This thesis describes the initial development of the Texas A&M Autonomous Ground Vehicle test platform and waypoint following software, including the associated controller design. The original goal of the team responsible for the development of the vehicle was to enter the DARPA Grand Challenge in October 2005. A 2004 Ford F150 4x4 pickup was chosen as the vehicle platform and was modified with a 6” suspension lift and 35” tires, as well as a commercial drive-by-wire system. The waypoint following software, the design of which is described in this thesis, is written in C and successfully drives the vehicle on a course defined by GPS waypoints at speeds up to 50 mph. It uses various heuristics to determine desired speeds and headings and uses control feedback to guide the vehicle towards these desired states. A vehicle dynamics simulator was also developed for software testing. Ultimately, this software will accept commands from advanced obstacle avoidance software so that the vehicle can navigate in true off-road terrain.

abstract: In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs. / Dissertation/Thesis / Doctoral Dissertation Engineering 2015

Master of Science / Department of Biological & Agricultural Engineering / Daniel Flippo / Robotic vehicles have the potential to play a key role in the future of agriculture. For this to happen designs that are cost effective, robust, and easy to use will be necessary. Robotic vehicles that can pest scout, monitor crop health, and potentially plant and harvest crops will provide new ways to increase production within agriculture. At this time, the use of robotic vehicles to plant and harvest crops poses many challenges including complexity and power consumption. The incorporation of small robotic vehicles for monitoring and scouting fields has the potential to allow for easier integration of robotic systems into current farming practices as the technology continues to develop. Benefits of using unmanned ground vehicles (UGVs) for crop scouting include higher resolution and real time mapping, measuring, and monitoring of pest location density, crop nutrient levels, and soil moisture levels. The focus of this research is the ability of a UGV to scout pest populations and pest patterns to complement existing scouting technology used on UAVs to capture information about nutrient and water levels. There are many challenges to integrating UGVs in conventionally planted fields of row crops including intra-row and inter-row maneuvering. For intra-row maneuvering; i.e. between two rows of corn, cost effective sensors will be needed to keep the UGV between straight rows, to follow contoured rows, and avoid local objects. Inter-row maneuvering involves navigating from long straight rows to the headlands by moving through the space between two plants in a row. Oftentimes headland rows are perpendicular to the row that the UGV is within and if the crop is corn, the spacing between plants can be as narrow as 5”. A vehicle design that minimizes or eliminates crop damage when inter-row maneuvering occurs will be very beneficial and allow for earlier integration of robotic crop scouting into conventional farming practices. Using three fixed HC-SR04 ultrasonic sensors with LabVIEW programming proved to be a cost effective, simple, solution for intra-row maneuvering of an unmanned ground vehicle through a simulated corn row. Inter-row maneuvering was accomplished by designing a transformable tracked vehicle with the two configurations of the tracks being parallel and linear. The robotic vehicle operates with tracks parallel to each other and skid steering being the method of control for traveling between rows of corn. When the robotic vehicle needs to move through narrow spaces or from one row to the next, two motors rotate the frame of the tracks to a linear configuration where one track follows the other track. In the linear configuration the vehicle has a width of 5 inches which allows it to move between corn plants in high population fields for minimally invasive maneuvers. Fleets of robotic vehicles will be required to perform scouting operations on large fields. Some robotic vehicle operations will require coordination between machines to complete the tasks assigned. Simulation of the path planning for coordination of multiple machines was studied within the context of a non-stationary traveling salesman problem to determine optimal path plans.

Feature detection and matching is an important step in many object tracking and detection algorithms. This paper discusses methods to improve upon previous work on the SYnthetic BAsis feature descriptor (SYBA) algorithm, which describes and compares image features in an efficient and discreet manner. SYBA utilizes synthetic basis images overlaid on a feature region of interest (FRI) to generate binary numbers that uniquely describe the feature contained within the FRI. These binary numbers are then used to compare against feature values in subsequent images for matching. However, in a non-ideal environment the accuracy of the feature matching suffers due to variations in image scale, and rotation. This paper introduces a new version of SYBA which processes FRI’s such that the descriptions developed by SYBA are rotation and scale invariant. To demonstrate the improvements of this robust implementation of SYBA called rSYBA, included in this paper are applications that have to cope with high amounts of image variation. The first detects objects along an oil pipeline by transforming and comparing frame-by-frame two surveillance videos recorded at two different times. The second shows camera pose plotting for a ground based vehicle using monocular visual odometry. The third generates panoramic images through image stitching and image transforms. All applications contain large amounts of image variation between image frames and therefore require a significant amount of correct feature matches to generate acceptable results.

An autonomous system consisting of an unmanned aerial vehicle (UAV) in cooperation with an unmanned ground vehicle (UGV) is of interest in applications for military reconnaissance, surveillance and target acquisition (RSTA). The basic idea of such a system is to take advantage of the vehicles strengths and counteract their weaknesses. The cooperation aspect suggests that the UAV is capable of autonomously landing on the UGV. A fundamental part of the landing is to localise the UAV with respect to the UGV. Traditional navigation systems utilise global navigation satellite system (GNSS) receivers for localisation. GNSS receivers have many advantages, but they are sensitive to interference and spoofing. Therefore, this thesis investigates the feasibility of autonomous landing in a GNSS-denied scenario. The proposed landing system is divided into a control and an estimation system. The control system uses a proportional navigation (PN) control law to approach the UGV. When sufficiently close, a proportional-integral-derivative (PID) controller is used to match the movements of the UGV and perform a controlled descent and landing. The estimation system comprises an extended Kalman filter that utilises measurements from a camera, an imu and ultra-wide band (UWB) impulse radios. The landing system is composed of various results from previous research. First, the sensors used by the landing system are evaluated experimentally to get an understanding of their characteristics. The results are then used to determine the optimal sensor placements, in the design of the EKF, as well as, to shape the simulation environment and make it realistic. The simulation environment is used to evaluate the proposed landing system. The combined system is able to land the UAV safely on the moving UGV, confirming a fully-functional landing system. Additionally, the estimation system is evaluated experimentally, with results comparable to those obtained in simulation. The overall results are promising for the possibility of using the landing system with the presented hardware platform to perform a successful landing.

Interaction with unmanned ground vehicles have traditionally been done through a lap-top based system. New technology is on the rise which can provide new benefits to operating soldiers, with superimposed information and a more lightweight control unit, namely augmented reality. Designing interfaces for augmented reality systems have seen an improvement but has yet to be widely implemented in various domains. Satisfaction and high user acceptance are aspects that have been identified to be factors for success in the field of human-robot interaction. This thesis intends to explore interface design solutions for interactions with unmanned ground vehicles through augmented reality in head-mounted displays. This has been done through an iterative design process in the form of concept generation and prototyping. The produced prototype has then been evaluated with users to find usability issues and to measure the potential in the prototype to be satisfactory and have a high user acceptance. The evaluation resulted in eight usability issues of which three was critical. The three usability issues are (1) Video module was placed too far down of the user’s view, (2) Difficulties to find modules outside of the view, and (3) Crucial information to distinguish units was non-existent. The prototype did show signs of having potential of being satisfactory and have a high user acceptance, although there are issues which still need to be resolved before this user interface could be used by the military. It is a first step towards integrating augmented reality as a tool when interacting with UGV.

Vehicle characteristics have a significant impact on handling, stability, and rollover propensity. This research is dedicated to furthering the research in and modeling of vehicle dynamics and parameter estimation. Parameter estimation is a challenging problem. Many different elements play into the stability of a parameter estimation algorithm. The primary trade-off is robustness for accuracy. Lyapunov estimation techniques, for instance, guarantee stability but do not guarantee parameter accuracy. The ability to observe the states of the system, whether by sensors or observers is a key problem. This research significantly improves the Generalized Polynomial Chaos Extended Kalman Filter (gPC-EKF) for state-space systems. Here it is also expanded to parameter regression, where it shows excellent capabilities for estimating parameters in linear regression problems. The modeling of ground vehicles has many challenges. Compounding the problems in the parameter estimation methods, the modeling of ground vehicles is very complex and contains many difficulties. Full multibody dynamics models may be able to accurately represent most of the dynamics of the suspension and vehicle body, but the computational time and required knowledge is too significant for real-time and realistic implementation. The literature is filled with different models to represent the dynamics of the ground vehicle, but these models were primarily designed for controller use or to simplify the understanding of the vehicle’s dynamics, and are not suitable for parameter estimation. A model is devised that can be utilized for the parameter estimation. The parameters in the model are updated through the aforementioned gPC-EKF method as applies to polynomial systems. The mass and the horizontal center of gravity (CG) position of the vehicle are estimated to high accuracy. The culmination of this work is the estimation of the normal forces at the tire contact patch. These forces are estimated through a mapping of the suspension kinematics in conjunction with the previously estimated vehicle parameters. A proof of concept study is shown, where the system is mapped and the forces are recreated and verified for several different scenarios and for changing vehicle mass. / Ph. D.

O emprego de veículos terrestres autônomos tem se tornado cada vez mais comum nos últimos anos em aplicações civis e militares. Eles podem ser úteis para as pessoas com necessidades especiais e para reduzir os acidentes de trânsito e o número de baixas em combate. Esta tese aborda o problema da classificação de obstáculos e da localização do veículo em relação a um mapa topológico, sem fazer uso de GPS e de mapas digitais detalhados. Um sensor laser 3D é usado para coletar dados do ambiente. O sistema de classificação de obstáculos extrai as features da nuvem de pontos e usam-nas para alimentar um classificador que separa os dados em quatro classes: veículos, pessoas, construções, troncos de árvores e postes. Durante a extração de features, um método original para transformar uma nuvem 3D em um grid 2D é proposto, o que ajuda a reduzir o tempo de processamento. As interseções de vias de áreas urbanas são detectadas e usadas como landmarks em um mapa topológico. O sistema consegue obter a localização do veículo, utilizando os pontos de referência, e identifica as mudanças de direção do veículo quando este passa pelos cruzamentos. Os experimentos demonstraram que o sistema foi capaz de classificar corretamente os obstáculos e localizar-se sem o uso de sinais de GPS. / The employment of autonomous ground vehicles, both in civilian and military applications, has become increasingly common over the past few years. Those vehicles can be helpful for disabled people and also to reduce traffic accidents. In this thesis, approaches to the problem of obstacles classification and the localization of the vehicle in relation to a topologic map are presented. GPS devices and previous digital maps are not employed. A 3D laser sensor is used to collect data from the environment. The obstacle classification system extracts features from point clouds and uses them to feed a classifier which separates data into four classes: vehicle, people, building and light poles/ trees. During the feature extraction, an original method to transform 3D to 2D data is proposed, which helps to reduce the processing time. Crossing roads are detected and used as landmarks in a topological map. The vehicle performs self-localization using the landmarks and identifying direction changes through the crossing roads. Experiments demonstrated that system was able to correctly classify obstacles and to localize itself without using GPS signals.

O emprego de veículos terrestres autônomos tem se tornado cada vez mais comum nos últimos anos em aplicações civis e militares. Eles podem ser úteis para as pessoas com necessidades especiais e para reduzir os acidentes de trânsito e o número de baixas em combate. Esta tese aborda o problema da classificação de obstáculos e da localização do veículo em relação a um mapa topológico, sem fazer uso de GPS e de mapas digitais detalhados. Um sensor laser 3D é usado para coletar dados do ambiente. O sistema de classificação de obstáculos extrai as features da nuvem de pontos e usam-nas para alimentar um classificador que separa os dados em quatro classes: veículos, pessoas, construções, troncos de árvores e postes. Durante a extração de features, um método original para transformar uma nuvem 3D em um grid 2D é proposto, o que ajuda a reduzir o tempo de processamento. As interseções de vias de áreas urbanas são detectadas e usadas como landmarks em um mapa topológico. O sistema consegue obter a localização do veículo, utilizando os pontos de referência, e identifica as mudanças de direção do veículo quando este passa pelos cruzamentos. Os experimentos demonstraram que o sistema foi capaz de classificar corretamente os obstáculos e localizar-se sem o uso de sinais de GPS. / The employment of autonomous ground vehicles, both in civilian and military applications, has become increasingly common over the past few years. Those vehicles can be helpful for disabled people and also to reduce traffic accidents. In this thesis, approaches to the problem of obstacles classification and the localization of the vehicle in relation to a topologic map are presented. GPS devices and previous digital maps are not employed. A 3D laser sensor is used to collect data from the environment. The obstacle classification system extracts features from point clouds and uses them to feed a classifier which separates data into four classes: vehicle, people, building and light poles/ trees. During the feature extraction, an original method to transform 3D to 2D data is proposed, which helps to reduce the processing time. Crossing roads are detected and used as landmarks in a topological map. The vehicle performs self-localization using the landmarks and identifying direction changes through the crossing roads. Experiments demonstrated that system was able to correctly classify obstacles and to localize itself without using GPS signals.

With the increasingly prevalent use of robots, this paper presents the design and evaluation of a multifunctional Unmanned Ground Vehicle (UGV) with an adjustable suspension system, overmolding omni-wheels, and a unique tool head pick-up mechanism. The UGV addresses current adaptability, performance, and versatility limitations across various industries, including agriculture, construction, and surveillance. The adjustable suspension system enhances the UGV's stability and adaptability on diverse terrains, and the overmolding omni-wheels improve maneuverability and durability in off-road conditions. The tool head pick-up mechanism allows for the seamless integration of various tools, enabling the UGV to perform multiple tasks without manual intervention. A comprehensive performance evaluation assessed the UGVs' versatility, load capacity, passability, and adaptability. The results indicate that the proposed UGV design successfully addresses current limitations and has the potential to revolutionize various applications in different industries. Further research and development are necessary to optimize the UGV's performance, safety, and cost-effectiveness. / Thesis / Master of Applied Science (MASc)