A survivability framework for autonomous systems

A S URVIVABILITY F RAMEWORK AUTONOMOUS S YSTEMS Q UEK B OON K IAT N ATIONAL U NIVERSITY OF S INGAPORE 2008 FOR A S URVIVABILITY F RAMEWORK FOR AUTONOMOUS S YSTEMS Q UEK B OON K IAT (B.Eng. (Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF D OCTOR OF P HILOSOPHY NUS GRADUATE SCHOOL FOR INTEGRATIVE SCIENCES AND ENGINEERING N ATIONAL U NIVERSITY OF S INGAPORE 2008 Abstract i Abstract Autonomous systems are being envisioned for enhancing human capabilities especially in harsh or dangerous operating conditions. Despite considerable advances, achieving autonomous operation remains a major challenge. Current approaches predomi- nantly rely on the integration of disparate components or functional modules such as perception, navigation, actuation and decision making, into complex, stand-alone systems. While notable progress has been achieved, few systems remain operational when subject to unexpected situations or mishaps. Apart from physical limitations in sensors and hardware, the difficulty is exacerbated by the need to control and coordinate the operation of multiple components or capabilities that constitutes the whole system. Addressing these limitations requires system architectures that can fulfil the operational and functional requirements of the intended application, while leveraging on the systems’ capabilities despite their inherent imperfections. Many biological organisms in nature (e.g. mammals) possess innate, autonomous capabilities that enable them to survive. Concordantly, understanding how survivability arises in biological systems may provide valuable insights for applying similar principles to artificial systems in order to enhance their autonomous operation. Leveraging on the survivability paradigm, this thesis introduces an architectural framework to address the autonomous operation of artificial systems by exploiting the effectiveness of survival-oriented processes occurring within biological organisms. This is directed at enhancing the decision making capabilities of artificial systems in order to increase their level of autonomy. In this framework, the concept of survivability is introduced as an important operational requirement, forming an integral part of a system’s architecture and governing its design process. The Survivability Framework incorporates the understanding of physiological, psychological and cognitive processes underlying the ability of biological systems to survive when subject to unexpected challenges, especially those involving potential or immediate Abstract ii danger. It introduces the notions of needs and emotions as state representations whose interactions form the mechanisms that motivate and regulate the behaviour of an artificial system. Within this context, survivability is defined as the ability of a system to fulfil its needs. These needs represent the gaps between current and desired system states, whereas emotions serve as indicators for monitoring the fulfilment of these needs. Based on these representations, survivability then emerges from determining suitable actions that can allow a system to fulfil its needs. The relationships between needs, emotions and actions are encapsulated within a survivability reference architecture, from which a selection of system architectures can be instantiated according to the requirements of the intended applications. The design and deployment of autonomous systems is realized with the steps defined by the Survivability Framework. This process is illustrated in the design walk-through, simulation and implementation of an autonomous surveillance vehicle. The feasibility of the proposed framework is demonstrated in several use cases that highlight the interactions between needs, emotions and actions in controlling and regulating the behaviour of the autonomous surveillance vehicle. Foreword iii Foreword This thesis is an outcome of asking some very big questions. What makes an entity autonomous? What is missing in the current understanding of how autonomy can be achieved in artificial entities? What approach should one adopt in any attempt to increase the autonomy of an artificial system, such as a mobile robot, or an autonomously guided vehicle? While these questions have been asked for decades since the advent of modern computing, many of them are still unanswered. The difficulty with achieving autonomy boils down to a lack of understanding of what being autonomous means to an artificial system, such as a mobile robot. This is still the case today, as queries on the nature of autonomy remains centre-stage in artificial intelligence and robotics. In particular, autonomy is often discussed within the framework of Agent Theory (AT). Agent Theory is itself, an attempt at understanding autonomy by prescribing some of its known requirements, for instance, percepts, and actions, to artificial entities that may possibly enable them to react in an autonomous manner to the environment (and increasingly, to their own internal states). Despite its comprehensiveness, AT is just one among many other possibilities in which this could be accomplished. By observing biological entities in nature, one expresses amazement at the multitude of seamless activities occurring within these entities that gives them the ability to act in an autonomous manner, and react to the environment in ways that can safeguard their survival. Most of the activities endowing a biological organism with its autonomous behaviours are closely related to those that safeguard its survival. From such observations, the survivability of an organism becomes an important and necessary condition for the autonomy of a system. This is the tenet that the thesis is based on. To understand the elements of survivability, a correlation between the needs of a biological entity and its survival is formed. Many of the activities that safeguard its survival are driven by the presence of needs that the organism fulfils, via a constant Foreword adaptation of its behaviour to meet these needs. iv The different intensities of such needs account for the motivations for different types of behaviour. Needs, essentially, motivates different behaviours. With neurological and psychological evidence to attest to these principles, they in return form the basic premises upon which an architectural framework is constructed. The aim is to mirror the processes occurring in nature, and apply them in the design and deployment of autonomous systems, in a biologicallyinspired manner. With the approach laid out above, this thesis presents what can be termed a survivability paradigm, i.e. the notion that survivability is an intrinsic capability of a system that makes it autonomous, and that autonomy can be enhanced therefore, by addressing the survivability of a system. Following this paradigm, a Survivability Framework is thus constructed, incorporating the concepts of needs and emotions as factors that motivate and regulate behaviour in an artificial system. Organization of needs, emotions and actions into various relationships gives rise to a reference architecture, which can be used to spawn many different (and perhaps, some existing) architectures for an autonomous system. The implementation of the framework in the form of these architectures is shown in a collection of case studies involving both real and simulated robots. The results obtained in this thesis are encouraging as a foray into the multidisciplinary field of cognitive and affective robotics. Though this thesis is adopting such an approach, its objective is not to mimic exactly the cognitive processes operating within biological systems; rather, the attempt is directed at understanding such processes, learning from them and determining the ways in which these principles can be applied to the engineering of artificial systems. As the increased adoption of cognitive models of intelligence in future becomes a clear trend, it is hoped that this thesis can serve as a contribution in closing the explanatory gap between cognitive and affective models of human intelligence and behaviour, with the system engineering perspective of artificial systems design. Boon Kiat, Quek July 2007 Acknowledgement v Acknowledgement “Hofstadter’s Law states that: It always takes longer than you expect, even when you take into account Hofstadter’s Law.” – Douglas Hofstadter, in his 1979 magnum opus, Gödel, Escher, Bach: An Eternal Golden Braid This is the single most difficult piece of writing I have ever done. As the conclusion of many years of education, this thesis bears testimony to many cycles of inspiration, discovery, frustration, disappointments, and inspiration all over again, over the course of several years. The journey was not lonesome, however. It is to all the following esteemed persons that I have met along the way who have helped me in some way or another in the pursuit of this thesis, that I am grateful for. I would like to express heartfelt gratitude to my supervisor, Dr. Lim Khiang Wee, without whose encouragements, knowledge and advice, and the intellectual freedom and latitude that he has allowed me, I would not have been able to undertake this research. Similarly, the greatest influence over the daily direction of my research was perhaps provided by my co-supervisor, Dr. Javier Ibañez-Guzmán, who continued to guide me tirelessly with an equal mix of inspiration and motivation, despite his relocation back to France. For guiding me into the research path as an outcome of the successful completion of an honours year dissertation, are Dr. Prahlad Vadakkepat and Dr. Xu Jian Xin, both of whom are members of my thesis advisory committee. It is my great privilege to be under their continued guidance in the course of my candidature, where our interactions are not merely those between student and supervisor, but more like those between friends. The patience and support of my family is a constant stream of reassurance. For that I wish to thank my father and mother, Mr Quek Kim Meng and Madam Ang Li Kian, for their care and nurture, and my brothers, Ben and Ronnie Quek, for showing constant Acknowledgement vi interest in, and endless amusement at the robot in this thesis. Thanks also goes to my cousins Pauline & Irene Quek, for their support and love. Heartfelt appreciation goes to my fellow sufferers, namely fellow Ph.D. candidates: Kiew Choon Meng, Lee Hui Mien, Joe Yen Yen, Gan Hiong Yap, Mustafa Shabbir Kurbanhusen, Daniel Teo Tat Joo, Jason Teo Chek Sing, Tey Ju Nie, Fua Cheng Heng, Goh Chwee Sim, Lim Wei Ying, Wong Shek Yoon, for sharing this journey together, and especially Ang Su Fen, for her constant support and nurture, for sharing the final stages of her candidature together, and being that beacon of light in the darkest moments. Despite the difficulty of it all, we have had a wonderful four years together. Sincere gratitude to a special few, who are forever loyal, patient, and always believing in me: Dr. Teh Huey Fang, Tan Hui Tong, Dr. Florencia Edith Wiria, Wendy Tan Geak Huang, Chua Chen Yun, Wei Xiao Qin, Cynthia Quah, Jovice Ng Boon Sing, Tang Ying, Ada Lo, and especially my godsister, Joanna Koh Ying Ting. My fellow buddies, with whom I had shared the significant years of my tertiary education, for all the crazy things we used to together, not just due to the need for an outlet from work, but for our impulsive whims: Donny Soh Cheng Lock, Ng Chu Ming, Geoffrey Koh Yeow Nam, Han Boon Kiat, and Yeow Wai Leong. My long-time friends, who have shown support and believe in what I do: William Sze and Song Chenying, Tan Chin Wee, Loy Hui Chien and his wife Xinyue, Lim Boon Pang, Ng Kwan Jee, Varian Lim and Emily Loh, Loh Tzu Liang, Walter Lim and Soh Sook Leng, Lin Wei Ling, Lam Lap, Lee Seong Per, Rhys Wong, Mark Sin, Mahadev Appan, and Yee-wah Lee Jefferson. Many friends and colleagues with whom I have had the privilege of working with, have assisted me greatly, be it over suggestions on where to look, or the occasional brainstorming over a cup of tea. From SIMTech: Dr. Lim Ser Yong, Dr. Lin Wei, Dr. Andrew Shacklock, Dr. Ong Jiun Keat, Mr Ng Teck Chew, and Ms Jeannette Lim Shouk Gaem. From the Mechatronics and Automation Laboratory, NUS: Mr Chan Kit Wai, Dr. Tang Kok Zuea, Tan Chee Siong, Jim Tan Shin Jiuh, Liu Xin, and Dr. Xiao Peng. Heartfelt appreciation to members of staff from the NUS Graduate School for Integrative Sciences and Engineering, for all the administrative assistance and advice throughout my whole candidature. Finally, I wish to thank the Agency for Science, Technology and Research (A*STAR) for scholarship support and funding throughout the entire duration of the candidature, and my sureties, Ang Choon Ngiap and Quek Kim Hoe (my godfather), without whom all of this would not have been possible. Acknowledgement vii Picture Credits The following pictures reproduced in this thesis are obtained courtesy of, and with due acknowledgement to their respective sources: Figure 1.1(a): http://www.irobot.com/sp.cfm?pageid=138 Figure 1.1(b): http://www.rec.ri.cmu.edu/projects/auto_harvesting/index.htm Figure 1.1(e): http://www-2.cs.cmu.edu/afs/cs/project/alv/www/index.html Figure 1.2(a): http://world.honda.com/news/2005/photo/c051213/index.html Figure 1.2(b): http://http://www.ubergizmo.com/photos/2006/9/hitachi-emiew.jpg Figure 1.2(c): http://www.sony.net/SonyInfo/QRIO/top_nf.html Figure 1.2(d): http://www.irobot.com/index.cfm Figure 2.1(a): http://marsrovers.jpl.nasa.gov Figure 2.1(b): http://www-rocq.inria.fr/imara/site2/gallery/2004_antibes/ Figure 2.1(c): http://www.defenselink.mil/news/May2004/n05172004_200405173.html Figure 2.1(d): http://www.darpa.mil/grandchallenge/ Figure 2.2(a),(b),(c),(d): http://www.darpa.mil/grandchallenge04/ Figure 3.2: sprites are courtesy of http://www.starwars.com/databank/droid/ Contents viii Contents Abstract i Foreword iii Acknowledgement v Contents viii List of Figures xiv List of Tables xix List of Abbreviations xxii List of Symbols PART I. C ONCEPTUAL F RAMEWORK xxiii AND F OUNDATIONS Chapter Introduction 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Understanding autonomous systems . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Defining autonomous systems . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Potential applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Motivation for autonomous systems . . . . . . . . . . . . . . . . . . . 1.3 Challenges in autonomous systems . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.1 Operational and functional challenges . . . . . . . . . . . . . . . . . . 11 1.3.2 Situational awareness and decision making . . . . . . . . . . . . . . . 12 1.3.3 System engineering and integration . . . . . . . . . . . . . . . . . . . 13 1.3.4 The Survivability Paradigm . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Statement of the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Appendix D. Supporting Case Studies 250 (a) t = 0s (b) t = 10s (c) t = 25s (d) t = 40s (e) t = 50s (d) t = 60s Figure D.16: Use Case D.3: Snapshots of the team of six robots performing vehicleplatooning task, with no observability of the need for safety, nsafe . Appendix D. Supporting Case Studies 251 (a) t = 0s (b) t = 10s (c) t = 25s (d) t = 40s (e) t = 50s (d) t = 60s Figure D.17: Use Case D.3: Snapshots of the team of six robots performing vehicleplatooning task, with no observability of the need for accomplishment, naccomp . Appendix E. Publications 252 A PPENDIX E Publications Some of the results presented in this thesis have previously been published in various conference proceedings and articles. These are listed below and referred to in the main body of the thesis where appropriate. E.1 List of articles published during the candidature 1. Quek, B. K., J. Ibañez-Guzmán, and K. W. Lim (2004, December). Feature detection for stereo-vision-based unmanned navigation. In IEEE Conference on Cybernetics and Intelligent Systems, Singapore. 2. Quek, B. K., J. Ibañez-Guzmán, and K. W. Lim (2005, November). Feature-based perception for autonomous unmanned navigation. In The 31st Annual Conference of the IEEE Industrial Electronics Society, Raleigh, North Carolina, USA, pp. 1791– 1796. 3. Quek, B. K., J. Ibañez-Guzmán, and K. W. Lim (2006a, November). Attaining operational survivability in an autonomous unmanned ground surveillance vehicle. In The 32st Annual Conference of the IEEE Industrial Electronics Society, Paris, France. 4. Quek, B. K., J. Ibañez-Guzmán, and K. W. Lim (2006b, December). A Survivability Framework for the development of autonomous unmanned systems. In The 9th International Conference on Control, Automation, Robotics and Vision, Singapore. Bibliography 253 Bibliography Adams, M. and J. Ibañez-Guzmán (2002a). Limiting velocity and acceleration commands for dynamic control of a large vehicle. 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[...]... search and rescue operations in the aftermath of natural and man-made disasters (Murphy, 2004), are areas which are driving efforts for effective solutions based on autonomous systems For instance, since 2003, more than 330 EOD robots have been shipped to Iraq and Afghanistan (Karlin, 2007) While the potential for military applications continue to be a key driver for the development of autonomous systems, ... capable of making choices and reasoning about the world in a self-directed fashion A central aspect of autonomy is the capability to select one’s goals and the means of realizing them (Savage, 2003) However, for an entity to logically reason about the choices to make and the behaviour to adopt, first it needs to become self-aware, or at least, be aware of alternative courses of action An entity that... the description of the Survivability Framework and its underlying justifications This framework is directed at behaviour generation in autonomous systems that takes survivability into account As an architectural framework, it identifies a reference architecture for integrating environment models, robot capabilities, decision making mechanisms, and robot behaviours to form a complete autonomous system The... List of Abbreviations List of Abbreviations ADAS Advanced Driver Assistance System AGV Autonomously Guided Vehicle, Autonomous Ground Vehicle ARV Armed Robotic Vehicle CICAS Cooperative Intersection Collision Avoidance Systems CRASAR Centre for Robot Assisted Search And Rescue CVIS Cooperative Vehicle-Infrastructure Systems DARPA Defence Advance Research Projects Agency EURON European Robotics Research... guidance and active safety (Dickmanns, 1998, 2002; Pradalier et al., 2005), and assistive robotic devices for the elderly and physically-challenged are now available (Arkin et al., 2003) These assistive devices are designed not just to compensate for a reduction in the physical and cognitive capabilities of human beings, but to enhance them as well 1.2.3 Motivation for autonomous systems The above are... nature’s laws, man has harnessed the knowledge of science and applied it in every aspect of his life Today, technological progress in information and communication systems have resulted in escalating computational capacity and a proliferation of artificial systems with many embedded processing capabilities, bringing closer the reality of truly intelligent and autonomous systems that may prove capable of... on-board, using lightweight computational devices (Garcia and Valavanis, 2006; Valavanis et al., 2005) The miniaturization of such devices paves the way for autonomous systems with greater mobility and smaller form-factors (Barnes et al., 2005; Bruch et al., 2005) Progress in sensor systems and computer vision have led to an increase in machine perception capability (Brown et al., 2003; Dickmanns,... Background Since the dawn of time, mankind has been seeking to extend his physical and intellectual capabilities via artificial means This has evolved from the early domestication of animals as tools for farming and hunting, to the mechanical augmentation of man’s advantage over other species through the generation of power and the invention of language and writing With increasing understanding of nature’s... systems An appreciation of the nature of autonomous systems, and the challenges involved in their design and deployment is discussed in this section autonomous systems is presented First a definition of Potential applications and the motivations for autonomous systems are identified, to highlight the lack of convergence between the need for autonomous systems, and the operational, technical and theoretical... needs and their measurement 101 4.4 Core emotions in humans / animals and correlation with needs 103 4.5 Examples of arbitration schemes for selection of actions 119 4.6 Comparing architectural approaches using the Survivability Framework 120 4.7 Comparison of Survivability Framework and task-oriented approaches 121 5.1 Tasks specifications and the needs that are . A SURVIVABILITY FRAMEWORK FOR AUTONOMOUS SYSTEMS QUEK BOON KIAT NATIONAL UNIVERSITY OF SINGAPORE 2008 A SURVIVABILITY FRAMEWORK FOR AUTONOMOUS SYSTEMS QUEK BOON KIAT (B.Eng. (Hons.), NUS) A. for enhancing human capabilities especially in harsh or dangerous operating conditions. Despite considerable advances, achieving autonomous operation remains a major challenge. Current approaches. capability of a system that makes it autonomous, and that autonomy can be enhanced therefore, by addressing the survivability of a system. Following this paradigm, a Survivability Framework is thus