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Remote and Autonomous Controlled Robotic Car Based on Arduino with Real Time Obstacle Detection and Avoidance
Universal Journal of Engineering Science 7(1): 1-7, 2019 http://www.hrpub.org DOI: 10.13189/ujes.2019.070101 Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance Esra Yılmaz, Sibel T. Özyer* Department of Computer Engineering, Çankaya University, Turkey Copyright©2019 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract In robotic car, real time obstacle detection electronic devices. The environment can be any physical and obstacle avoidance are significant issues. In this study, environment such as military areas, airports, factories, design and implementation of a robotic car have been hospitals, shopping malls, and electronic devices can be presented with regards to hardware, software and smartphones, robots, tablets, smart clocks. These devices communication environments with real time obstacle have a wide range of applications to control, protect, image detection and obstacle avoidance. Arduino platform, and identification in the industrial process. Today, there are android application and Bluetooth technology have been hundreds of types of sensors produced by the development used to implementation of the system. In this paper, robotic of technology such as heat, pressure, obstacle recognizer, car design and application with using sensor programming human detecting. Sensors were used for lighting purposes on a platform has been presented. This robotic device has in the past, but now they are used to make life easier. been developed with the interaction of Android-based Thanks to technology in the field of electronics, incredibly device. -
Human to Robot Whole-Body Motion Transfer
Human to Robot Whole-Body Motion Transfer Miguel Arduengo1, Ana Arduengo1, Adria` Colome´1, Joan Lobo-Prat1 and Carme Torras1 Abstract— Transferring human motion to a mobile robotic manipulator and ensuring safe physical human-robot interac- tion are crucial steps towards automating complex manipu- lation tasks in human-shared environments. In this work, we present a novel human to robot whole-body motion transfer framework. We propose a general solution to the correspon- dence problem, namely a mapping between the observed human posture and the robot one. For achieving real-time imitation and effective redundancy resolution, we use the whole-body control paradigm, proposing a specific task hierarchy, and present a differential drive control algorithm for the wheeled robot base. To ensure safe physical human-robot interaction, we propose a novel variable admittance controller that stably adapts the dynamics of the end-effector to switch between stiff and compliant behaviors. We validate our approach through several real-world experiments with the TIAGo robot. Results show effective real-time imitation and dynamic behavior adaptation. Fig. 1. Transferring human motion to robots while ensuring safe human- This constitutes an easy way for a non-expert to transfer a robot physical interaction can be a powerful and intuitive tool for teaching assistive tasks such as helping people with reduced mobility to get dressed. manipulation skill to an assistive robot. The classical approach for human motion transfer is I. INTRODUCTION kinesthetic teaching. The teacher holds the robot along the Service robots may assist people at home in the future. trajectories to be followed to accomplish a specific task, However, robotic systems still face several challenges in while the robot does gravity compensation [6]. -
AUV Adaptive Sampling Methods: a Review
applied sciences Review AUV Adaptive Sampling Methods: A Review Jimin Hwang 1 , Neil Bose 2 and Shuangshuang Fan 3,* 1 Australian Maritime College, University of Tasmania, Launceston 7250, TAS, Australia; [email protected] 2 Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada; [email protected] 3 School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China * Correspondence: [email protected] Received: 16 July 2019; Accepted: 29 July 2019; Published: 2 August 2019 Abstract: Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed. Keywords: autonomous underwater vehicle(s); maritime robotics; adaptive sampling; underwater feature tracking; in-situ sensors; sensor fusion 1. Introduction Autonomous underwater vehicles (AUVs) are unmanned marine robots. Owing to their mobility and increased ability to accommodate sensors, they have been used for a broad range of oceanographic missions, such as surveying underwater plumes and other phenomena, collecting bathymetric data and tracking oceanographic dynamic features. -
Development of an Open Humanoid Robot Platform for Research and Autonomous Soccer Playing
Development of an Open Humanoid Robot Platform for Research and Autonomous Soccer Playing Karl J. Muecke and Dennis W. Hong RoMeLa: Robotics and Mechanisms Lab Virginia Tech Blacksburg, VA 24061 [email protected], [email protected] Abstract This paper describes the development of a fully autonomous humanoid robot for locomotion research and as the first US entry in to RoboCup. DARwIn (Dynamic Anthropomorphic Robot with Intelligence) is a humanoid robot capable of bipedal walking and performing human like motions. As the years have progressed, DARwIn has evolved from a concept to a sophisticated robot platform. DARwIn 0 was a feasibility study that investigated the possibility of making a humanoid robot walk. Its successor, DARwIn I, was a design study that investigated how to create a humanoid robot with human proportions, range of motion, and kinematic structure. DARwIn IIa built on the name ªhumanoidº by adding autonomy. DARwIn IIb improved on its predecessor by adding more powerful actuators and modular computing components. Finally, DARwIn III is designed to take the best of all the designs and incorporate the robot's most advanced motion control yet. Introduction Dynamic Anthropomorphic Robot with Intelligence (DARwIn), a humanoid robot, is a sophisticated hardware platform used for studying bipedal gaits that has evolved over time. Five versions of DARwIn have been developed, each an improvement on its predecessor. The first version, DARwIn 0 (Figure 1a), was used as a design study to determine the feasibility of creating a humanoid robot abd for actuator evaluation. The second version, DARwIn I (Figure 1b), used improved gaits and software. -
Autonomous Mobile Robots Annual Report 1985
The Mobile Robot Laboratory Sonar Mapping, Imaging and Navigation Visual Navigation Road Following i Motion Control The Robots Motivation Publications Autonomous Mobile Robots Annual Report 1985 Mobile Robot Laboratory CM U-KI-TK-86-4 Mobile Robot Laboratory The Robotics Institute Carncgie Mcllon University Pittsburgh, Pcnnsylvania 15213 February 1985 Copyright @ 1986 Carnegie-Mellon University This rcscarch was sponsored by The Office of Naval Research, under Contract Number NO00 14-81-K-0503. 1 Table of Contents The Mobile Robot Laboratory ‘I’owards Autonomous Vchiclcs - Moravcc ct 31. 1 Sonar Mapping, Imaging and Navigation High Resolution Maps from Wide Angle Sonar - Moravec, et al. 19 A Sonar-Based Mapping and Navigation System - Elfes 25 Three-Dirncnsional Imaging with Cheap Sonar - Moravec 31 Visual Navigation Experiments and Thoughts on Visual Navigation - Thorpe et al. 35 Path Relaxation: Path Planning for a Mobile Robot - Thorpe 39 Uncertainty Handling in 3-D Stereo Navigation - Matthies 43 Road Following First Resu!ts in Robot Road Following - Wallace et al. 65 A Modified Hough Transform for Lines - Wallace 73 Progress in Robot Road Following - Wallace et al. 77 Motion Control Pulse-Width Modulation Control of Brushless DC Motors - Muir et al. 85 Kinematic Modelling of Wheeled Mobile Robots - Muir et al. 93 Dynamic Trajectories for Mobile Robots - Shin 111 The Robots The Neptune Mobile Robot - Podnar 123 The Uranus Mobile Robot, a First Look - Podnar 127 Motivation Robots that Rove - Moravec 131 Bibliography 147 ii Abstract Sincc 13s 1. (lic Mobilc I<obot IAoratory of thc Kohotics Institute 01' Crrrncfiic-Mcllon IInivcnity has conduclcd hsic rcscarcli in aTCiiS crucial fur ;iiitonomoiis robots. -
The Autonomous Underwater Vehicle Initiative – Project Mako
Published at: 2004 IEEE Conference on Robotics, Automation, and Mechatronics (IEEE-RAM), Dec. 2004, Singapore, pp. 446-451 (6) The Autonomous Underwater Vehicle Initiative – Project Mako Thomas Bräunl, Adrian Boeing, Louis Gonzales, Andreas Koestler, Minh Nguyen, Joshua Petitt The University of Western Australia EECE – CIIPS – Mobile Robot Lab Crawley, Perth, Western Australia [email protected] Abstract—We present the design of an autonomous underwater vehicle and a simulation system for AUVs as part II. AUV COMPETITION of the initiative for establishing a new international The AUV Competition will comprise two different competition for autonomous underwater vehicles, both streams with similar tasks physical and simulated. This paper describes the competition outline with sample tasks, as well as the construction of the • Physical AUV competition and AUV and the simulation platform. 1 • Simulated AUV competition Competitors have to solve the same tasks in both Keywords—autonomous underwater vehicles; simulation; streams. While teams in the physical AUV competition competition; robotics have to build their own AUV (see Chapter 3 for a description of a possible entry), the simulated AUV I. INTRODUCTION competition only requires the design of application Mobile robot research has been greatly influenced by software that runs with the SubSim AUV simulation robot competitions for over 25 years [2]. Following the system (see Chapter 4). MicroMouse Contest and numerous other robot The competition tasks anticipated for the first AUV competitions, the last decade has been dominated by robot competition (physical and simulation) to be held around soccer through the FIRA [6] and RoboCup [5] July 2005 in Perth, Western Australia, are described organizations. -
Design and Realization of a Humanoid Robot for Fast and Autonomous Bipedal Locomotion
TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Angewandte Mechanik Design and Realization of a Humanoid Robot for Fast and Autonomous Bipedal Locomotion Entwurf und Realisierung eines Humanoiden Roboters für Schnelles und Autonomes Laufen Dipl.-Ing. Univ. Sebastian Lohmeier Vollständiger Abdruck der von der Fakultät für Maschinenwesen der Technischen Universität München zur Erlangung des akademischen Grades eines Doktor-Ingenieurs (Dr.-Ing.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr.-Ing. Udo Lindemann Prüfer der Dissertation: 1. Univ.-Prof. Dr.-Ing. habil. Heinz Ulbrich 2. Univ.-Prof. Dr.-Ing. Horst Baier Die Dissertation wurde am 2. Juni 2010 bei der Technischen Universität München eingereicht und durch die Fakultät für Maschinenwesen am 21. Oktober 2010 angenommen. Colophon The original source for this thesis was edited in GNU Emacs and aucTEX, typeset using pdfLATEX in an automated process using GNU make, and output as PDF. The document was compiled with the LATEX 2" class AMdiss (based on the KOMA-Script class scrreprt). AMdiss is part of the AMclasses bundle that was developed by the author for writing term papers, Diploma theses and dissertations at the Institute of Applied Mechanics, Technische Universität München. Photographs and CAD screenshots were processed and enhanced with THE GIMP. Most vector graphics were drawn with CorelDraw X3, exported as Encapsulated PostScript, and edited with psfrag to obtain high-quality labeling. Some smaller and text-heavy graphics (flowcharts, etc.), as well as diagrams were created using PSTricks. The plot raw data were preprocessed with Matlab. In order to use the PostScript- based LATEX packages with pdfLATEX, a toolchain based on pst-pdf and Ghostscript was used. -
Smart Sensors and Small Robots
IEEE Instrumentation and Measurement Technology Conference Budapest, Hungary, May 21-23, 2001. Smart Sensors and Small Robots Mel Siegel Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213-2605, USA Phone: +1 412 268 88025, Fax: +1 412 268 5569 Email: [email protected], URL: http://www.cs.cmu.edu/~mws Abstract – Robots are machines that sense, think, act and (more Thinking: Cognition is a prerequisite for adaptability, with- recently) communicate. Sensing, thinking, and communicating out which no real machine could operate for any significant have all been greatly miniaturized as a consequence of continuous progress in integrated circuit design and manufacturing technol- time in any real environment. No predetermined plan could ogy. More recent progress in MEMS (micro electro mechanical realistically anticipate every feature of the environment - and systems) now holds out promise for comparable miniaturization of if it could, the mission might be utilitarian, but it would the heretofore-retarded third member of the sense-think-act- hardly be interesting, as what would be its purpose if there communicate quartet. More speculatively, initial experiments toward microbiological approaches to robotics suggest possibilities were no new knowledge to be gained? Anyway, even a toy for even further miniaturization, perhaps eventually extending mission in a completely known environment would soon fail down to the molecular level. Alongside smart sensing for robots, in open-loop operation, as accumulated mechanical and sen- with miniaturization of the electromechanical components, i.e., the sor errors would inevitably lead to disastrous discrepancies machinery of robotic manipulation and mobility, we quite natu- rally acquire the capability of smart sensing by robots. -
Learning Preference Models for Autonomous Mobile Robots in Complex Domains
Learning Preference Models for Autonomous Mobile Robots in Complex Domains David Silver CMU-RI-TR-10-41 December 2010 Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, 15213 Thesis Committee: Tony Stentz, chair Drew Bagnell David Wettergreen Larry Matthies Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics Copyright c 2010. All rights reserved. Abstract Achieving robust and reliable autonomous operation even in complex unstruc- tured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parame- ters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have proven to be incredibly tedious and time-consuming, while typically not producing optimal results except in the sim- plest of circumstances. This thesis presents the development and application of machine learning tech- niques that automate the construction and tuning of preference models within com- plex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback. -
Combining Search and Action for Mobile Robots
Combining Search and Action for Mobile Robots Geoffrey Hollinger, Dave Ferguson, Siddhartha Srinivasa, and Sanjiv Singh Abstract— We explore the interconnection between search and action in the context of mobile robotics. The task of searching for an object and then performing some action with that object is important in many applications. Of particular interest to us is the idea of a robot assistant capable of performing worthwhile tasks around the home and office (e.g., fetching coffee, washing dirty dishes, etc.). We prove that some tasks allow for search and action to be completely decoupled and solved separately, while other tasks require the problems to be analyzed together. We complement our theoretical results with the design of a combined search/action approximation algorithm that draws on prior work in search. We show the effectiveness of our algorithm by comparing it to state-of-the- art solvers, and we give empirical evidence showing that search and action can be decoupled for some useful tasks. Finally, Fig. 1. Mobile manipulator: Segway RMP200 base with Barrett WAM arm we demonstrate our algorithm on an autonomous mobile robot and hand. The robot uses a wrist camera to recognize objects and a SICK laser rangefinder to localize itself in the environment performing object search and delivery in an office environment. I. INTRODUCTION objects (or targets) of interest. A concrete example is a robot Think back to the last time you lost your car keys. While that refreshes coffee in an office. This robot must find coffee looking for them, a few considerations likely crossed your mugs, wash them, refill them, and return them to office mind. -
Uncorrected Proof
JrnlID 10846_ ArtID 9025_Proof# 1 - 14/10/2005 Journal of Intelligent and Robotic Systems (2005) # Springer 2005 DOI: 10.1007/s10846-005-9025-1 Temporal Range Registration for Unmanned 1 j Ground and Aerial Vehicles 2 R. MADHAVAN*, T. HONG and E. MESSINA 3 Intelligent Systems Division, National Institute of Standards and Technology, Gaithersburg, MD 4 20899-8230, USA; e-mail: [email protected], {tsai.hong, elena.messina}@nist.gov 5 (Received: 24 March 2004; in final form: 2 September 2005) 6 Abstract. An iterative temporal registration algorithm is presented in this article for registering 8 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured 9 environments. We are primarily motivated by the development of 3D registration algorithms to 10 overcome both the unavailability and unreliability of Global Positioning System (GPS) within 11 required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable 12 modifications to the well-known Iterative Closest Point (ICP) algorithm, the modified algorithm is 13 shown to be robust to outliers and false matches during the registration of successive range images 14 obtained from a scanning LADAR rangefinder on the UGV. Towards registering LADAR images 15 from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the terrain 16 being traversed, we then propose a hybrid registration approach. In this approach to air to ground 17 registration to estimate and update the position of the UGV, we register range data from two 18 LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. 19 Registration of range data guarantees an estimate of the vehicle’s position even when only one of 20 the vehicles has GPS information. -
Dynamic Reconfiguration of Mission Parameters in Underwater Human
Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration Md Jahidul Islam1, Marc Ho2, and Junaed Sattar3 Abstract— This paper presents a real-time programming and in the underwater domain, what would otherwise be straight- parameter reconfiguration method for autonomous underwater forward deployments in terrestrial settings often become ex- robots in human-robot collaborative tasks. Using a set of tremely complex undertakings for underwater robots, which intuitive and meaningful hand gestures, we develop a syntacti- cally simple framework that is computationally more efficient require close human supervision. Since Wi-Fi or radio (i.e., than a complex, grammar-based approach. In the proposed electromagnetic) communication is not available or severely framework, a convolutional neural network is trained to provide degraded underwater [7], such methods cannot be used accurate hand gesture recognition; subsequently, a finite-state to instruct an AUV to dynamically reconfigure command machine-based deterministic model performs efficient gesture- parameters. The current task thus needs to be interrupted, to-instruction mapping and further improves robustness of the interaction scheme. The key aspect of this framework is and the robot needs to be brought to the surface in order that it can be easily adopted by divers for communicating to reconfigure its parameters. This is inconvenient and often simple instructions to underwater robots without using artificial expensive in terms of time and physical resources. Therefore, tags such as fiducial markers or requiring memorization of a triggering parameter changes based on human input while the potentially complex set of language rules. Extensive experiments robot is underwater, without requiring a trip to the surface, are performed both on field-trial data and through simulation, which demonstrate the robustness, efficiency, and portability of is a simpler and more efficient alternative approach.