Recent advances in technology have enabled the development of truly autonomous ground vehicles capable of performing complex navigation tasks. As a result, the demand for practical unmanned ground vehicle (UGV) systems has increased dramatically in recent years. Central to these developments is maturation of emerging mobile robotic intelligent and autonomous capability. While the progress UGV technology has been substantial, there are many challenges that still face unmanned vehicle system developers. Foremost is the improvement of perception hardware and intelligent software that supports the evolution of UGV capability. The development of a Next-generation Experimentation Robotic Vehicle (NERV) serves to provide a small UGV baseline platform supporting experimentation focused on progression of the state-of-the-art in unmanned systems. Supporting research and user feedback highlight the needs that provide justification for an advanced small UGV research platform. Primarily, such a vehicle must be based upon open and technology independent system architecture while exhibiting improved mobility over relatively structured terrain. To this end, a theoretical kinematic model is presented for a novel two-body multi degree-of-freedom, four-wheel drive, small UGV platform. The efficacy of the theoretical kinematic model was validated through computer simulation and experimentation on a full-scale proof-of-concept mobile robotic platform. The kinematic model provides the foundation for autonomous multi-body control. Further, a modular system level design based upon the concepts of the Joint Architecture for Unmanned Systems (JAUS) is offered as an open architecture model providing a scalable system integration solution. Together these elements provide a blueprint for the development of a small UGV capable of supporting the needs of a wide range of leading-edge intelligent system research initiatives. / Master of Science

Teleoperation of an unmanned vehicle is a challenging task for human operators especially when the vehicle is out of line of sight. Improperly designed and applied display interfaces directly affect the operation performance negatively and even can result in catastrophic failures. If these teleoperation missions are human-critical then it becomes more important to improve the operator performance by decreasing workload, managing stress and improving situational awareness. This research aims to develop electrical and control system of an unmanned ground vehicle (UGV) using an All-Terrain Vehicle (ATV) and validate the development with investigation of the effects of force feedback devices on the teleoperation performance. After development, teleoperation tests are performed to verify that force feedback generated from the dynamic obstacle information of the environment improves teleoperation performance. Results confirm this statement and the developed UGV is verified for future research studies. Development of UGV, algorithms and real system tests are included in this thesis.

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.

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.

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)

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The research on autonomous vehicles, and more specifically cooperation between autonomous vehicles, has become a prominent research field during the last cou- ple of decades. One example is the combination of an unmanned aerial vehicle (UAV) together with an unmanned ground vehicle (UGV). The benefits of this are that the two vehicles complement each other, where the UAV provides an aerial view and can reach areas where a ground vehicle can not. Furthermore, since the UAV has a limited range, the UGV can then serve as transport and recharge sta- tion for the UAV. This master thesis studies how model predictive control (MPC) can be used to land a UAV on a moving UGV.  A linear MPC is chosen, since previous work using this has shown promising results. The UAV is chosen to be controlled using commands in pitch, roll and climbing rate. The MPC is designed as a decoupled controller, with a separate horizontal and vertical controller. This allows for a spatial constraint to be im- plemented, which constrains the UAV from entering ground level before arriving above the UGV. It also constrains the UAV from potentially hitting protruding ob- jects on the UGV. The horizontal controller uses a simple planner, which guides the UAV to land on the UGV from behind.  The MPC is evaluated using a additive white Gaussian noise (AWGN) sen- sor error model with zero mean. The scenario used is that the UAV starts 50 m from the UGV, and the UGV starts driving in a given direction turning randomly. The MPC lands successfully in 100 % of the simulations for a wide range of tun- ings. The MPC maintains the same landing statistics with a delay in the sensor information of up to 500 ms. The AWGN could be increased while maintaining successful landings, however with significantly more retakes and longer landing times. Lower AWGN variance only slightly improves performance, suggesting that the MPC is quite robust towards high variance in the state estimation.  The MPC is also compared to a PID controller. The MPC has significantly shorter landing times. The PID has a more oscillatory control signal, however, the PID has a lower variance in landing positions, but a slightly less centered mean on the UGV. The overall results show that an MPC can be used to achieve a flexible controller that can be tuned and reformulated to fit the situation, and performs as good or better compared to a PID controller.  The hardware tests show promising results for the implementation of the MPC. The controller is not tuned and no system identification is done specifi- cally for the physical UAV, suggesting that the controller is robust for varying settings. Even though the UAV never lands on the UGV, the visual behavior and control signal plots suggest that it would be able to land. However, these tests are performed using global navigation satellite system state estimation on a sta- tionary UGV, therefore further tests need to be performed in more challenging scenarios.

A system of an unmanned aerial vehicle (UAV) collaborating with an unmanned ground vehicle (UGV) for use in for example surveillance, reconnaissance, transport and target acquisition is studied. The project investigates the problem of estimating the relative position, velocity and orientation between the UAV and the UGV required to autonomously land the UAV on the UGV during movement. The use of global navigation satellite system (GNSS) receivers are not considered since they are sensitive to interference and spoofing attacks.  The developed estimation system consists of an extended Kalman filter (EKF) using measurements from several sensors, including: a camera, barometers, inertial measurement units (IMUs) and impulse-radio ultra-wide bandwidth (IRUWB) transceivers. Primarily the use of near infrared (NIR) light emitting diodes (LEDs) attached to the UGV and a camera on the UAV is investigated. Several configurations of LED placements, and what errors to expect when measuring them with the camera, are evaluated. The performance is evaluated in both simulations and hardware sensor tests, but no live experiments that include any autonomous landing manoeuvre are conducted.  The results indicate that high estimation precision can be achieved, at close range the errors in position average below 2 cm and in orientation under 0.5 degrees. However, some problems arise from the detection and identification of the LEDs. Further, if measurements of the LEDs are completely missing, the estimation precision suffers due to error accumulation in the inertial navigation. These results indicate that autonomous landing is possible, since the amount of LED measurements and consequently also the estimation precision increases as the relative position decreases.