<p> The past decade has seen the rapid development and deployment of unmanned systems throughout the world in both civilian and military applications. Significant development has been led by the Department of Defense (DoD), which has sought to develop and field military systems, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), with elevated levels of autonomy to accomplish their mission with reduced funding and manpower. As their role increases, such systems must be able to adapt and learn, and make nondeterministic decisions. Current unmanned systems exhibit minimal autonomous behaviors. As their autonomy increases and their behaviors become more intelligent (adapting and learning from previous experiences), the state space for their behaviors becomes non deterministic or intractably complex. </p><p> Consequently, fielding such systems requires extensive testing and evaluation, as well as verification and validation to determine a system&rsquo;s performance and the acceptable level of risk to make it releasable &ndash; a challenging task. To address this, I apply a novel systems perspective to develop a model-based framework to predict future system performance based on the complexity of the operating environment using newly introduced complexity measures and learned costs. Herein I consider an autonomous military ground robot navigating in complex off-road environments. Using my model and data from Defense Advanced Research Projects Agency (DARPA)-led experiments, I demonstrate the accuracy with which my model can predict system performance and then validate my model against other experimental results.</p>

<p> This thesis explores three topics in the field of cost estimation for Unmanned Autonomous Systems. First, we propose a common definition of an Unmanned Autonomous System. We accomplish this through exhausting the literature in the areas cost estimation, autonomy in its current form, and how such advanced systems might be integrated into their environment. Second, we introduce a method to estimate the cost of Unmanned Autonomous Systems utilizing existing parametric cost estimation tools: SEER &ndash; HDR, COCOMO II, COSYSMO, and two cost estimating relationships &ndash; weight and performance. This discussion is guided by focusing on how current tools attempt to account for emergent systems. We also attempt to address challenges surrounding autonomy. To address these challenges from a cost perspective, this thesis recommends modifications to parameters within COCOMO II &ndash; via the use of object-oriented function points in lieu of current methods, and COSYSMO &ndash; via the introduction of two cost drivers namely, TVED and HRI-T. Third, we conduct analysis on four current Army Unmanned Autonomous Systems in an attempt to establish early trends within existing estimates. Finally, we explore areas of further research and discuss the implications of how pursing a more adequate cost model will lead to a better understanding of this ill-defined paradigm. </p><p> *This material is based upon work supported by the Naval Postgraduate School Acquisition Research Program under Grant No. N00244-15-1-0008. The views expressed in written materials or publications, and/or made by speakers, moderators, and presenters, do not necessarily reflect the official policies of the Naval Postgraduate School nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.</p>

<div>In the years to come, a growing global population will require more crop production than ever before. As technological advances improve across all industries, autonomous agricultural vehicles (AAVs) can be part of the solution to the rising demand for food. By improving and transforming conventional farming methods, AAVs have the potential to transform the way farming operations are completed. AAVs are a class of robotic machines that have the ability to complete agricultural tasks without requiring direct and constant control of a human operator. By removing the need for an operator, these agricultural robotic machines allow for new vehicle designs and new opportunities for different vehicle configurations and sizes. </div><div><br></div><div>A simulation model was developed that calculates the energy requirements of AAVs operating on row crops. This deterministic model was used to quantify the energy needs and energy expenditures of agricultural vehicles, and to investigate the effects of using AAVs in lieu of conventional human-operated agricultural machinery.</div><div><br></div><div>The energy model was demonstrated using a pre-defined scenario of a typical row-crop farming operation in the Midwest U.S. The purpose of the case study was to compare a conventional crop production operation with operations that have implemented autonomous machines. Four general vehicle configurations were chosen based on the traction machine size: large tractors (e.g.,~greater than 60 kW), small tractors (e.g.,~less than 60 kW), utility vehicles (e.g.,~John Deere Gator), and single row machines. The complete crop production operation was based on a farm size of 607 ha (1,500 acre) with half the land devoted to corn production. The four main operations were fertilizer application, pesticide spraying, no-till planting, and harvesting.</div><div><br></div><div>First, the energy model was used to compare a whole farm operation with three different machine configurations: using all conventional large machines, using all autonomous large machines, and using all autonomous smaller machines (55 kW tractor). The results show that from an energy standpoint, the most significant savings comes from the decreased amount of agrochemical application associated with AAVs. The total energy consumption of the large tractor AAV configuration is 36% less than the conventional operation (11,081 MJ/ha vs. 7,090 MJ/ha).</div><div><br></div><div>In order to have a better perspective on the effects of using AAVs, further analysis was conducted on an individual operation basis: fertilizing, pesticide spraying, no-till planting, and harvesting. Because AAVs can work 24-hours per day, the fertilizing operation for the single large tractor AAV could be completed in 1.6 working days, as opposed 2.4 working days for the conventional machine. It only required two small tractor AAVs to meet or exceed the performance of the conventional machine, yet for the same amount of money, four to five small tractor AAVs could be purchased.</div><div><br></div><div>The greatest benefit to utilizing AAVs is the intelligent application of pesticide, which can allow for 65--95% reduction in chemical use. The spraying operation highlighted the advantages of large machines (conventional and autonomous), namely speed of operation and width. It takes two small tractor AAVs, seven utility AAVs, or 12 single-row AAVs to match their performance. </div><div><br></div><div>For the no-till planting operation, two small tractor AAVs, seven utility AAVs, or 39 single-row AAVs are required to match the performance of conventional machinery. However, for the same cost as the conventional machine, six small tractor AAVs, 16 utility AAVs, or 55 single-row AAVs could be purchased. The benefit of using higher numbers of AAVs is seen in the amount of time required to complete the planting task, where the swarms of AAVs could finish planting in nearly 1/3 of the time.</div><div><br></div><div>Harvesting was previously analyzed during the whole farm scenario. In general, the energy consumption and costs are relatively the same between the conventional machine and the large AAV. The advantage of the autonomous harvesting is that is can operate continuously throughout the night. Continuous operation is possible for this scenario because corn can be harvested at night. However, soybeans cannot because the onset of dew at dusk does not allow for proper processing of the crop. </div><div><br></div><div>Along with the energy model, crop production efficiency metrics were studied that provided an objective method of analyzing the advantages and disadvantages associated with replacing and/or augmenting conventional farming vehicles with AAVs. Energy-per-unit-area shows the amount of energy that is consumed over the entire field, regardless of the task time required. Because labor energy consumption is insignificant compared to the other three inputs, energy-per-unit-area is also independent of the number of machines simultaneously in use. Working days and machinery capital cost are other metrics that proved beneficial when comparing AAVs to conventional machines.</div><div><br></div><div>Finally, a modeling tool was developed and demonstrated that allows a user to interact with the energy model in an intuitive way. Creating the modeling tool in Microsoft Excel allows for easy distribution to a wide audience, as opposed to using a more expensive software package. The energy model workbook is composed of five spreadsheets that contain instructions, inputs, outputs, and supporting data tables. A GUI was created using Microsoft Excel VBA that lets the user interact with an event-driven program. Data sets can easily be created and modified for the purpose of evaluating different farming operations. Additionally, options within the GUI allow for parameter studies where multiple data sets can be instantly created in order to analyze the effects of changing a single variable. </div>

Whether we want to drive a car, play a ball game, or even enjoy watching a flying bird, we need to track moving objects. This is possible via smooth pursuit eye movements (SPEMs), which maintain the image of the moving object on the fovea (i.e., a very small portion of the retina with high visual resolution). At first glance, performing an accurate SPEM by the brain may seem trivial. However, imperfect visual coding, processing and transmission delays, wide variety of object sizes, and background textures make the task challenging. Furthermore, the existence of distractors in the environment makes it even more complicated and it is no wonder why understanding SPEM has been a classic question of human motor control. To understand physiological systems of which SPEM is an example, creation of models has played an influential role. Models make quantitative predictions that can be tested in experiments. Therefore, modelling SPEM is not only valuable to learn neurobiological mechanisms of smooth pursuit or more generally gaze control but also beneficial to give insight into other sensory-motor functions. In this thesis, I present a neurocomputational SPEM model based on Neural Engineering Framework (NEF) to drive an eye-like robot. The model interacts with the real world in real time. It uses naturalistic images as input and by the use of spiking model neurons controls the robot. This work can be the first step towards more thorough validation of abstract SPEM control models. Besides, it is a small step toward neural models that drive robots to accomplish more intricate sensory-motor tasks such as reaching and grasping.

[EN] The efficiency of free-collision motion planning results very sensible on robot and obstacle modelling technique selected. In this way, many works have been oriented to define models with proper throughput to speed up the collision detection proccess. This dissertation presents a new approach to the problem, whose complexity is reduced notably by means of using enveloping models of real objects, allowing security regions or distances. This objective is reached by means of the definition of a spherical model, composed of infinite spheres, generated from the application of linear or polynomial equations to a reduced number of control spheres, giving the so-called poly-spheres and spheroids respectively. These models, with evident simplicity, present a high modelling power, adapt easily to the requirements need in collision-detection and path planning applications for robotics systems. In order to represent a complete multi-robot cell, an extended hierarchical structure has been defined, in form of an AND-OR graph, with different degrees of accuracy, according to the different approximation model used. In order to generate automatically this structure, a procedure has been developed to compute the minimum volume enveloping spherical model in an off-line process with two levels based on Downhill Simplex method and Hough transform. This procedure can be greatly speed up by using clustering techniques to obtain appropiate initial conditions, allowing an on-line use. With a hierarchical structure computed in such a way, a fast procedure for collision detection in a multi-robot cell is introduced, based on several algorithms for distance computation including polyspheres and spheroids. This methodology presents a fast and anticipativa response, in the sense that every movement of a system has been validated before its execution, implying that not necessarily must be done in an off-line simulation. The use of spherical models, in addition to their fast distance computation, results suitable for the definition of artificial potential fields allowing a path planning in robotics systems with up to six degrees of freedom, including three for translation and three for rotation. The definition of these new potential fields and the study of new planning techniques based on classical optimisation methods allow their application straight forward in Cartesian space, with all their advantages. Last but not least, with the help of some systems for robot programming, simulation and control, the correctness of these contributions have been validated in a set of prototype applications, covering from robot-obstacle and multi-robot collision detection, to motion planning for a robot-arm or an auto-guided vehicle. / [ES] La eficiencia de la planificación de movimientos libres de colisión resulta muy sensible al modelado de los robots y obstáculos que se consideren, por lo que, frente al modelado tradicional con politopos, muchos trabajos en robótica han estado orientados a la definición de unos modelos que presenten buenas prestaciones de cara a acelerar el proceso de detección de colisiones. En esta Tesis se presenta una nueva perspectiva del problema, cuya complejidad queda reducida notablemente al utilizar envolventes de los objetos reales, lo que permite definir zonas o distancias de seguridad. Para ello se han definido unos modelos esféricos, compuestos de infinitas esferas generadas a partir de la aplicación de unas relaciones lineales o polinómicas a un número reducido de esferas de control, dando lugar a las llamadas poli-esferas y esferoides respectivamente. Estos modelos, de sencillez clara, presentan una potencia de modelado elevada, adaptándose fácilmente a los requisitos necesarios en las aplicaciones de detección de colisiones y planificación de movimientos en sistemas robotizados. Para la representación de una célula multi-robot completa, se ha definido una estructura jerárquica extendida, en forma de grafo AND-OR, con diferentes grados de precisión, mediante diferentes modelos de aproximación. De cara a generar automáticamente esta estructura, se ha desarrollado un procedimiento para generar el modelo esférico envolvente de mínimo volumen en un proceso off-line con dos niveles, basados en el método de minimización Downhill Simplex y en la transformada de Hough. Este procedimiento se acelera enormemente al utilizar técnicas de agrupamiento para obtener condiciones iniciales apropiadas, permitiendo su uso on-line. Con una estructura jerárquica generada de esta forma, se introduce un procedimiento rápido de detección de colisiones aplicable a una célula multi-robot, basado en algoritmos básicos de cálculo de distancias que pueden considerar poli-esferas y esferoides. Esta metodología presenta una respuesta rápida y anticipativa, entendiendo por tal que todo movimiento de cualquier sistema ha sido validado antes de su ejecución, por lo que no necesariamente debe realizarse en una simulación off-line. La utilización de modelos esféricos, así como el rápido cálculo de distancias entre ellos, resulta idónea para la definición de campos potenciales artificiales que permitan una planificación de movimientos en sistemas robotizados con hasta seis grados de libertad, incluyendo tres de traslación y tres de rotación. La definición de estos nuevos campos potenciales y el estudio de nuevas técnicas de planificación basados en métodos clásicos de optimización permiten su aplicación directamente en el espacio cartesiano, con las claras ventajas que esto conlleva. Finalmente, con la ayuda de varios sistemas de programación, simulación y control de robots, se ha demostrado la validez de estas aportaciones en una serie de aplicaciones prototipo que van desde la detección de colisiones de un robot con un obstáculo o entre sistemas multi-robot, a la planificación de movimientos de un brazo-robot o un vehículo autoguiado. / Mellado Arteche, M. (1996). Modelado, detección de colisiones y planificación de movimientos en sistemas robotizados mediante volúmenes esféricos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56621

Orientador: Prof. Dr. Luiz de Siqueira Martins Filho / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Mecânica, 2014.

This master thesis is focused on Modular Self-Reconfigurable Robotic Systems. Their description is made at first and then possibilities of their use are listed. The next chapter concerns Molecubes modular system. The design of similar system where the construction of the individual modules is described follows. The transformations of coordinated systems in the individual modules are described and the calculation of forward kinematics and simulation of inverse kinematics is made at the end of the thesis.