Unmanned vehicles, both ground and aerial, have become prevalent in recent years. The research community has different needs than the industrial community when designing a finalized unmanned system since the vehicle, the sensors and the control design are dynamic and change frequently as new ideas are developed and implemented. Current autopilot hardware, which is available as on-the-market products and proposed in research, is sufficient for unmanned systems design. However, this equipment falls short of being able to accommodate the needs of those in the research community who must be able to quickly implement new ideas on a flexible platform. The contribution of this research is the realization of a hardware platform, which provides for rapid implementation of newly developed theory. Rapid implementation is gained by providing for software development from within the Simulink environment and utilizing previously unrealized flexibility in sensor selection. In addition to the development of the hardware platform, research was performed within Simulink's System Generator environment in order to complement the hardware. The software produced consists of a user template that integrates to the selected hardware. The template creates a user friendly environment, which provides the end user the capability to develop software algorithms from within the Simulink environment. This capability facilitates the final step of full hardware implementation. The major novelty of the research was the overall FPGA based autopilot design. The approach provided flexibility, functionality and generality. The approach is also suitable for and applicable to the design of multiple platforms. This research yielded a first time approach to the development of an unmanned systems autopilot platform by utilizing: -Development of programmable voltage level digital Input/Output (I/O), ports, -Utilization of Field Programmable Analog Arrays (FPAA), -Hardware capabilities to allow for integration with full computer systems, -A full Field Programmable Gate Array (FPGA), implementation, -Full integration of the hardware within Simulink's System Generator Toolbox

Cette thèse a pour but de proposer une formulation EF adaptée à la modélisation de l'usinage à grande vitesse en pointe d'outil. La simulation doit pouvoir détecter les phénomènes extrêmement localisés qui peuvent apparaître, avec des temps de calcul raisonnables. L'implémentation est réalisée à l'aide de la librairie EF CimLib, offrant un remailleur adaptatif robuste non structuré et permettant le calcul massivement parallèle. Le travail se décompose en deux parties. La première consiste à développer, implémenter et valider une formulation de type explicite non sensibles au locking volumique, permettant l'utilisation d'algorithmes de remaillage non structurés robustes. Deux formulations sont comparées en dynamique rapide: une semi-explicite, basée sur des éléments tétraédriques mixtes avec stabilisation bulle et une explicite, basée sur des éléments tétraédriques linéaires modifiés. La seconde partie consiste à appliquer la formulation explicite, retenue pour son efficacité, au cas particulier de l'usinage à grande vitesse. Une résolution thermique est implémentée et couplée à la mécanique. Des simulations thermomécaniques de coupe orthogonales 2D de Ti6Al4V sont réalisées. Les résultats obtenus sont en très bonne adéquation avec la littérature, et permettent d'importants gains de temps de calcul. L'initiation et la propagation de la bande de cisaillement dans le copeau peuvent être analysés précisément. Un algorithme de R-Adaptation surfacique a été ajouté afin d'étendre ces résultats en 3D. On peut alors se rapprocher de la réalité industrielle du procédé sans perdre en précision.

There are a number of professions in which exposure to life threatening risks is part of daily routine and robots could possibly be used to avoid some of these. In fact, there are applications in which this is already done, the most prominent being bomb disposal and mine clearing. The user testing of new technology is part of achieving similar benefits for other tasks. Methods for use need to be explored, technical solutions have to be trialed, and advantages gained must be compared to the loads imposed in order to guide future development and to determine if the new tools are ready to be deployed. This thesis has performed such feasibility tests on robots within Military Operations in Urban Terrain (MOUT). The aim has been to gain a comprehensive view of a potential user and to embed a robot amongst them in order to assess its tactical feasibility and evaluate its technical performance. An army company specialized in urban operations made up the primary user group and an iRobot Packbot Scout was the robot system in focus. Setting up the tests included identifying and modifying a number of the company’s standard behaviors to include the robot. During the two tests, which lasted over a period of three and six months respectively, it was up to the users to deploy the robot as they considered appropriate. It was found that the military rely on precise and thoroughly trained actions that can be executed with a minimum of ambiguity. Gaining similar efficiency with robots will require tactical optimization over several years. The most common application during the tests was exploration inside buildings in situations where an enemy presence was uncertain and time was not critical. Deploying the robot took more time and was less precise than traditional methods. In return it kept the soldiers out of harm’s way and enabled them to decrease weapon deployment. The range of the radio link, limited video feedback, and the operator control unit were the features constraining the system’s overall performance the most. Other properties, such as the robot’s ruggedness, size, weight, terrain ability and endurance of the robot, on the other hand, proved to match the application. The test unit was of the opinion that robots such as the Packbot Scout would be valuable to have as a standard feature. Four additional users groups were surveyed to examine to what extent the gained results had general validity for high-risk professionals. The most extensive of these included embedding a Packbot into a Special Weapons And Tactics (SWAT) police team for five months. It was found that the robot could be used during negotiation if upgraded with two-way audio. Further technical adaptations would also enable deployment during long term surveillance and for deploying non-lethal weapons. Explosive Ordnance Disposal (EOD), firefighting, and Chemical Biological Radiological and Nuclear Contamination Control (CBRN) were the other groups surveyed. These were investigated by means of interviews and observations during 1-2 days. It was found that while the five professions share many demands they also have unique needs which prevents a single type of robot from being satisfactory for all of them. The tasks within EOD and fire fighting includes grasping and moving objects of up to 50-70 kg. The MOUT, CBRN and SWAT applications are less dependent on the grasping ability, but require a robot that can be easily transported and which is able to access narrows. / QC 20100806

<p>The goal of this thesis is to make an unmanned ground vehicle (UGV) follow a given reference trajectory, without colliding with obstacles in its way. This thesis will especially focus on modeling and controlling the UGV, which is based on the power wheelchair Trax from Permobil.</p><p>In order to make the UGV follow a given reference trajectory without colliding, it is crucial to know the position of the UGV at all times. Odometry is used to estimate the position of the UGV relative a starting point. For the odometry to work in a satisfying way, parameters such as wheel radii and wheel base have to be calibrated. Two control signals are used to control the motion of the UGV, one to control the speed and one to control the steering angles of the two front wheels. By modeling the motion of the UGV as a function of the control signals, the motion can be predicted. A path following algorithm is developed in order to make the UGV navigate by maps. The maps are given in advance and do not contain any obstacles. A method to handle obstacles that comes in the way is presented.</p>

This work describes the design and implementation of a generic lightweight onboard processing system for miniature Unmanned Vehicles (UVs) that is computationally powerful and highly adaptable. First, several classical approaches to giant scale and full size UV onboard processing systems are described along with their corresponding limitations. Second, a detailed study is presented that describes the key characteristics of an onboard system along with associated limitations. Next, an implementation of a generic onboard system capable of vision processing and servo based control is presented along with detailed hardware specifications and implementation software. Last, experimental data, both laboratory and field, are presented to show validation of the onboard processing system design, functionality, and key characteristics presented. Two primary contributions are made in this work. i) Identification of key characteristics of an onboard system allows for a high level validation of the hardware of an onboard system along with a design template for a reconfigurable, platform independent, processing system for UVs. ii) Detailed design and implementation of an adaptable onboard processing system that is both computationally powerful and easily adapted. This system is validated by showing satisfiability of the design characteristics necessary for an adaptable onboard system along with fully operational field test and their corresponding results.

The goal of this thesis is to make an unmanned ground vehicle (UGV) follow a given reference trajectory, without colliding with obstacles in its way. This thesis will especially focus on modeling and controlling the UGV, which is based on the power wheelchair Trax from Permobil. In order to make the UGV follow a given reference trajectory without colliding, it is crucial to know the position of the UGV at all times. Odometry is used to estimate the position of the UGV relative a starting point. For the odometry to work in a satisfying way, parameters such as wheel radii and wheel base have to be calibrated. Two control signals are used to control the motion of the UGV, one to control the speed and one to control the steering angles of the two front wheels. By modeling the motion of the UGV as a function of the control signals, the motion can be predicted. A path following algorithm is developed in order to make the UGV navigate by maps. The maps are given in advance and do not contain any obstacles. A method to handle obstacles that comes in the way is presented.

In recent years, the market for automatized surveillance and use of unmanned ground vehicles (UGVs) has increased considerably. In order for unmanned vehicles to operate autonomously, high level algorithms of artificial intelligence need to be developed and accompanied by some way to make the robots perceive and interpret the environment. The purpose of this work is to investigate methods for real-time obstacle detection using stereo vision and implement these on an existing UGV platform. To reach real-time processing speeds, the algorithms presented in this work are designed for parallel processing architectures and implemented using programmable graphics hardware. The reader will be introduced to the basics of stereo vision and given an overview of the most common real-time stereo algorithms in literature along with possible applications. A novel wide-baseline real-time depth estimation algorithm is presented. The depth estimation is used together with a simple obstacle detection algorithm, producing an occupancy map of the environment allowing for evasion of obstacles and path planning. In addition, a complete system design for autonomous navigation in multi-UGV systems is proposed.

Unmanned systems (US:s) of various kinds are becoming more and more sought after resources as they do not have an onboard driver, and thus, can be employed in situations that are either dangerous or impossible for humans to engage in. Consequently, US:s are envisioned to play a large role both in civilian and military contexts in the future, which presents new challenges regarding how humans should interact with these new systems. The purpose with this thesis was to explore different ways of interaction between human and artificial agents and how interfaces of autonomous systems supports this interaction. To investigate this, a literature overview of previous research regarding various ways of interacting with unmanned systems was conducted. This illustrated that a multimodal interface offers a more robust and natural form of interaction compared to fixed interaction principles that have their advantages and disadvantages depending on both context and situation. Moreover, a case study was conducted to explore human-autonomy interaction in a realistic battle mission, simulated in Virtual Battle Simulator 3. The results from the study indicate that speech is an essential mode of communication for controlling an unmanned autonomous ground vehicle in a mounted setting. Furthermore, problems were identified with the visual and auditory feedback from the unmanned vehicle in which verbal feedback was identified as a possible solution. Experience regarding both the simulation environment and as a commander of mechanised units was also identified as an important factor to adhere to in future studies.

Unmanned autonomous vehicles, airborne or terrestrial, can be used to solve many varying tasks in vastly different environments. This thesis describes a proposed collaboration between two types of such vehicles, namely unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The vehicles' objective is to traverse unknown terrain in order to access a target area. The exploration of the unknown terrain is in this thesis divided into three parts. These parts are terrain mapping, informative path planning (IPP) for the UAVs and path planning for the UGV. A Gaussian Process (GP) is used to model the terrain. The use of a GP map makes it possible to model spatial dependence and to interpolate data between measurements. Furthermore, sequential update of the map is achieved with a Kalman filter when new measurements are collected. In the second part, IPP is used to decide the best locations for the terrain height measurements. The IPP algorithm will prioritize measurements in locations with uncertain terrain height estimates in order to lower the overall map uncertainty. Finally, when the map is complete, the UGV plans an optimal path through the mapped terrain using A* graph search, while minimizing the total altitude difference for the path and respecting the map uncertainty. Collaborative behavior of autonomous vehicles requires highly accurate position estimates. In this thesis RTK is investigated and its accuracy and precision evaluated for the positioning of autonomous UAVs and UGVs through a series of experiments. The experiments range from stationary and dynamic accuracy to investigation of the consistency of the positioning estimates. The results are promising, RTK outperforms standard GNSS and can be used for centimeter-level accuracy when positioning a UAV in-flight. The proposed exploration algorithms are evaluated in simulations. The results show that the algorithms successfully solves the task of mapping and traversing unknown terrain. IPP makes the mapping of the unknown terrain efficient, which enables the possibility to use the resulting map to plan safe paths for the UGV. Traversing unknown terrain is hard for a single UGV but with the help from one or more UAVs the process is much more efficient. The use of multiple cooperating autonomous vehicles makes it possible to solve tasks complicated for the individual vehicle in an efficient manner.

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