The number of resident space objects (RSOs) in orbit has increased dramatically within the last 10 years. While RSOs pose a serious challenge to our continued use of space, these objects also provide an opportunity to improve on-orbit state estimation by detecting and identifying these objects using star trackers. While star trackers are currently used to determine the orientation of their host spacecraft, their utility could be expanded to both position and orientation estimation by harnessing RSO detections. In order for RSO-based optical navigation to be commercially viable, a reliable filter covariance estimate is required. This paper introduces an Unscented Kalman Filter (UKF) for estimating an observing spacecraft's position and attitude based on RSO observations. To ensure that this filter is reliable, a new assessment technique is introduced where we compare the moving standard deviation and mean of the filter's estimate error with the predicted error based on the filter's covariance estimate. We demonstrate the utility of this assessment method by comparing the covariance trusts for our UKF and a previously developed Extended Kalman Filter.

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This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

Presented in this thesis is a method for tool-path planning for automated polishing. This work is an intergral part of the research program on automated polishing/deburring being carried out at Ryerson University. Whereas tool-path planning for machining is treated as a geometry problem, it ks shown here that tool-path planning for polishing should be treated as a contact mechanics problem because of the contact action between the polishing tool and the part surface. To develop this algorithm, contact mechanics is applied for contact area modeling and analysis, Once the contact area is determined, for multiple contact points along the given polishing path, a map of the contact area is generated and utilized to show the coverage area during polishing. This map is then used to plan a polishing path that ensures complete coverage for polishing, Simulation has been carried out to show the effetiveness of this new polishing path algorithm.

This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm