Institute of Fundamental Technological Research

W artykule przedstawiono projekt autonomicznego robota inspekcyjnego. Ze względu na fakt, że komercyjne rozwiązania nie oferują satysfakcjonującej funkcjonalności w stosunkowo niskiej cenie zdecydowano się zaprojektować autonomicznego robota inspekcyjnego na bazie komercyjnej platformy wyposażonej w autorskie rozwiązanie laserowego systemu pomiarowego 3D. Projekt lasera 3D wykonano z wykorzystaniem technik szybkiego prototypowania metodą druku 3D. Autonomiczny robot mobilny nawigowany jest na podstawie systemu IMU (Inertial Measurement Unit) ze zintegrowanym GPS (Global Positioning System). Opracowane rozwiązanie dostarcza użytkownikowi danych w postaci map lokalnych 3D wraz z częściową analizą semantyczną (obliczanie wektorów normalnych dla chmury punktów metodą PCA Principal Component Analysis) w trybie on-line. Przeprowadzono eksperymenty weryfikujące poprawność działania systemu. W rezultacie powstało nowoczesne stanowisko badawcze, które może być wykorzystane do kolejnych badań z wykorzystaniem mobilnych systemów inspekcyjnych.

robot inspekcyjny, laserowy system pomiarowy 3D, PCA (Principal Component Analysis)

Purpose – The purpose of this paper is to demonstrate a real time 3D localization and mapping approach for the USAR (Urban Search and Rescue) robotic application, focusing on the performance and the accuracy of the General-purpose computing on graphics processing units (GPGPU)-based iterative closest point (ICP) 3D data registration implemented using modern GPGPU with FERMI architecture. Design/methodology/approach – The authors put all the ICP computation into GPU, and performed the experiments with registration up to 106 data points. The main goal of the research was to provide a method for real-time data registration performed by a mobile robot equipped with commercially available laser measurement system 3D. The main contribution of the paper is a new GPGPU based ICP implementation with regular grid decomposition. It guarantees high accuracy as equivalent CPU based ICP implementation with better performance. Findings – The authors have shown an empirical analysis of the tuning of GPUICP parameters for obtaining much better performance (acceptable level of the variance of the computing time) with minimal lost of accuracy. Loop closing method is added and demonstrates satisfactory results of 3D localization and mapping in urban environments. This work can help in building the USAR mobile robotic applications that process 3D cloud of points in real time. Practical implications – This work can help in developing real time mapping for USAR robotic applications. Originality/value – The paper proposes a new method for nearest neighbor search that guarantees better performance with minimal loss of accuracy. The variance of computational time is much less than SoA.

Robotics, Search and rescue, Mapping, Data handling, Data registration, Point to point, Iterative closest point, General-purpose computing on graphics processing units

The paper concerns the research related to the improvement of control and supervision of web connected mobile robots using Physic Processing Unit (PPU). PPU computations taken into the consideration include rigid body dynamics, collision detection and raycasting. The result is improved by Human Machine Interface that allows performing semantic simulation during multi robot task execution. Semantic simulation engine provides tools to implement the mobile robot simulation, which is based on real data delivered by robot’s observations in INDOOR environment. The supervision of real objects such as robots is performed by association with its virtual representation in the simulation, therefore events such as object intersection, robot orientation - pitch and roll are able to be monitored. The simulation can be integrated with real part of the system with an assumption of robust localization of real entities, therefore Augmented Reality capabilities are available.

semantic mapping, Human Machine Interface, mobile robot

W artykule przedstawiono mobilny system tworzenia przestrzennej dokumentacji semantycznej. Zaproponowano nową metodę filtracji oraz rejestracji danych wykorzystującą obliczenia równoległe (NVIDIA FERMI). Opracowany system informatyczny umożliwia gromadzenie danych przestrzennych z wykorzystaniem geodezyjnego systemu pomiarowego 3D oraz pozwala na etykietowanie obiektów. Tworzona mapa semantyczna jest dostępna z poziomu dowolnego urządzenia mobilnego (laptop, smartphone, tablet).

mapa semantyczna, przestrzenna dokumentacja semantyczna, skanowanie laserowe, chmura punktów, obliczenia równoległe, wizualizacja

The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of data registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous environments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demonstrated an experiment in real large environment. We compared two strategies of data alingment - simple ICP and ICP using so called meta scan.

6D SLAM, parallel computation

This paper concentrates on the topic of gathering, processing and presenting 3D data for use in Search and Rescue operations. The data are gathered by unmanned ground platforms, in form of 3D point clouds. The clouds are matched and transformed into a consistent, highly accurate 3D model. The paper describes the pipeline for such matching based on Iterative Closest Point algorithm supported by loop closing done with LUM method. The pipeline was implemented for parallel computation with Nvidia CUDA, which leads to higher matching accuracy and lower computation time. An analysis of performance for multiple GPUs is presented. The second problem discussed in the paper is immersive visualization of 3d data for search and rescue personnel. Five strategies are discussed: plain 3D point cloud, hypsometry, normal vectors, space descriptors and an approach based on light simulation through the use of NVIDIA OptiX Ray Tracing Engine. The results from each strategy were shown to end users for validation. The paper discusses the feedback given. The results of the research are used in the development of a support module for ICARUS project.

Three-dimensional displays, Data visualization, Graphics processing units, Image color analysis, Computational modeling, Solid modeling, Pipelines

This article concerns current progress in the development of multi robot simulation for TIRAMISU project. This simulator is designed for training of UGV (Unmanned Ground Vehicles) operators in cooperative mission execution. The core components of the system are implemented using VORTEX physics simulation engine with OSG (Open Scene Graph) used for rendering. The engine provides an accurate physics simulation for robots working on a single stage. The main goal during development was to prepare a multi robot architecture for the simulation. The challenge was to integrate all simulation components into a common framework, therefore allowing the robots to interact with each other, without lose of simulation accuracy. Current version of the simulator has two types of robots: a) iRobot-PacBot b)LOCSTRA - a TIRAMISU robot for humanitarian demining. An example of multi robot scenario, transportation of UXO (UneXploded Ordnance), will be discussed.

Humanitarian demining, mobile robot simulation, operator training