Starting a few decades ago, the unmanned wheeled vehicle research has drawn lately more attention, especially for off-road environment. As the demand to use electric vehicles increased, the need to conceptualize the use of electrically driven vehicles in autonomous operations became a target. That is because in addition to the fact that they are more environmentally friendly, they are also easier to control. This also gives another reason to enhance further the energy economy of those unmanned electric vehicles. Off-road vehicles research was always challenging, but in the present work the nature of the off-road land is utilized to benefit from in order to enhance the energy consumption of those vehicles. An algorithm for energy consumption optimization for electrically driven unmanned wheeled vehicles is presented. The algorithm idea is based on the fact that in off-road conditions, when the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill could be utilized to reduce the energy consumption for moving uphill if the dimensions of the ditch/hole were known a distance ahead. Two manipulated variables are evaluated: the wheels DC motors supply voltage and the DC armature current. The developed algorithm is analysed and compared to the PID speed iii controller and to the open-loop control of DC motors. The developed predictive controller achieved encouraging results compared to the PID speed control and also compared to the open-loop control. Also, the use of the DC armature current as a manipulated variable showed more noticeable improvement over using the DC input voltage. Experimental work was carried out to validate the predictive control algorithm. A mobile robot with two DC motor driven wheels was deployed to overcome a ditch-like hindrance. The experimental results verified the simulation results. A parametric study for the predictive control is conducted. The effect of changing the downhill angle and the uphill angle as well as the size of the prediction horizon on the consumed electric energy by the DC motors is addressed. The simulation results showed that, when using the proposed approach, the larger the prediction horizon, the lower the energy consumption is.

Os veículos submarinos não tripulados tem uma importância crescente devido à sua flexibilidade e baixo custo. Devido à sua complexidade intrínseca, eles requerem diversas competências diferentes para serem desenvolvidos e permitem realizar pesquisas em vários campos do conhecimento. No contexto de uma universidade, que possui pessoal heterogêneo e de alta rotatividade, faz-se mister adotar uma organização que permita que os esforços de cada aluno possam ser reusados pelos outros, de modo as atividades de pesquisa possam avançar com pouca perda de tempo e retrabalho. Tal necessidade pode ser respondida pela aplicação de conceitos da engenharia de sistemas, tais como modularidade, separação formal entre soluções tecnológicas e necessidades, classificação funcional, critérios para escolha do método de desenvolvimento, uso de referencial normativo técnico, plano tecnológico, integração, verificação e validação e gerenciamento de configurações. Este trabalho se limita a desenvolver uma arquitetura de controle, observando os conceitos de engenharia de sistemas, aplicada a um AUV. É feita uma comparação com outras arquiteturas similares do estado da arte e mostram-se resultados de testes em piscina para esta arquitetura. É mostrado também que foi possível estabelecer a continuidade do desenvolvimento por outros alunos, validando a utilidade da metodologia. Conclui-se que, para aumentar a eficiência da pesquisa universitária, é necessário observar aspectos gerenciais e institucionais além dos aspectos técnicos ao conceber soluções técnicas. / The unmanned underwater vehicles have a growing position due to their flexibility and low cost. Due to their inherent complexity, they require many different skills to be developed and they allow conducting research in various fields of knowledge. In the context of a university, which has heterogeneous staff and high turnover, there is the need of adopting an organization that allows the efforts of each student be reused by others, so research activities can proceed with little loss of time and rework. This need can be answered by the application of system engineering concepts such as modularity, formal separation between technology solutions and needs, functional classification, criteria for the choice of development method, use of technical reference standard, technological plan, integration, verification and validation and configuration management. This work is limited to development of a control architecture, observing the concepts of systems engineering, applied to an AUV. A comparison is made with other similar architectures in the state of the art and shows up test results in the pool for this architecture. It is also shown that it was possible to keep the development by other students, validating the utility of the methodology. It is concluded that in order to increase the efficiency of university research, it must be observed managerial and institutional aspects beyond the technical aspects when designing technical solutions.

Starting a few decades ago, the unmanned wheeled vehicle research has drawn lately more attention, especially for off-road environment. As the demand to use electric vehicles increased, the need to conceptualize the use of electrically driven vehicles in autonomous operations became a target. That is because in addition to the fact that they are more environmentally friendly, they are also easier to control. This also gives another reason to enhance further the energy economy of those unmanned electric vehicles. Off-road vehicles research was always challenging, but in the present work the nature of the off-road land is utilized to benefit from in order to enhance the energy consumption of those vehicles. An algorithm for energy consumption optimization for electrically driven unmanned wheeled vehicles is presented. The algorithm idea is based on the fact that in off-road conditions, when the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill could be utilized to reduce the energy consumption for moving uphill if the dimensions of the ditch/hole were known a distance ahead. Two manipulated variables are evaluated: the wheels DC motors supply voltage and the DC armature current. The developed algorithm is analysed and compared to the PID speed iii controller and to the open-loop control of DC motors. The developed predictive controller achieved encouraging results compared to the PID speed control and also compared to the open-loop control. Also, the use of the DC armature current as a manipulated variable showed more noticeable improvement over using the DC input voltage. Experimental work was carried out to validate the predictive control algorithm. A mobile robot with two DC motor driven wheels was deployed to overcome a ditch-like hindrance. The experimental results verified the simulation results. A parametric study for the predictive control is conducted. The effect of changing the downhill angle and the uphill angle as well as the size of the prediction horizon on the consumed electric energy by the DC motors is addressed. The simulation results showed that, when using the proposed approach, the larger the prediction horizon, the lower the energy consumption is.

The purpose of this study was to empirically test the oft-noted hypothesis that shared mental models lead to implicit coordination. Specifically, this dissertation investigated the underlying mechanisms of implicit coordination and how different aspects of shared mental models affect the process. The research questions tested in this study were (a)how perceptions of sharedness affect the initiation of implicit coordination, (b) how actual levels of sharedness affect the process of implicit coordination, and (c) how quality of task mental models affects successful implicit coordination. Sixty same-gender, two-person teams engaged in a complex military reconnaissance planning task in which the team members were required to work together by exchanging information to plan routes for one unmanned aerial vehicle (UAV) and one unmanned ground vehicle (UGV). The results provided partial support for the influence of different facets of shared mental models on the process of implicit coordination. Specifically, individual mental model quality, not perceptions of sharedness or actual mental model sharedness, was the biggest predictor of the initiation of implicit coordination. Additionally, perceptions of sharedness and actual mental model sharedness interacted with one another, such that teams in mismatched conditions (high perceptions of sharedness but low actual sharedness [false consensus], or low perceptions of sharedness and high actual sharedness, [pluralistic ignorance]) tended to increase their communications. The implications and recommendations for future research on implicit coordination and shared mental models are discussed. Additionally, the implications for operators of unmanned vehicles are also discussed.

Scientists can better understand wetlands environments by collecting data they are interested in via sensor networks. However the deployment of these sensor nodes manually can be disruptive to these sensitive environments. We develop a set of algorithms for autonomously differentiating land from water via aerial imagery using an unmanned aerial vehicle (UAV). The UAV takes a picture of the area, clusters, classifies, defines regions, and then communicates the regions to other UAVs responsible for deploying the sensor nodes. These UAVs run an algorithm to determine the optimal locations for sensor nodes such that they completely cover the regions and allow for communication between the nodes in the sensor network. Our classifier training algorithm identifies the best classifier using clusters and we compare its successful classification rate to a pixel-based approach and we see classification rates of 89.6%. This classifier feeds into our online algoorithm that the UAV successfully uses to classify the Calaveras River in California. In our simulations to determine the most effective algorithm for determining where the place the sensor nodes in a sensor network, we found Triangular Geometric Tessellation was the optimal algorithm, able to achieve 91.5% coverage in concave areas and 88.2% coverage in convex areas with relatively low computational complexity.

No description available.

Unmanned vehicle systems are becoming more important in the future of the military and in commercial applications. These systems are used to prevent humans from entering dangerous situations or to automate dull tasks. In order to facilitate rapid development of these systems, testing procedures and infrastructure need to be created. Once developed, the performance characteristics of unmanned vehicle systems can be determined and compared to similar systems. This information will be beneficial to system developers and potential customers. In order to provide the infrastructure and test procedures to the unmanned systems community, the Joint Robotics Program created the National Unmanned Systems Experimentation Environment (NUSE2). NUSE2 consists of a variety of military organizations and academic resources, including the Joint Unmanned Systems Test Experimentation and Research (JOUSTER) site at Virginia Tech. JOUSTER was tasked specifically with creating a mobile instrumentation platform capable of providing wireless communications, data collection, and video coverage of a testing site. This thesis presents the system designed and created to meet this need. For the first time, a mobile instrumentation platform has been created to specifically support unmanned systems research. Additionally, the performance characteristics of this system have been fully evaluated and will serve as a benchmark for future improvements to the system. / Master of Science

Thesis (PhD)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: This research focuses on the development of new efficient algorithms for multi-path planning and multi-rigid body constrained attitude control. The work is motivated by current and future applications of these algorithms in: intelligent control of multiple autonomous aircraft and spacecraft systems; control of multiple mobile and industrial robot systems; control of intelligent highway vehicles and traffic; and air and sea traffic control. We shall collectively refer to the class of mobile autonomous systems as “agents”. One of the challenges in developing and applying such algorithms is that of complexity resulting from the nontrivial agent dynamics as agents interact with other agents, and their environment. In this work, some of the current approaches are studied with the intent of exposing the complexity issues associated them, and new algorithms with reduced computational complexity are developed, which can cope with interaction constraints and yet maintain stability and efficiency. To this end, this thesis contributes the following new developments to the field of multipath planning and multi-body constrained attitude control: • The introduction of a new LMI-based approach to collision avoidance in 2D and 3D spaces. • The introduction of a consensus theory of quaternions by applying quaternions directly with the consensus protocol for the first time. • A consensus and optimization based path planning algorithm for multiple autonomous vehicle systems navigating in 2D and 3D spaces. • A proof of the consensus protocol as a dynamic system with a stochastic plant matrix. • A consensus and optimization based algorithm for constrained attitude synchronization of multiple rigid bodies. • A consensus and optimization based algorithm for collective motion on a sphere. / AFRIKAANSE OPSOMMING: Hierdie navorsing fokus op die ontwikkeling van nuwe koste-effektiewe algoritmes, vir multipad-beplanning en veelvuldige starre-liggaam beperkte standbeheer. Die werk is gemotiveer deur huidige en toekomstige toepassing van hierdie algoritmes in: intelligente beheer van veelvuldige outonome vliegtuig- en ruimtevaartuigstelsels; beheer van veelvuldige mobiele en industrile robotstelsels; beheer van intelligente hoofwegvoertuie en verkeer; en in lug- en see-verkeersbeheer. Ons sal hier “agente” gebruik om gesamentlik te verwys na die klas van mobiele outonome stelsels. Een van die uitdagings in die ontwikkeling en toepassing van sulke algoritmes is die kompleksiteit wat spruit uit die nie-triviale agentdinamika as gevolg van die interaksie tussen agente onderling, en tussen agente en hul omgewing. In hierdie werk word sommige huidige benaderings bestudeer met die doel om die kompleksiteitskwessies wat met hulle geassosieer word, bloot te l^e. Verder word nuwe algoritmes met verminderde berekeningskompleksiteit ontwikkel. Hierdie algoritmes kan interaksie-beperkings hanteer, en tog stabiliteit en doeltreffendheid behou. Vir hierdie doel dra die proefskrif die volgende nuwe ontwikkelings by tot die gebied van multipad-beplanning van multi-liggaam beperkte standbeheer: • Die voorstel van ’n nuwe LMI-gebasseerde benadering tot botsingsvermyding in 2D en 3D ruimtes. • Die voorstel van ’n konsensus-teorie van “quaternions” deur “quaternions” vir die eerste keer met die konsensusprotokol toe te pas. • ’n Konsensus- en optimeringsgebaseerde padbeplanningsalgoritme vir veelvoudige outonome voertuigstelsels wat in 2D en 3D ruimtes navigeer. • Die bewys van ’n konsensusprotokol as ’n dinamiese stelsel met ’n stochastiese aanlegmatriks. • ’n Konsensus- en optimeringsgebaseerde algoritme vir beperkte stand sinchronisasie van veelvoudige starre liggame. • ’n Konsensus- en optimeringsgebaseerde algoritme vir kollektiewe beweging op ’n sfeer.

Though multirotor unmanned aerial vehicles (UAVs) have become widely used during the past decade, challenges in autonomy have prevented their widespread use when moving vehicles act as their base stations. Emerging use cases, including maritime surveillance, package delivery and convoy support, require UAVs to autonomously operate in this scenario. This thesis presents improved solutions to both the state estimation and control problems that must be solved to enable robust, autonomous landing of multirotor UAVs onto moving vehicles.Current state-of-the-art UAV landing systems depend on the detection of visual fiducial markers placed on the landing target vehicle. However, in challenging conditions, such as poor lighting, occlusion, or extreme motion, these fiducial markers may be undected for significant periods of time. This thesis demonstrates a state estimation algorithm that tracks and estimates the locations of unknown visual features on the target vehicle. Experimental results show that this method significantly improves the estimation of the state of the target vehicle while the fiducial marker is not detected.This thesis also describes an improved control scheme that enables a multirotor UAV to accurately track a time-dependent trajectory. Rooted in Lie theory, this controller computes the optimal control signal based on an error-state formulation of the UAV dynamics. Simulation and hardware experiments of this control scheme show its accuracy and computational efficiency, making it a viable solution for use in a robust landing system.

Approved for public release; distribution is unlimited / Reissued 30 May 2017 with correction to student's affiliation on title page. / Autonomous vehicle teams have great potential in a wide range of maritime sensing applications, including mine countermeasures (MCM). A key enabler for successfully employing autonomous vehicles in MCM missions is motion planning, a collection of algo-rithms for designing trajectories that vehicles must follow. For maximum utility, these algorithms must consider the capabilities and limitations of each team member. At a minimum, they should incorporate dynamic and operational constraints to ensure trajectories are feasible. Another goal is maximizing sensor performance in the presence of uncertainty. Optimal control provides a useful frame-work for solving these types of motion planning problems with dynamic constraints and di_x000B_erent performance objectives, but they usually require numerical solutions. Recent advances in numerical methods have produced a general mathematical and computational framework for numerically solving optimal control problems with parameter uncertainty—generalized optimal control (GenOC)— thus making it possible to numerically solve optimal search problems with multiple searcher, sensor, and target models. In this dissertation, we use the GenOC framework to solve motion planning problems for di_x000B_erentMCMsearch missions conducted by autonomous surface and underwater vehicles. Physics-based sonar detection models are developed for operationally relevant MCM sensors, and the resulting optimal search trajectories improve mine detection performance over conventional lawnmower survey patterns—especially under time or resource constraints. Simulation results highlight the flexibility of this approach for optimal mo-tion planning and pre-mission analysis. Finally, a novel application of this framework is presented to address inverse problems relating search performance to sensor design, team composition, and mission planning for MCM CONOPS development.