Characterizing and Evaluating Autonomous Controllers
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Departamento de Autom´atica Programa de Doctorado en Investigaci´on Espacial Characterizing and Evaluating Autonomous Controllers Dissertation written by Pablo Mu~noz Mart´ınez Under the supervision of Dra. Mar´ıa Dolores Rodr´ıguez Moreno International advisors Dr. Amedeo Cesta and Dr. Andrea Orlandini Dissertation submitted to the School of Computing of the Universidad de Alcal´a, in partial fulfilment of the requirements for the degree of Doctor of Philosophy Characterizing and Evaluating Autonomous Controllers 2016 \The Viking Lander is a superbly instrumented and designed ma- chine. It extends human capabilities to other and alien land- scapes. By some standards, it's about as smart as a grasshopper, by others, only as intelligent as a bacterium. There's nothing de- meaning in these comparisons; it took nature hundreds of millions of years to evolve a bacterium, and billions of years to make a grasshopper. With only a little experience in this sort of business, we're getting pretty good at it." Carl Sagan Cosmos { episode 5, 1980. Acknowledgements Como en cualquier tesis, el primer agradecimiento no puede ser m´asque para el director, o directora en este caso. En gran medida esta tesis es fruto de Mar´ıa Dolores. El esfuerzo y dedicaci´onque he puesto yo en su confecci´onposiblemente se vea superado por el suyo. No puedo m´asque expresar mi m´asprofunda admiraci´on por sus conocimientos, aptitudes y el entusiasmo que pone en todo lo que hace. Por ello, muchas gracias por permitirme hacer este viaje junto a ti. Tambi´enquiero agradecer a todos mis compa~neros del laboratorio E31 en el que he pasado m´astiempo que en mi propia casa durante los ´ultimos a~nos, con menci´on especial a Yolanda y Javi. El laboratorio sin vosotros se hizo un lugar aburrido. Por suerte he seguido contando con m´asgente de la que poder aprender: Alex y Rub´en primero y ahora Dani, Diego y Fernando. Tambi´enagradecer a los miembros del Intelligent Systems Group por su apoyo, sobre todo al Dr. David F. Barrero que siempre tendr´aun hueco en el laboratorio. Y por supuesto al Dr. Bonifacio Casta~no; espero que disfrutes de tu jubilaci´onen lo m´asalto del mundo. >Y qu´eser´ıa de una tesis sin la familia y amigos? Tengo que dedicar esta tesis a mis padres, Juli´any Florinda y a mi hermana Flor. Siempre hab´eis estado ah´ı d´andome apoyo en los buenos y malos momentos. Por ello esta tesis tambi´en es vuestra. Aunque para sobrellevar los peores momentos nada mejor que el baloncesto, jugando en Rucker junto a Xuma, Juampa, Alberto y el resto de la plantilla que tanto ha cambiado estos a~nos. During this thesis I spent some time in Rome with the PST group at ISTC-CNR. I want to thank all the PST staff, but specially to the senior members: Amedeo, Andrea, Riccardo and Angelo. You form an excellent group and I hope I can continue working and learning from you; this thesis is only a first step. Finally, I want to thank to the ESA's technical officer Mr. Michel Van Win- nendael for his continuous support. I really appreciate his comments during every stage of this thesis. Also, I appreciate the help from the people of the Automation and Robotics Section in ESA-ESTEC, with emphasis to Mart´ın Azkarate and Carlos Crespo and their valuable knowledge about robotics. This thesis was supported by the European Space Agency under the Networking and Partnering Initiative titled Cooperative Systems for Autonomous Exploration Missions, contract 4000106544/12/NL/PA in collaboration with ISTC-CNR (Italy). I Abstract Autonomy in robotics by means of Artificial Intelligence (AI) Planning & Schedul- ing (P&S) is a widely research area with great interest in applications such as ex- ploration robots in hazardous or human unreachable areas. However, autonomous controllers for robotics are usually not well assessed. For instance, it is not easy to compare newer assets with previous works in the field. In this thesis we propose a framework, called On-Ground Autonomy Test Environment (OGATE), to support testing and assessment of autonomous controllers. It is supported on a methodology and a set of generally applicable and domain independent metrics to generate objec- tive evaluations, and a software tool to enable automatic benchmarking processes. To demonstrate the effectiveness of the framework, we exploit two autonomous con- trollers based on different P&S paradigms. First, the Goal Oriented Autonomous Controller (GOAC) developed under an European Space Agency (ESA) contract. Second, the Model-Based Architecture (MOBAR) developed during this PhD that exploits a different paradigm for autonomy. Particularly, MOBAR is designed with the objective of testing different Planning Domain Definition Language (PDDL) based planners to achieve on-board autonomy. In this regard, we also introduce a new planner, Unified Path Planning and Task Planning Architecture (up2ta), that integrates a state of the art PDDL planner with path planning algorithms. The objective of up2ta is to produce efficient plans for robotics exploration missions. Re- garding to the path planning algorithm, in this thesis we introduce two algorithms focused on mobile robots: S-Theta* that effectively reduces the heading changes of the path, and the 3D Accurate Navigation Algorithm (3Dana) that deals with Digital Terrain Models (DTMs) and traversability cost maps to produce safer and reachable paths in realistic environments. Given both controllers, OGATE has been success- fully exploited to evaluate them, allowing to characterize relevant aspects about the integration between Planning & Execution (P&E) that are hardly to be assessed in other ways. Moreover, the results are reproducible and objective, enabling compari- son among controllers with different P&S technologies and paradigms. III Extended abstract The expectations for robotic systems have evolved from the classical teleoperation paradigm of the previous century to the current aim of fully autonomous robots in- teracting with each other, being able to take their own decisions. However, although progress over the past few decades has been significant, such full autonomy still re- mains an open issue, partially due to the extensive variability and complexity of the scenarios to operate within. Reaching high autonomy levels can be justified in scenar- ios such as planetary and underwater exploration or natural disasters, as scenarios in which the human presence is rather than complicated, while also teleoperation may be infeasible. In recent time the European Space Agency (ESA) has started to explore possibil- ities for autonomy on board in different internal activities and studies. In particular, they have produced the Goal Oriented Autonomous Controller (GOAC) architec- ture, which is a prototype for robotics autonomy in space missions. GOAC can be modified in order to test on-board Artificial Intelligence (AI) Planning & Schedul- ing (P&S) techniques to provide goal oriented autonomy. Moreover, GOAC can be customized exploiting different autonomy levels and P&S systems, allowing other research groups not to start from scratch but to take advantage of the work done. For this reason this PhD starts from ESA assets in robotics to investigate open problems. In particular, we propose research effort to better explore the evaluation aspects of different autonomy architectures, to study efficient reaction policies when executing the goals and allow operators to focus on what they want the mission to do. In this direction, a main objective is to generate a framework that deals with autonomous controllers evaluation, assessing aspects related to the P&S system and its integration in robotics. With the objective of providing a wider support to verify the generated frame- work, this PhD provides the description of a new autonomous controller called Model- Based Architecture (MOBAR), that has similar functionalities as GOAC, but employ- ing different technologies and paradigms for P&S. From this robotic controller, this thesis also addresses the decisional capabilities of such controllers. Particularly, the deliberative layer has to produce feasible and safe plans to guide the execution to achieve the mission goals. In this extent, robotics applications in planetary surfaces require navigation planners that take into consideration terrain constraints to safely achieve the target locations. Then, path planning capabilities seems to be a required component in an autonomous mobile robot when facing long term missions. V For the generation of safer routes two aspects have been covered. First, as some robotics have limited turning capabilities, a new path planning algorithm, called S- Theta*, has been produced. This algorithm effectively reduces the heading changes of the robot respect to its former one, Theta*. From this point, classical path planning algorithms usually exploit a loosely realistic 2D terrain discretisation, being in the best case a traversability cost map. However, this seems not to be enough to provide safer paths in uneven terrains. Then, a second assess produced in this thesis is the 3D Accurate Navigation Algorithm (3Dana), a path planning algorithm that provides support to deal with traversability cost maps as well as Digital Terrain Models (DTMs). DTMs provide realistic 3D surfaces, so using them it is possible to extract paths considering the terrain relief. 3Dana evolves from S-Theta*, and provides different parametrizations that allows generation of smooth paths, while also avoids excessive terrain slopes that cannot be reached by the robotic platform. However, just providing a safe route between two points does not guarantee opti- mality in large missions with several targets. For this reason, we have merged a state of the art Planning Domain Definition Language (PDDL) planner with the proposed path planning algorithms. This new deliberative, called Unified Path Planning and Task Planning Architecture (up2ta), interleaves task planning and path planning for optimal sequencing of activities taking into consideration the robot paths.