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Cognitive Risk Perception System for Obstacle Avoidance in Outdoor Muav Missions

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Cognitive Risk Perception System for Obstacle Avoidance in Outdoor Muav Missions Cognitive risk perception system for obstacle avoidance in outdoor mUAV missions David Sanz Escuela Tecnica Superior de Ingenieros Industriales Universidad Politecnica de Madrid A thesis submitted for the degree of PhD in Automation and Robotics PhilosophiæDoctor (PhD) in Automation and Robotics. June, 2015 1. Reviewer: Dr. Ahmad Aamir 2. Reviewer: Dr. Oscar Reinoso 3. Reviewer: Dr. Angela´ Ribeiro 4. Reviewer:Dr. Claudio Rossi 5. Reviewer: Dr. David Travieso 6. Reserve reviewer: Dr. Ram´onBarber 7. Reserve reviewer: Dr. Gonzalo Pajares 8. International reviewer: Dr. Pablo Fernandez-Alcantarilla 9. International reviewer: Dr. Francisco Costela Day of the defense: Signature from head of PhD committee: ii Abstract Robotics has undergone a great revolution in the last decades. Nowadays this technology is able to perform really complex tasks with a high degree of accuracy and speed, however this is only true in precisely defined situations with fully controlled variables. Since the real world is dynamic, changing and unstructured, flexible and non context-dependent systems are required. The ability to understand situations, acknowledge changes and balance reactions is required by robots to successfully interact with their surroundings in a fully autonomous fashion. In fact, it is those very processes that define human interactions with the envi- ronment. Social relationships, driving or risk/incertitude management... in all these activities and systems, context understanding and adaptability are what allow human beings to survive: contrarily to the traditional robotics, people do not evaluate obstacles according to their position but according to the potential risk their presence imply. In this sense, human perception looks for information which goes beyond location, speed and dynamics (the usual data used in traditional obstacle avoidance systems). Specific features in the behaviour of a particular element allows the understanding of that el- ement's nature and therefore the comprehension of the risk posed by it. This process defines the second main difference between traditional obstacle avoidance systems and human behaviour: the ability to understand a situation/scenario allows to get to know the implications of the elements and their relationship with the observer. Establishing these semantic relationships -named cognition- is the only way to estimate the actual danger level of an element. Furthermore, only the application of this knowledge allows the generation of coherent, suitable and adjusted responses to deal with any risk faced. The research presented in this thesis summarizes the work done towards translating these human cognitive/reasoning procedures to the field of robotics. More specifically, the work done has been focused on employing human-based methodologies to enable aerial robots to navigate safely. To this effect, human perception, cognition and reac- tion processes concerning risk management have been experimentally studied; as well as the acquisition and processing of stimuli. How psychological, sociological and anthro- pological factors modify, balance and give shape to those stimuli has been researched. And finally, the way in which these factors motivate the human behaviour according to different mindsets and priorities has been established. This associative workflow has been reproduced by establishing an equivalent struc- ture and defining similar factors and sources. Besides, all the knowledge obtained experimentally has been applied in the form of algorithms, techniques and strategies which emulate the analogous human behaviours. As a result, a framework capable of understanding and reacting in response to stimuli has been implemented and validated. iii Resumen La rob´oticaha evolucionado exponencialmente en las ´ultimasd´ecadas,permitiendo a los sistemas actuales realizar tareas sumamente complejas con gran precisi´on,fiabili- dad y velocidad. Sin embargo, este desarrollo ha estado asociado a un mayor grado de especializaci´ony particularizaci´onde las tecnolog´ıasimplicadas, siendo estas muy efi- cientes en situaciones concretas y controladas, pero incapaces en entornos cambiantes, din´amicosy desestructurados. Por eso, el desarrollo de la rob´oticadebe pasar por dotar a los sistemas de capacidad de adaptaci´ona las circunstancias, de entendedimiento so- bre los cambios observados y de flexibilidad a la hora de interactuar con el entorno. Estas son las caracteristicas propias de la interacci´ondel ser humano con su entorno, las que le permiten sobrevivir y las que pueden proporcionar a un sistema inteligencia y capacidad suficientes para desenvolverse en un entorno real de forma aut´onomae independiente. Esta adaptabilidad es especialmente importante en el manejo de riesgos e incetidum- bres, puesto que es el mecanismo que permite contextualizar y evaluar las amenazas para proporcionar una respuesta adecuada. As´ı,por ejemplo, cuando una persona se mueve e interactua con su entorno, no eval´ualos obst´aculosen funci´onde su posici´on, velocidad o din´amica(como hacen los sistemas rob´oticostradicionales), sino mediante la estimaci´ondel riesgo potencial que estos elementos suponen para la persona. Esta evaluaci´onse consigue combinando dos procesos psicof´ısicosdel ser humano: por un lado, la percepci´onhumana analiza los elementos relevantes del entorno, tratando de entender su naturaleza a partir de patrones de comportamiento, propiedades asoci- adas u otros rasgos distintivos. Por otro lado, como segundo nivel de evaluaci´on,el entendimiento de esta naturaleza permite al ser humano conocer/estimar la relaci´on de los elementos con ´elmismo, as´ıcomo sus implicaciones en cuanto a nivel de riesgo se refiere. El establecimiento de estas relaciones sem´anticas -llamado cognici´on- es la ´unicaforma de definir el nivel de riesgo de manera absoluta y de generar una respuesta adecuada al mismo. No necesariamente proporcional, sino coherente con el riesgo al que se enfrenta. La investigaci´onque presenta esta tesis describe el trabajo realizado para trasladar esta metodolog´ıade an´alisisy funcionamiento a la rob´otica. Este se ha centrado es- pecialmente en la nevegaci´onde los robots aereos, dise~nandoe implementado proced- imientos de inspiraci´onhumana para garantizar la seguridad de la misma. Para ello se han estudiado y evaluado los mecanismos de percepci´on,cognici´ony reacci´onhumanas en relaci´onal manejo de riesgos. Tambi´ense ha analizado como los est´ımulos son capturados, procesados y transformados por condicionantes psicol´ogicos,sociol´ogicosy antropol´ogicosde los seres humanos. Finalmente, tambi´ense ha analizado como estos iv factores motivan y descandenan las reacciones humanas frente a los peligros. Como re- sultado de este estudio, todos estos procesos, comportamientos y condicionantes de la conducta humana se han reproducido en un framework que se ha estructurado basadan- dose en factores an´alogos.Este emplea el conocimiento obtenido experimentalmente en forma de algoritmos, t´ecnicasy estrat´egias,emulando el comportamiento humano en las mismas circunstancias. Dise~nado,implementeado y validado tanto en simulaci´oncomo con datos reales, este framework propone una manera innovadora -tanto en metodolo- gia como en procedimiento- de entender y reaccionar frente a las amenazas potenciales de una misi´onrobotica. v vi Acknowledgements ii Contents List of Figures vii List of Tables xi Glossary xiii Executive summary 1 1 Risk management 9 1.1 Introduction and motivation . 9 1.2 Obstacle sense and avoidance . 11 1.3 Risk management . 11 1.3.1 Risk management architectures . 12 1.3.2 Human cognition . 14 1.3.3 Proposed solution: a cognitive approach . 16 1.4 Goals . 18 2 Risk identification 23 2.1 Introduction . 23 2.2 Legal framework . 24 2.2.1 Common applying normative . 24 2.2.2 UAV regulatory polices . 25 2.2.3 On-going regulation . 27 2.3 Limits statement . 29 2.4 Hazard identification . 31 2.4.1 External hazards . 31 2.4.2 Internal hazards . 32 iii CONTENTS 3 Risk perception 35 3.1 Introduction . 35 3.2 Traditional obstacle detection . 36 3.3 Fundamentals of human perception . 40 3.3.1 Stimuli acquisition . 41 3.3.2 Detection . 42 3.3.2.1 Recognition-by-components theory . 42 3.3.3 Clustering and segregation . 43 3.3.3.1 Gestaltism . 43 3.3.4 Identification . 45 3.3.5 Characteristics extraction and behavioural analysis . 46 3.4 Risk perception processing system . 48 3.4.1 Sensing system . 48 3.4.2 Segmentation and blobbing . 48 3.4.3 Clustering . 51 3.4.4 Identification . 53 3.4.4.1 Tracking . 55 3.4.4.2 Recognition . 60 3.4.5 Characterization and behavioural analysis . 67 3.4.5.1 Load distribution analysis . 69 3.4.5.2 Time to contact . 71 3.4.5.3 Variability analysis . 71 3.4.5.4 Visibility analysis . 73 3.5 Discussion . 76 4 Risk cognition 79 4.1 Introduction . 79 4.2 Formal risk assessment . 80 4.2.1 Seriousness of damage . 80 4.2.2 Probability of damage . 81 4.2.3 Ability to prevent or limit the harm . 81 4.2.4 Risk assessment result . 82 4.3 Fundamentals of human risk cognition . 83 4.3.1 Socio-Anthropological approach . 84 4.3.1.1 The Cultural Theory of Risk . 84 4.3.1.2 Cultural cognition . 86 4.3.1.3 The Social Amplification of Risk (SARF) . 87 4.3.1.4 Situated cognition . 88 4.3.2 Neuropsychological approach . 89 iv CONTENTS 4.3.2.1 Cognitive Psychology . 89 4.3.2.2 Representational/Computational Theory of Mind . 90 4.3.2.3 Heuristic psychology: Intuitive judgment . 91 4.3.2.4 Memory-Prediction Framework . 93 4.3.2.5 Psychometric paradigm . 93 4.3.2.6 The biological evolution of risk preferences . 96 4.3.3 Risk-related cognitive variables . 96 4.3.3.1 Attitude - Voluntariness . 99 4.3.3.2 Benefit perception . 100 4.3.3.3 Confidence . 100 4.3.3.4 Damage distribution . 101 4.3.3.5 Delay Effect . 102 4.3.3.6 Familiarity . 102 4.3.3.7 Feeling evocation . 103 4.3.3.8 Observability . 104 4.3.3.9 Risk knowledge - class membership .
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