measurements developed by the authors. With this method, during laboratory trials, a total measurement error of one centimeter was achieved using a 905 nm pulsed laser diode and pulse width of 39 ns. The maximum range of measurements with such high precision is limited only by a signal to noise ratio, duration of measurements and atmospheric conditions. All algorithms were implemented in a laser rangefinder module developed by the authors. Simulations and laboratory experiments were conducted and algorithm’s accuracy and precision were tested for various SNR conditions and changing distances.

Improve the Estimation of Monocular Vision 6-DOF Pose Based on the Fusion of Camera and Laser Rangefinder

Monocular vision is one of the most commonly used noncontact six-degrees-of-freedom (6-DOF) pose estimation methods. However, the large translational DOF measurement error along the optical axis of the camera is one of its main weaknesses, which greatly limits the measurement accuracy of monocular vision measurement. In this paper, we propose a novel monocular camera and 1D laser rangefinder (LRF) fusion strategy to overcome this weakness and design a remote and ultra-high precision cooperative targets 6-DOF pose estimation sensor. Our approach consists of two modules: (1) a feature fusion module that precisely fuses the initial pose estimated from the camera and the depth information obtained by the LRF. (2) An optimization module that optimizes pose and system parameters. The performance of our proposed 6-DOF pose estimation method is validated using simulations and real-world experiments. The experimental results show that our fusion strategy can accurately integrate the information of the camera and the LRF. Further optimization carried out on this basis effectively reduces the measurement error of monocular vision 6-DOF pose measurement. The experimental results obtained from a prototype show that its translational and rotational DOF measurement accuracy can reach up to 0.02 mm and 15″, respectively, at a distance of 10 m.