Online Searching with an Autonomous Robot

We discuss online strategies for visibility-based searching for an object hidden behind a corner, using Kurt3D, a real autonomous mobile robot. This task is closely related to a number of well-studied problems. Our robot uses a three-dimensional laser scanner in a stop, scan, plan, go fashion for building a virtual three-dimensional environment. Besides planning trajectories and avoiding obstacles, Kurt3D is capable of identifying objects like a chair. We derive a practically useful and asymptotically optimal strategy that guarantees a competitive ratio of 2, which differs remarkably from the well-studied scenario without the need of stopping for surveying the environment. Our strategy is used by Kurt3D, documented in a separate video.Comment: 16 pages, 8 figures, 12 photographs, 1 table, Latex, submitted for publicatio

Localization And Mapping Of Unknown Locations And Tunnels With Unmanned Ground Vehicles

The main goals of this research were to enhance a commercial off the shelf (COTS) software platform to support unmanned ground vehicles (UGVs) exploring the complex environment of tunnels, to test the platform within a simulation environment, and to validate the architecture through field testing. Developing this platform will enhance the U. S. Army Engineering Research and Development Center’s (ERDC’s) current capabilities and create a safe and efficient autonomous vehicle to perform the following functions within tunnels: (1) localization (e.g., position tracking) and mapping of its environment, (2) traversing varied terrains, (3) sensing the environment for objects of interest, and (4) increasing the level of autonomy of UGVs available at the ERDC. The simulation experiments were performed in the STAGE Simulator, a physics-based multi-scale numerical test bed developed by Robotic Operating System (ROS). Physical testing was conducted in Vicksburg, MS using a Coroware Explorer. Both the simulation and physical testing evaluated three SLAM algorithms, i.e., Hector SLAM, gMapping, and CORESLAM to determine the superior algorithm. The superior algorithm was then used to localize the robot to the environment and autonomously travel from a start location to a destination location. Completion of this research has increased the ERDC’s level of autonomy for UGVs from tether to tele-operated to autonomous

Fast 3D cluster tracking for a mobile robot using 2D techniques on depth images

User simultaneous detection and tracking is an issue at the core of human-robot interaction (HRI). Several methods exist and give good results; many use image processing techniques on images provided by the camera. The increasing presence in mobile robots of range-imaging cameras (such as structured light devices as Microsoft Kinects) allows us to develop image processing on depth maps. In this article, a fast and lightweight algorithm is presented for the detection and tracking of 3D clusters thanks to classic 2D techniques such as edge detection and connected components applied to the depth maps. The recognition of clusters is made using their 2D shape. An algorithm for the compression of depth maps has been specifically developed, allowing the distribution of the whole processing among several computers. The algorithm is then applied to a mobile robot for chasing an object selected by the user. The algorithm is coupled with laser-based tracking to make up for the narrow field of view of the range-imaging camera. The workload created by the method is light enough to enable its use even with processors with limited capabilities. Extensive experimental results are given for verifying the usefulness of the proposed method.Spanish MICINN (Ministry of Science and Innovation) through the project ‘‘Applications of Social Robots=Aplicaciones de los Robots Sociales.’’Publicad

Path Tracking of a Wheeled Mobile Manipulator through Improved Localization and Calibration

This chapter focuses on path tracking of a wheeled mobile manipulator designed for manufacturing processes such as drilling, riveting, or line drawing, which demand high accuracy. This problem can be solved by combining two approaches: improved localization and improved calibration. In the first approach, a full-scale kinematic equation is derived for calibration of each individual wheel’s geometrical parameters, as opposed to traditionally treating them identical for all wheels. To avoid the singularity problem in computation, a predefined square path is used to quantify the errors used for calibration considering the movement in different directions. Both statistical method and interval analysis method are adopted and compared for estimation of the calibration parameters. In the second approach, a vision-based deviation rectification solution is presented to localize the system in the global frame through a number of artificial reflectors that are identified by an onboard laser scanner. An improved tracking and localization algorithm is developed to meet the high positional accuracy requirement, improve the system’s repeatability in the traditional trilateral algorithm, and solve the problem of pose loss in path following. The developed methods have been verified and implemented on the mobile manipulators developed by Shanghai University

The simultaneous localization and mapping (SLAM):An overview

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality

Percepcija u inteligentnim prostorima: kombinirana primjena distribuiranih i robotskih senzora

This work considers the joint use of robot onboard sensors and a network of sensors distributed in the environment for tracking the position of the robot and other objects. This is motivated by our research on Intelligent Spaces, which combine the use of distributed sensors with mobile robots to provide various services to users. Here we analyze the distributed sensing using the extended information filter and computation issues that arise due to correlations between estimates. In turn we show how the correlations can be resolved with the use of Covariance Intersection at a cost of conservative estimates, and analyze two special cases where the issues related to correlations can be reduced.Ovaj rad razmatra kombiniranu primjenu senzora na mobilnim robotima i mreže senzora distribuiranih u prostoru za praćenje položaja robota i ostalih objekata. Rad je dio istraživanja o "inteligentnim prostorima", gdje se koriste distribuirani senzori i mobilni roboti sa svrhom pružanja različitih usluga korisnicima prostora. Analizirana je upotreba proširenog informacijskog filtra za distribuiranu percepciju te računski problem uzrokovan korelacijama u procesu estimacije. Potom je objašnjeno rješenje problema korelacija korištenjem metode presjeka kovarijanci (Covariance Intersection), koje međutim daje konzervativne rezultate, te je dana analiza dva specijalna slučaja kod kojih je moguće ublažiti utjecaj korelacija

A review of sensor technology and sensor fusion methods for map-based localization of service robot

Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robot’s own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications