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El proyecto consiste en el diseño y la construcción de un robot Gusano que sea capaz de desplazarse en línea recta sin el uso de patas ni ruedas. El robot Gusano imitará el movimiento zigzagueante de estos seres invertebrados basándose en modelos matemáticos que serán explicados en ésta tesis. Asimismo, de forma alternativa, el robot Gusano desarrollará un desplazamiento adoptando la forma de una rueda para poder recorrer a mayores velocidades sobre superficies planas. Sobre las dimensiones del robot Gusano, ésta tendrá cuarenta y cinco centímetros (45 cm.) de longitud y constará de ocho (08) eslabones. De estos ochos eslabones, seis serán eslabones totalmente modulares y los otros dos eslabones establecerán la cabeza y cola del robot. El material que se empleará será el más idóneo para la presente aplicación. Para lograr las secuencias de movimientos del robot Gusano, se analizará la locomoción natural de dichos invertebrados. Se desarrollarán modelos matemáticos que reproduzcan dichos movimientos de manera eficiente y, posteriormente, estos serán probados mediante programas por computadora. Finalmente, las secuencias con mejores resultados de desplazamiento serán grabadas en una memoria que llevará el robot para su desplazamiento autónomo. Para que el robot Gusano pueda desplazarse de manera autónoma, este poseerá una tarjeta electrónica que se encargará principalmente de controlar los motores del robot. Dicha tarjeta constará principalmente de un microcontrolador, una memoria EEPROM para almacenar las secuencias de movimiento y un módulo inalámbrico para lograr el comando inalámbrico del robot. El microcontrolador controlará siete (07) motores, los cuales se encargarán de generar los movimientos para cada articulación. El programa del microcontrolador tendrá la capacidad de controlar hasta dieciséis (16) motores para futuras ampliaciones y aprovechar su diseño modular. Asimismo, las secuencias de movimientos del robot serán guardadas de forma permanente en una memoria tipo EEPROM. El microcontrolador se encargará de leer las secuencias de movimiento desde la memoria y, con ella, generar las señales de control para los motores. Gracias al módulo de comunicación inalámbrica, el robot podrá ser controlado a distancia. El microcontrolador se encargará de obedecer las instrucciones recibidas desde el mando. En lo concerniente al mando, se diseñará una tarjeta electrónica que poseerá un módulo inalámbrico y será gobernada desde una computadora a través su interfaz serial RS232. A dicha computadora se conectará una consola de videojuegos con interface USB y, con la ayuda de un programa desarrollado en esta tesis, el robot será comandado de una manera más sencilla.

Master of Science / Department of Mechanical and Nuclear Engineering / Dale Schinstock / Developments in farm related technology have increased the importance of mapping individual plants in the field. An automated mapping system allows the size of these fields to scale up without being hindered by time-intensive, manual surveying. This research focuses on the development of a mapping system which uses geo-located images of the field to automatically locate plants and determine their coordinates. Additionally, this mapping process is capable of differentiating between groupings of plants by using Quick Response (QR) codes. This research applies to green plants that have been grown into seedlings before being planted, known as transplants, and for fields that are planted in nominally straight rows. The development of this mapping system is presented in two stages. First is the design of a robotic platform equipped with a Real Time Kinematic (RTK) receiver that is capable of traversing the field to capture images. Second is the post-processing pipeline which converts the images into a field map. This mapping system was applied to a field at the Land Institute containing approximately 25,000 transplants. The results show the mapped plant locations are accurate to within a few inches, and the use of QR codes is effective for identifying plant groups. These results demonstrate this system is successful in mapping large fields. However, the high overall complexity makes the system restrictive for smaller fields where a simpler solution may be preferable.

Robots have long been used for completing tasks that are too difficult, dangerous, or distant to be accomplished by humans. In many cases, these robots are highly specialized platforms - often expensive and capable of completing every task related to a mission's objective. An alternative approach is to use multiple platforms, each less capable in terms of number of tasks and thus significantly less complex and less costly. With advancements in embedded computing and wireless communications, multiple such platforms have been shown to work together to accomplish mission objectives. In the extreme, collections of very simple robots have demonstrated emergent behavior akin to that seen in nature (e.g., bee colonies) motivating the moniker of ''swarm robotics'' - a group of robots working collaboratively to accomplish a task. The use of robotic swarms offers the potential to solve complex tasks more efficiently than a single robot by introducing robustness and flexibility to the system. This work investigates localization in heterogeneous and autonomous robotic swarms to improve their ability to carry out exploratory missions in unknown terrain. Collaboratively, these robots can, for example, conduct sensing and mapping of an environment while simultaneously evolving a communication network. For this application, among many others, it is required to determine an accurate knowledge of the robot's pose (i.e., position and orientation). The act of determining the pose of the robot is known as localization. Some low cost robots can provide location estimates using inertial measurements (i.e., odometry), however this method alone is insufficient due to cumulative errors in sensing. Image tracking and wireless localization methods are implemented in this work to increase the accuracy of localization estimates. These localization methods complement each other: image tracking yields higher accuracy than wireless, however a line-of-sight (LOS) with the target is required; wireless localization can operate under LOS or non-LOS conditions, however has issues in multipath conditions. Together, these methods can be used to improve localization results under all sight conditions. The specific contributions of this work are: (1) a concept of 'shared sensing' in which extremely simple and inexpensive robots with unreliable localization estimates are used in a heterogeneous swarm of robots in a way that increases the accuracy of localization for the simple agents and simultaneously extends the sensing capabilities of the more complex robots, (2) a description, evaluation, and discussion of various means to estimate a robot's pose, (3) a method for increasing reliability of RSSI measurements for wireless ranging/localization systems by averaging RSSI measurements over both time and space, (4) a process for developing an in-field model to be used for estimating the location of a robot by leveraging the existing wireless communication system.