An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.<p>This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.

Automated spacecraft docking is a concept of operations with several important potential applications. One application that has received a great deal of attention recently is that of an automated docking capable unmanned re-supply spacecraft. In addition to being useful for re-supplying orbiting space stations, automated shuttles would also greatly facilitate the manned exploration of nearby space objects, including the Moon, near-Earth asteroids, or Mars. These vehicles would allow for longer duration human missions than otherwise possible and could even accelerate human colonization of other worlds. This thesis develops an optimal docking controller for an automated docking capable spacecraft. An innovative vision-based relative navigation system called VisNav is used to provide real-time relative position and orientation estimates, while a Kalman post-filter generates relative velocity and angular rate estimates from the VisNav output. The controller's performance robustness is evaluated in a closed-loop automated spacecraft docking simulation of a scenario in circular lunar orbit. The simulation uses realistic dynamical models of the two vehicles, both based on the European Automated Transfer Vehicle. A high-fidelity model of the VisNav sensor adds realism to the simulated relative navigation measurements. The docking controller's performance is evaluated in the presence of measurement noise, with the cases of sensor noise only, vehicle mass errors plus sensor noise, errors in vehicle moments of inertia plus sensor noise, initial starting position errors plus sensor noise, and initial relative attitude errors plus sensor noise each being considered. It was found that for the chosen cases and docking scenario, the final controller was robust to both types of mass property modeling errors, as well as both types of initial condition modeling errors, even in the presence of sensor noise. The VisNav system was found to perform satisfactorily in all test cases, with excellent estimate error convergence characteristics for the scenario considered. These results demonstrate preliminary feasibility of the presented docking system, including VisNav, for space-based automated docking applications.

In principle, general approaches to optimal nonlinear filtering can be described in a unified way from the recursive Bayesian approach. The central idea to this recur- sive Bayesian estimation is to determine the probability density function of the state vector of the nonlinear systems conditioned on the available measurements. However, the optimal exact solution to this Bayesian filtering problem is intractable since it requires an infinite dimensional process. For practical nonlinear filtering applications approximate solutions are required. Recently efficient and accurate approximate non- linear filters as alternatives to the extended Kalman filter are proposed for recursive nonlinear estimation of the states and parameters of dynamical systems. First, as sampling-based nonlinear filters, the sigma point filters, the unscented Kalman fil- ter and the divided difference filter are investigated. Secondly, a direct numerical nonlinear filter is introduced where the state conditional probability density is calcu- lated by applying fast numerical solvers to the Fokker-Planck equation in continuous- discrete system models. As simulation-based nonlinear filters, a universally effective algorithm, called the sequential Monte Carlo filter, that recursively utilizes a set of weighted samples to approximate the distributions of the state variables or param- eters, is investigated for dealing with nonlinear and non-Gaussian systems. Recentparticle filtering algorithms, which are developed independently in various engineer- ing fields, are investigated in a unified way. Furthermore, a new type of particle filter is proposed by integrating the divided difference filter with a particle filtering framework, leading to the divided difference particle filter. Sub-optimality of the ap- proximate nonlinear filters due to unknown system uncertainties can be compensated by using an adaptive filtering method that estimates both the state and system error statistics. For accurate identification of the time-varying parameters of dynamic sys- tems, new adaptive nonlinear filters that integrate the presented nonlinear filtering algorithms with noise estimation algorithms are derived. For qualitative and quantitative performance analysis among the proposed non- linear filters, systematic methods for measuring the nonlinearities, biasness, and op- timality of the proposed nonlinear filters are introduced. The proposed nonlinear optimal and sub-optimal filtering algorithms with applications to spacecraft orbit es- timation and autonomous navigation are investigated. Simulation results indicate that the advantages of the proposed nonlinear filters make these attractive alterna- tives to the extended Kalman filter.

In recent years, road navigational devices equipped with GPS technologies are becoming more and more popular. These devices can facilitate drivers to guide the way to reach their destinations. In this Thesis, we propose a dynamic route navigational algorithm (DNA) that can be combined with GPS, electronic map system, and wireless technologies (such as DSRC, VANET, 802.11p, WLAN, WiMAX ..., etc.). By calculating metric from road type, road length, average distance, and average speed of each available candidate road, DNA generates road score table and choose the route with the highest score among candidate routes to reach destination. Additionally, in DNA, a Dynamic Block re-Broadcasting (DBB) scheme is designed to carry out Road-Request message (R-REQ) rebroadcast function. The R-REQ rebroadcast function provides two advantages: (i) It can broadcast R-REQ to a specific coverage area with least hop counts; (ii) It can reduce the number of R-REQ broadcast packets significantly. Although DNA takes more processing time than other navigational algorithms, it can greatly reduce the traveling time by determining the best route to destination. For the purpose of performance evaluation, we develop a simulator by using C++ programming language to compare the performance of DNA with other navigational algorithms. Simulation results have shown that DNA performs much better than other navigational algorithms in reaching destination with shorter travel distance and shorter traveling time.

Texte remanié de: Th. doct.--Droit privé--Nantes, 2004. / Bibliogr. p. 668-723. Index.

Texte remanié de: Thèse de doctorat--Histoire--Angers, 2001. / Bibliogr. p. 429-450. Notes bibliogr. Glossaire.

Reproduction de : Thèse de doctorat : Signaux, image et acoustique : Toulouse, INPT : 2002. / Texte en anglais. Indroduction, conclusion, table des matières et résumé en français. Titre provenant de l'écran-titre. Bibliogr. 19 réf.

Reproduction de : Thèse de doctorat : Sciences économiques. Economie des transports : Lyon 2 : 2008. / Titre provenant de l'écran-titre. Bibliogr.

Reproduction de : Thèse de doctorat : Histoire : Paris 8 : 2007. Dottorato di ricerca : Forme della conoscenza storica dal Medioevo alla contemporaneità : Università degli studi di Trieste : 2007. / Thèse soutenue en co-tutelle. Texte en italien. Sommaire et résumé substantiel en français. Titre provenant de l'écran-titre. Bibliogr. p. 340-359.

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