DOCSLIB.ORG

Collaborative Control: a Robot-Centric Model for Vehicle Teleoperation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Collaborative Control: a Robot-Centric Model for Vehicle Teleoperation Collaborative Control: A Robot-Centric Model for Vehicle Teleoperation Terrence Fong CMU-RI-TR-01-34 The Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, Pennsylvania 15213 November 2001 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Thesis Committee Charles Thorpe (chair) Charles Baur Eric Krotkov Chris Atkeson © 2001 by Terrence Fong. All rights reserved. This research was partially funded by grants from the DARPA ITO “Mobile Autonomous Robot Software” and TTO “Tactical Mobile Robots” (NASA Jet Propulsion Laboratory 1200008) programs, the National Science Foundation (Dissertation Enhancement Award 1120030), and SAIC. The Institut de Systèmes Robotiques (DMT-ISR) of the Ecole Polytechnique Fédérale de Lausanne (EPFL) served as host institution (institution d’accueil) for this thesis and provided research facilities and infrastructure. Abstract Telerobotic systems have traditionally been designed and solely operated from a human point of view. Though this approach suffices for some domains, it is sub-optimal for tasks such as operating multiple vehicles or controlling planetary rovers. Thus, I believe it is worthwhile to examine a new system model for teleoperation: collaborative control. In collaborative control, a human and a robot collaborate to perform tasks and to achieve common goals. Instead of a supervisor dictating to a subordinate, the human and the robot engage in dialogue to exchange information, to ask questions, and to resolve differences. Instead of serving the human as a mere tool, the robot can operate more like a partner. With this approach, the robot has more freedom in execution and is more likely to find good solutions when there are problems. With collaborative control, the human is able to function as a resource for the robot, providing information and processing just like other system modules. In particular, the robot can ask the human questions as it works, to obtain assistance with cognition and perception during task execution. This enables the human to compensate for inadequacies of autonomy, but does not require time-critical nor situation-critical response. Collaborative control is both a novel and a useful paradigm for teleoperation. Collaborative control is novel because it uses dialogue as a framework for coordination, to direct joint task performance and to focus attention where it is needed. Collaborative control is useful because it provides an efficient mechanism for adaptation, to adjust autonomy and human-robot interaction to fit situational needs and user capabilities. This thesis investigates the numerous human-robot interaction and system design issues raised by collaborative control, describes the implementation of a dialogue-based user interface and control architecture, and examines the use of this new approach in several vehicle teleoperation applications and a user study. Keywords: collaborative control, human-robot collaboration, human-robot communication, human-robot interaction, man-machine sysems, mobile robots, PDA interface, remote driving, robot control architecture, safeguarded teleoperation, vehicle teleoperation. iii For Mom and Dad v Acknowledgements When I started the Robotics Ph.D. program in August 1994, I had no idea the road to this thesis would be so long and winding. In fact, when Jessica and I arrived in Pittsburgh, we both thought, “We’ll be back in California before the year 2000.” Even two years later, I was sure that I would finish my research before Navlab II was retired. But then one day, Jessica said, “I think I want to join the High-energy (Physics) group. Do you want to move to Switzerland?” So, we packed our bags and moved across the Atlantic. A few months later, after having settled in at EPFL, I caught myself saying, “It’s really not so hard to understand. I’m a CMU student, but I’m doing my research in Switzerland. I have two advisors named Charles and I’m being paid by a start-up company in California.” At that point, I realized that this was probably not the usual (or easy) route to a Ph.D. But having now reached the end, I must say that the journey has been memorable and rich, and I am truly glad for hav- ing traveled it. There are many people to whom I would like to express my appreciation and gratitude. First and foremost, I would like to thank Dr. Charles (Chuck) Thorpe and Dr. Charles Baur for their guidance, encouragement, patience, and wisdom during the past few years. It has been a great privilege to work with such far thinking and inspirational individuals. I am particularly indebted to Chuck for allowing me to follow such an unconventional path and to Charles for his unwavering support along the way. All that I have learned and all that I have accomplished during the course of this thesis would not have been possible without their dedication. To the community that is the Robotics Institute, my thanks go to: Marie Elm, Jim “Fraz” Frazier, Ruth Gaus, Martial Hebert, Suzanne Lyons Muth, Dorothy “Dot” Marsh, Matt Mason, Stephanie Riso, Sandy Rocco, Red Whittaker, and Marce Zaragoza. To the Navlab Group (Al, Barry, Dirk, Jennie, Julio, Kate, Jim), thanks for making the Slag Heap “enjoy- able”. To Robograds ‘94 (Andy, Daniel, Dong Mei, Henry, Lisa, Murali, Patrick, Sundar, Zack), thanks for making Pittsburgh fun. To my fellow Planeteers (Butler, Erik, Laurent, Mike, and Phil), thanks for being so flexi- ble and supportive even when I was on the other side of the world. Fourth Planet was a great experience, a joy, a family. I will forever remember our telecons, meetings, and lunches. I hope that we will soon have the opportunity to all work together again (and that I will once more have the pleasure of amusing you all with my choice of “breakfast”). vii Un grand merci à tous ceux à l’EPFL qui m’ont aidé. Je suis particulièrement reconnai- sant: au Docteur Reymond Clavel qui m’a accueilli au sein de l’ISR; à Mme Heidi Banz, à Mlle Karine Genoud, à Mme Marie-Jose Pellaud et à Mme Katrin Schleiss pour leur assis- tance avec tous les taches administratives grandes et petites; et à Mme Nadia Nibbio (CFRC) qui a gracieusement financé quelques voyages aux Etats-Unis. Je remercie tous les étudiants qui ont travaillé avec moi (Gilbert Bouzeid, Mathias Deschler, Roger Meier, et Grégoire Terrien). Bon travail! Je remercie chaleureusement le Groupe VRAI (Didier, François, Gaëtan, Nicolas, Olivier, Patrice, Philippe et Sébastien) grâce à qui ce doctorat a put être réalisé dans une ambiance amicale et motivante. A Lorenzo et à Cécile, merci pour votre amitié, votre generosité, et pour tout le reste. I wish to thank my family for their love and for encouraging me to follow my dreams. To Tammy and Tim, thanks for always being there. To Mom and Dad, thanks for caring, for believing, and for supporting me all these years, no matter how far away I have been. To Karina, un très gros bisou de ton papa. Finally, to Jessica, thanks for your hugs, your kisses, your laughter, your smile and your love. You make it all worthwhile. Lausanne, Switzerland November 2001 viii Contents 1 Introduction 1 1.1 Robot as Tool. 1 1.2 Human as Controller . 2 1.3 Collaborative Control . 2 1.4 Why Collaborative Control? . 5 1.5 Related Research. .7 1.5.1 Supervisory Control . 7 1.5.2 Cooperative Teleoperation . 8 1.5.3 Human Robot Control Architectures . 9 1.5.4 Adjustable Autonomy and Mixed Initiative. 9 1.5.5 KTH Collaborative Human-Robot Systems. 10 1.6 Thesis Organization. 11 2 Developing Collaborative Control 13 2.1 Vehicle Teleoperation . 13 2.2 Conventional System Models . 14 2.2.1 Direct Control. 15 2.2.2 Supervisory Control . 16 2.2.3 Fully Autonomous Control . 17 2.2.4 Limitations . 18 2.3 Collaborative Control System Model . 19 2.4 Key Design Issues . 20 2.4.1 Dialogue . 20 2.4.2 Awareness. 20 2.4.3 Self-reliance . 21 2.4.4 Adaptiveness . 21 2.4.5 Human-Robot Interaction. 22 2.4.6 User Interface Design. 22 2.4.7 Control and Data Flow . 23 ix Contents 2.5 Evaluation. .23 2.6 Summary . .24 3 System Design 25 3.1 Design. .25 3.1.1 Principles. .25 3.1.2 Approach . .26 3.1.3 Architecture. .26 3.2 UserModeller . .29 3.2.1 Attributes. .29 3.2.2 Definition. .29 3.2.3 Use. .30 3.3 QueryManager . .31 3.4 EventLogger . .32 3.5 Human-Robot Dialogue . .33 3.6 Background . .36 3.6.1 Human-Robot Interaction . .36 3.6.2 Collaboration Systems. .37 3.6.3 Human-Computer Dialogue . .38 3.7 Summary . .40 4 PdaDriver: A User Interface for Collaborative Control 41 4.1 Requirements . .41 4.1.1 Collaborative Control Needs . .42 4.1.2 Vehicle Teleoperation Needs. ..
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Robots Are Becoming Autonomous
    FORSCHUNGSAUSBLICK 02 RESEARCH OUTLOOK STEFAN SCHAAL MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS, TÜBINGEN Robots are becoming autonomous Towards the end of the 1990s Japan launched a globally or carry out complex manipulations are still in the research unique wave of funding of humanoid robots. The motivation stage. Research into humanoid robots and assistive robots is was clearly formulated: demographic trends with rising life being pursued around the world. Problems such as the com- expectancy and stagnating or declining population sizes in plexity of perception, effective control without endangering highly developed countries will create problems in the fore- the environment and a lack of learning aptitude and adapt- seeable future, in particular with regard to the care of the ability continue to confront researchers with daunting chal- elderly due to a relatively sharp fall in the number of younger lenges for the future. Thus, an understanding of autonomous people able to help the elderly cope with everyday activities. systems remains essentially a topic of basic research. Autonomous robots are a possible solution, and automotive companies like Honda and Toyota have invested billions in a AUTONOMOUS ROBOTICS: PERCEPTION–ACTION– potential new industrial sector. LEARNING Autonomous systems can be generally characterised as Today, some 15 years down the line, the challenge of “age- perception-action-learning systems. Such systems should ing societies” has not changed, and Europe and the US have be able to perform useful tasks for extended periods autono- also identified and singled out this very problem. But new mously, meaning without external assistance. In robotics, challenges have also emerged. Earthquakes and floods cause systems have to perform physical tasks and thus have to unimaginable damage in densely populated areas.
    [Show full text]
  • An Autonomous Grape-Harvester Robot: Integrated System Architecture
    electronics Article An Autonomous Grape-Harvester Robot: Integrated System Architecture Eleni Vrochidou 1, Konstantinos Tziridis 1, Alexandros Nikolaou 1, Theofanis Kalampokas 1, George A. Papakostas 1,* , Theodore P. Pachidis 1, Spyridon Mamalis 2, Stefanos Koundouras 3 and Vassilis G. Kaburlasos 1 1 HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece; [email protected] (E.V.); [email protected] (K.T.); [email protected] (A.N.); [email protected] (T.K.); [email protected] (T.P.P.); [email protected] (V.G.K.) 2 Department of Agricultural Biotechnology and Oenology, School of Geosciences, International Hellenic University (IHU), 66100 Drama, Greece; [email protected] 3 Laboratory of Viticulture, Faculty of Agriculture, Forestry and Natural Environment, School of Agriculture, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece; [email protected] * Correspondence: [email protected]; Tel.: +30-2510-462-321 Abstract: This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of address- ing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdepen- dent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely Citation: Vrochidou, E.; Tziridis, K.; harvest, green harvest and defoliation.
    [Show full text]
  • Space Applications of Micro-Robotics: a Preliminary Investigation of Technological Challenges and Scenarios
    SPACE APPLICATIONS OF MICRO-ROBOTICS: A PRELIMINARY INVESTIGATION OF TECHNOLOGICAL CHALLENGES AND SCENARIOS P. Corradi, A. Menciassi, P. Dario Scuola Superiore Sant'Anna CRIM - Center for Research In Microengineering Viale R. Piaggio 34, 56025 Pontedera (Pisa), Italy Email: [email protected] ABSTRACT In the field of robotics, a microrobot is defined as a miniaturized robotic system, making use of micro- and, possibly, nanotechnologies. Generally speaking, many terms such as micromechanisms, micromachines and microrobots are used to indicate a wide range of devices whose function is related to the concept of “operating at a small scale”. In space terminology, however, the term “micro” (and “nano”) rover, probe or satellite currently addresses a category of relatively small systems, with a size ranging from a few to several tens of centimetres, not necessarily including micro- and nanotechnologies (except for microelectronics). The prefix “micro” will be used in this paper in (almost) its strict sense, in order to address components, modules and systems with sizes in the order of micrometers up to few millimetres. Therefore, with the term microrobot it will be considered a robot with a size up to few millimetres, where typical integrated components and modules have features in the order of micrometers and have been entirely produced through micromechanical and microelectronic mass- fabrication and mass-assembly processes. Hence, the entire robot is completely integrated in a stack of assembled chips. Due to its size, the capabilities of the single unit are limited and, consequently, microrobots need to work in very large groups, or swarms, to significantly sense or affect the environment.
    [Show full text]
  • Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle
    Dissertations and Theses 6-2015 Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle Gurasis Singh Follow this and additional works at: https://commons.erau.edu/edt Part of the Aerospace Engineering Commons Scholarly Commons Citation Singh, Gurasis, "Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle" (2015). Dissertations and Theses. 248. https://commons.erau.edu/edt/248 This Thesis - Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. SENSOR FUSION AND OBSTACLE AVOIDANCE FOR AN UNMANNED GROUND VEHICLE A Thesis Submitted to the Faculty of Embry-Riddle Aeronautical University by Gurasis Singh In Partial Fulfillment of the Requirements for the Degree of Master of Science in Aerospace Engineering June 2015 Embry-Riddle Aeronautical University Daytona Beach, Florida Acknowledgements It provides for me colossal joy to recognize the individuals who gave me the vital directions, help and ceaseless inspiration towards the fulfillment of this preposition. I would like to augment my appreciation and exceptionally recognize my advisor, Dr. Richard J. Prazenica for his specialized direction, supervision and consistent good backing amid the course of this postulation. He empowered me to strike the right harmony in the middle of classes and theory work, investigate the flexible character implanted in me and yield a quality output at all times. I would like to thank Dr. Hever Moncayo for serving on the panel and for furnishing me with quality specialized information throughout the span of this postulation.
    [Show full text]
  • Creation of a Learning, Flying Robot by Means of Evolution
    Creation of a Learning, Flying Robot by Means of Evolution Peter Augustsson Krister Wol® Peter Nordin Department of Physical Resource Theory, Complex Systems Group Chalmers University of Technology SE-412 96 GÄoteborg, Sweden E-mail: wol®, [email protected] Abstract derivation of limb trajectories, is computationally ex- pensive and requires ¯ne-tuning of several parame- ters in the equations describing the inverse kinematics We demonstrate the ¯rst instance of a real [Wol® and Nordin, 2001] and [Nordin et al, 1998]. The on-line robot learning to develop feasible more general question is of course if machines, to com- flying (flapping) behavior, using evolution. plicated to program with conventional approaches, can Here we present the experiments and results develop their own skills in close interaction with the of the ¯rst use of evolutionary methods for environment without human intervention [Nordin and a flying robot. With nature's own method, Banzhaf, 1997], [Olmer et al, 1995] and [Banzhaf et evolution, we address the highly non-linear al, 1997]. Since the only way nature has succeeded in fluid dynamics of flying. The flying robot is flying is with flapping wings, we just treat arti¯cial or- constrained in a test bench where timing and nithopters. There have been many attempts to build movement of wing flapping is evolved to give such flying machines over the past 150 years1. Gustave maximal lifting force. The robot is assembled Trouve's 1870 ornithopter was the ¯rst to fly. Powered with standard o®-the-shelf R/C servomotors by bourdon tube fueled with gunpowder, it flew 70 me- as actuators.
    [Show full text]
  • Xavier: an Autonomous Mobile Robot on the Web
    Xavier: An Autonomous Mobile Robot on the Web Reid Simmons, Joaquin Fernandez1, Richard Goodwin2, Sven Koenig3, Joseph O’Sullivan School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213 Abstract For the past three years, we have been running an experiment in web-based interaction with an autonomous indoor mobile robot. The robot, Xavier, can accept commands to travel to different offices in our building, broadcasting camera images as it travels. The experiment, which was originally designed to test a new navigation algorithm, has proven very successful, with over 30,000 requests received and 210 kilometers travelled, to date. This article describes the autonomous robot system, the web-based interfaces, and how they communicate with the robot. It highlights lessons learned during this experiment in web-based robotics and includes recommendations for putting future mobile robots on the web. Introduction In December 1995, we began what we assumed would be a short (two to three month) experiment to demonstrate the Figure 1: Xavier reliability of a new algorithm that we had developed for autonomous indoor navigation [12]. To provide a continual camera on a Directed Perception pan-tilt head. Xavier also source of commands to the robot, we set up a web page in has a speaker and a speech-to-text card. Control, which users throughout the world could view the robot’s perception, and planning are carried out on two 200 MHz progress and command its behavior. What we failed to Pentium computers, running Linux. A 486 laptop, also anticipate was the degree of interest an autonomous mobile running Linux, sits on top of the robot and provides for robot on the web would have.
    [Show full text]