Indiana University-Purdue University Indianapolis (IUPUI) / This thesis addresses a critical issue of automotive safety. As traffic is increasing on the roads day by day, road safety is also a very important concern. Driving simulators can play an extensive role in the development and testing of advanced safety systems in peculiar traffic environments, respectively. Advanced Driver Assist Systems (ADAS) are getting enormous reputation but there is still need for more improvements. This thesis presents a design of an Automatic Emergency Pullover (AEP) strategy using active safety systems for a semi-autonomous vehicle. The idea for this system is that a moving vehicle equipped with an AEP system can automatically pull over on the roadside safely when the driver is considered incapable of driving. Furthermore, AEP supporting features such as; Lane Keeping Assist, Blind Spot Monitoring, Vehicle and Pedestrian Automatic Emergency Braking, Adaptive Cruise Control are also included in this work. The designs for application of each system have been explained along with its algorithms, model development, component architecture, simulation results, vehicular/pedestrian behavior and trajectory precision on software tools provided by Realtime Technologies, Inc. All major variables which influence the performance of vehicle after AEP activation, have been observed and remodeled according to control algorithms. The implementation of AEP system which can control vehicle dynamics has been verified with the help of simulation results.

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 新制・課程博士 / 博士(工学) / 甲第24607号 / 工博第5113号 / 新制||工||1978(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 椹木 哲夫, 教授 小森 雅晴, 教授 泉井 一浩 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Unmanned vehicles (UVs) are seeing more widespread use in military, scientific, and civil sectors in recent years. These UVs range from unmanned air and ground vehicles to surface and underwater vehicles. Each of these different UVs has its own inherent strengths and weaknesses, from payload to freedom of movement. Research in this field is growing primarily because of the National Defense Act of 2001 mandating that one-third of all military vehicles be unmanned by 2015. Research using small UVs, in particular, is a growing because small UVs can go places that may be too dangerous for humans. Because of the limitations inherent in small UVs, including power consumption and payload, the selection of light weight and low power sensors and processors becomes critical. Low power CMOS cameras and real-time vision processing algorithms can provide fast and reliable information to the UVs. These vision algorithms often require computational power that limits their use in traditional general purpose processors using conventional software. The latest developments in field programmable gate arrays (FPGAs) provide an alternative for hardware and software co-design of complicated real-time vision algorithms. By tracking features from one frame to another, it becomes possible to perform many different high-level vision tasks, including object tracking and following. This thesis describes a vision guidance system for unmanned vehicles in general and the FPGA hardware implementation that operates vision tasks in real-time. This guidance system uses an object following algorithm to provide information that allows the UV to follow a target. The heart of the object following algorithm is real-time rank transform, which transforms the image into a more robust image that maintains the edges found in the original image. A minimum sum of absolute differences algorithm is used to determine the best correlation between frames, and the output of this correlation is used to update the tracking of the moving target. Control code can use this information to move the UV in pursuit of a moving target such as another vehicle.

This thesis focuses on making improvements to time-delayed teleoperation systems, with both direct and semi-autonomous haptic control, by addressing the challenges associated with force-position (F-P) predictive architectures. As the time delay from the communication channel increases, system stability and performance degrade. Previously, solutions focused on communication channel stability and environment force estimation methods that primarily rely on linearization of the Hunt-Crossley (HC) contact model. These result in a loss of transparency in the system and limiting use cases from linearization assumptions. Moreover, semi-autonomous solutions aimed at decreasing user effort and automating subtasks, such as obstacle avoidance and user guidance, require training or singularly focus on joint space tasks. This work addresses the shortcomings of the aforementioned methods by refocusing on system components to achieve more favorable dynamics during environment contact with the use of a series elastic actuator (SEA), investigating alternative HC parameter estimation techniques, and synthesizing an assistive semi-autonomous control framework that predicts user intention recognition and automates gross motion tasks. Experimental results with a remote SEA demonstrate improved performance with stiff environments in delays of up to two seconds round trip time. The coupling of the force and position through the actuator along with simultaneous sensing capabilities also show robustness for contact with soft environments. Further improvements with soft environment contact are achieved through HC parameter estimation, with smooth parameter update switching using a Sigmoid function. A novel application of Chebyshev polynomial approximation for adaptive parameter estimation of the HC model was also proposed. This approach provides control via backstepping with adaptive parameter estimation using Lyapunov methods. Additionally, this method reduces excitation requirements by using nonlinear swapping and the data accumulation concept to guarantee parameter convergence. A simulated teleoperation system demonstrates the effectiveness of this approach and initial results from experiment show promise for this approach in practice. Finally, a user study involving a pick and place task produced favorable results for the proposed semi-autonomous framework which significantly reduced task completion times. / Master of Science / Teleoperated systems are powerful solutions for remotely executing tasks in situations where autonomous solutions are not robust enough and/or user knowledge is desired for a task. However, teleoperation performance and stability is degraded by delays in the communication channel. A common way to deal with time delay is to use a predictive controller on the local side to cancel out the delay by knowing the remote side dynamics. Previous approaches have focused on stabilizing the communication channel or the use of estimators and observers to better capture the remote side dynamics. The drawback of these approaches is that they achieve stability at the expense of system transparency, leading to divergence in the force and position matching between the master and remote side. Many of the methods for environment force estimation involves linearizing contact models, creating limitations in their application. Moreover, semi-autonomous solutions aimed at decreasing user effort and automating subtasks such as obstacle avoidance and user guidance require training data sets for the algorithm or only focus individually on joint space tasks. This thesis addresses the shortcomings of the aforementioned methods by refocusing on system components to achieve more favorable dynamics using a series elastic actuator (SEA) while interacting with the environment, investigating nonlinear and linear contact model estimation methods for identifying parameters of the Hunt-Crossley (HC) model, and synthesising an assistive semi-autonomous control framework that predicts user intention for task execution. Experimental results for the use of an SEA demonstrate improved performance with stiff environments in delays of up to two seconds round trip time (RTT). The coupling of the force and position through the actuator along with simultaneous sensing capabilities also showed robustness for contact with soft environments. Various estimation methods for HC parameter identification was investigated to improve the local side model. A novel application of Chebyshev polynomial approximation of the HC model with adaptive parameter estimation was also proposed to provide control along with decreasing the excitation requirements by using backsteping control with nonlinear swapping and the data accumulation concept. A simulated teleoperation system demonstrated the effectiveness of this approach with a smooth paramater update transition. Initial results from experiment also show promise for this approach in practice. Finally, a user study involving a pick and place task produced favorable results for the proposed semi-autonomous framework which significantly reduced task completion times.

Made available in DSpace on 2016-04-28T20:19:28Z (GMT). No. of bitstreams: 1 2009 - Rodrigo Linhares Lauria.pdf: 1096290 bytes, checksum: cd2e531f6c6a91f9e7e441cc348ef596 (MD5) Previous issue date: 2009-07-06 / It is a descriptive qualitative research, accomplished in a Brazilian branch of a French multinational company, that it aims at to analyze how the semi-autonomous groups methodology works in a management point of view. As the increasing empowerment by the use of these semi-autonomous groups is a recognized practice of improving competitiveness in the companies, it was necessary to identify which aspects related to the implantation methodology, chosen by the multinational, presented positive aspects and deficiencies in the employees optics. The used methodology is a case study, where in the data collection was used a questionnaire and daily observations of the company. The chosen universe is the operational department of the multinational, due to its largest implication in the implementation of the semi-autonomous groups. The chosen sample refers to a specific section of the assembly department, because it was considered representative in the analysis universe. The associate theory, from which the questionnaire was elaborated, found the necessary abilities/knowledge to the implementation of semi-autonomous groups, as well as the three competence dimensions idealized for Le Boterf, generating thirty six questions, being two for each relationship abilities/knowledge - competence dimensions between abilities/knowledge and competence dimensions. The conclusion of the questionnaires results analyses basing on the lifted up theories, the implantation manual of the multinational and the section observations, identified five roads of progress so that it is possible to develop the necessary competences to the semi-autonomous groups of this multinational. Which are: leadership formation and sensitization, development of the evaluation system, complete implantation of the spokes, formation / training in the other necessary competences to GSA and implantation of monthly team meetings. / Trata-se de uma pesquisa qualitativa descritiva, realizada numa filial brasileira de uma empresa multinacional francesa, que objetiva analisar como a metodologia grupos semiaut?nomos est? em termos de gest?o. Como o aumento da responsabiliza??o pela utiliza??o destes grupos semi-aut?nomos ? uma pr?tica reconhecida de melhoria da competitividade das empresas, visualizou-se a necessidade de identificar quais aspectos ligados ? metodologia de implanta??o, escolhida pela multinacional, apresentavam aspectos positivos e defici?ncias na ?tica dos funcion?rios objeto de sua a??o. A metodologia utilizada ? um estudo de caso, onde na coleta de dados utilizou-se um question?rio e observa??es do cotidiano da empresa. O universo escolhido ? a parte operacional da multinacional, pois s?o os maiores implicados na implementa??o dos grupos semi-aut?nomos. A amostra escolhida ? referente a um setor espec?fico de produ??o do departamento de montagem, por este ser considerado representativo do universo em an?lise. O referencial te?rico, a partir do qual foi elaborado o question?rio, levantou as habilidades/conhecimentos necess?rias ? implementa??o de grupos semi-aut?nomos, assim como, as tr?s dimens?es da compet?ncia idealizadas por Le Boterf, gerando trinta e seis perguntas, sendo duas para cada rela??o entre habilidades/conhecimentos e dimens?es da compet?ncia. A conclus?o das an?lises dos resultados dos question?rios a luz das teorias levantadas, do manual de implanta??o da multinacional e das observa??es realizadas neste setor em quest?o, identificaram cinco vias de progresso para que seja poss?vel desenvolver as compet?ncias necess?rias aos grupos semi-aut?nomos desta multinacional. As quais s?o: forma??o e sensibiliza??o da chefia, evolu??o do sistema de avalia??o, implanta??o completa dos representantes, forma??o/ treinamento nas outras compet?ncias necess?rias ao GSA e formaliza??o de reuni?es mensais das equipes.

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and task sharing between a human operator and a robot with respect to tasks such as navigation, exploration and victim identification. Herein, a unique hierarchical reinforcement learning (HRL) -based semi-autonomous control architecture is presented for rescue robots operating in unknown and cluttered urban search and rescue (USAR) environments. The aim of the controller is to allow a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A new direction-based exploration technique and a rubble pile categorization technique are integrated into the control architecture for exploration of unknown rubble filled environments. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed control architecture.

Semi-autonomous revenue authorities (ARAs) have been established all over the world as a distinctive institutional model outside the traditional public service aimed at enhancing tax administration, and thereby raising tax revenues. In order to boost the robustness of their operations, substantial expenditures have gone towards modernising ARAs. Expenditures have been guided by medium-term corporate-wide plans, and the results monitored, assessed and reported using performance measures. Performance measurement has proved challenging for ARAs to implement and sustain in practice. Some of the challenges evolve around weak capacity, implementation costs, issues to do with quantification, competing demands from a wide range of constituents, the inappropriate selection of measures and a bias towards performance measures that focus on finances. As a means for enhancing performance measurement, there are practices, lessons and theoretical perspectives that can be discerned from the broadspectrum of literature on performance measurement in the public sector and ARAs from around the world. This thesis explores how performance measurement in African ARAs can be more strategic, efficient and effective by ascertaining which key factors shape its adoption. The research focuses on the in depth study of three ARAs in Sub Saharan Africa, involving a combination of structured interviews with internal and external stakeholders, the administration of a survey instrument and review of ARA documents. The final chapters of the thesis deploy fuzzy set logic techniques to identify and test the significance of various causal conditions in the adoption of performance measurement in ARAs, as a plausible answer to the research question.

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and task sharing between a human operator and a robot with respect to tasks such as navigation, exploration and victim identification. Herein, a unique hierarchical reinforcement learning (HRL) -based semi-autonomous control architecture is presented for rescue robots operating in unknown and cluttered urban search and rescue (USAR) environments. The aim of the controller is to allow a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A new direction-based exploration technique and a rubble pile categorization technique are integrated into the control architecture for exploration of unknown rubble filled environments. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed control architecture.

In this thesis, the dynamic modeling of a wheelchair-human system is performed, and the effects of steering action and sudden slope changes along the path on the system stability are analyzed for different road and driving conditions. For the cases where the wheelchair system is unstable three methods are proposed for stability augmentation. This study is performed to improve the stability of the wheelchair system under varying road conditions so as to increase the limit of independency for wheelchair users and enhance their life quality. Two separate mathematical models are obtained for the wheelchair driven on constant sloped and changing sloped roads. Matlab Simulink models are constructed with the obtained mathematical models and control structure. The stability of the system is analyzed by case studies and it is seen that the system is unstable in some of these cases. Three methods are used for enhancement of the stability. One is the speed reduction via joystick module during steep turns, by which the speed of the wheelchair is reduced automatically for a safe steering, but the wheelchair follows the desired course. The second method is the use of a shape filter in order to obtain a less jerky response for the speed. As a final method, the center of mass of the wheelchair-human system is shifted gently in a controlled manner to the side where the reaction force on the wheels decreases.

Les robots vont être de plus en plus utilisés dans les domaines civils, comme dans le domaine militaire. Ces robots, opérant en flottes, peuvent accompagner des soldats au combat, ou accomplir une mission en étant supervisés par un poste de contrôle. Du fait des exigences d'une opération militaire, il est difficile de laisser les robots décider de leurs actions sans accord d'un opérateur ou surveillance, en fonction de la situation. Dans cette thèse, nous nous attardons sur deux problématiques:D'une part, nous cherchons à exploiter l'autonomie ajustable de sorte à ce qu'un robot puisse accomplir sa mission de la manière la plus efficace possible, tout en respectant des restrictions assignées par un opérateur sur son niveau d'autonomie. Pour cela, celui-ci est en mesure de définir pour un ensemble d'états et d'actions donné un niveau de restriction. Ce niveau peut par exemple imposer au robot la télé-opération pour accéder à une zone à risque.D'autre part, comme nous envisageons la possibilité que plusieurs robots soient déployés en même temps, ces robots doivent se coordonner pour accomplir leurs objectifs. Seulement, comme les opérateurs peuvent prendre le contrôle de certains d'entre eux, la question de la coordination se pose. En effet, l'opérateur ayant ses propres préférences, perception de l'environnement, connaissances et étant sujet aux stress, hésitations, il est difficile de prévoir les actions que celui-ci va effectuer, et donc de s'y coordonner. Nous proposerons dans cette thèse une approche visant à estimer la politique exécutée par un robot télé-opéré à partir d'apprentissage basé sur les actions observés de ce robot.La notion de planification est très présente dans ces travaux. Ceux-ci se baseront sur des modèles de planifications comme les Processus Décisionnels de Markov. / Robots will be more and more used in both civil and military fields. These robots, operating in fleet, can accompany soldiers in fight, or accomplish a mission while being supervised by a control center. Considering the requirement of a military operation, it is complicated to let robots decide their action without an operator agreement or watch, in function of the situation.In this thesis, we focus on two problematics:First, we try to exploit adjustable autonomy to make a robot accomplishes is mission as efficiency as possible, while he respects restrictions, assigned by an operator, on his autonomy level. For this, it is able to define for given sets of states and actions a restriction level. This restriction can force, for example, the need of being tele-operated to access a dangerous zone.Secondly, we consider that several robots can be deployed at the same time. These robots have to coordinate to accomplish their objectives. However, since operators can take the control of some robots, the coordination is harder. In fact, the operator has preferences, perception, hesitation, stress that are not modeled by the agent. It is then hard to estimate his next actions, so to coordinate with him. We propose in this thesis an approach to estimate the policy executed by a tele-operated robot from learning methods, based on observed actions from this robot.The notion of planning his important in these works. These are based on planning models, such as Markov Decision Processes.