Open Access. Powered by Scholars. Published by Universities.®
Navigation, Guidance, Control and Dynamics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- #antcenter (1)
- Artificial intelligence (1)
- Autonomous pollination (1)
- Bayesian estimation (1)
- Control (1)
-
- Convex optimization (1)
- Dual-Quaternions (1)
- Factor graphs (1)
- Flight control (1)
- High performance computing (1)
- Monte-Carlo simulation (1)
- Multipath (1)
- On-orbit servicing (1)
- Optimal control (1)
- Particle accelerators (1)
- Path planning (1)
- Perception (1)
- Quaternions (1)
- Robust estimation (1)
- Rocket (1)
- Rotation Matrices (1)
- Scheduling (1)
- Sliding Mode Control (1)
- Space mission design (1)
- Space robots (1)
- Trajectory optimization (1)
- Unmanned aerial vehicles (1)
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Navigation, Guidance, Control and Dynamics
Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer
Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer
Symposium of Student Scholars
When navigating in unknown and unstructured environments, Unmanned Arial Vehicles (UAVs) can struggle when attempting to preform Simultaneous Localization and Mapping (SLAM) operations. Particularly challenging circumstance arise when an UAV may need to land or otherwise navigate through treacherous environments. As the primary UAV may be too large and unwieldly to safely investigate in these types of situations, this research effort proposes the use of actively guided CanSats for assisting in localization and mapping of unstructured environments. A complex UAV could carry multiple of these SLAM capable CanSats, and when additional mapping and localization capabilities where required, the CanSat would …
Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson
Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson
Graduate Theses and Dissertations
This research proposes problems, models, and solutions for the scheduling of space robot on-orbit servicing. We present the Multi-Orbit Routing and Scheduling of Refuellable On-Orbit Servicing Space Robots problem which considers on-orbit servicing across multiple orbits with moving tasks and moving refuelling depots. We formulate a mixed integer linear program model to optimize the routing and scheduling of robot servicers to accomplish on-orbit servicing tasks. We develop and demonstrate flexible algorithms for the creation of the model parameters and associated data sets. Our first algorithm creates the network arcs using orbital mechanics. We have also created a novel way to …
Artificial Intelligence, Controls, And Sensor Fusion For Optimization And Modeling Of Space Missions And Particle Accelerators, Reza Pirayeshshirazinezhad
Artificial Intelligence, Controls, And Sensor Fusion For Optimization And Modeling Of Space Missions And Particle Accelerators, Reza Pirayeshshirazinezhad
Mechanical Engineering ETDs
This PhD dissertation is devoted to developing artificial intelligence (AI) applications for space missions and particle accelerators considering constraints on the computational resources. The space mission studied in this research, the Virtual Telescope for X-ray Observations (VTXO), is the mission exploiting 2 6U-CubeSats operating in a precision formation. The goal of the VTXO project is to develop a space-based, X-ray imaging telescope with high angular resolution precision. VTXO space mission is designed and the mission is optimized to increase the performance of the mission. Trajectory optimization with AI, hybrid control, control algorithms, and high performance computing are all used to …
Path Planning And Flight Control Of Drones For Autonomous Pollination, Chapel R. Rice
Path Planning And Flight Control Of Drones For Autonomous Pollination, Chapel R. Rice
Masters Theses
The decline of natural pollinators necessitates the development of novel pollination technologies. In this thesis, a drone-enabled autonomous pollination system (APS) that consists of five primary modules: environment sensing, flower perception, path planning, flight control, and pollination mechanisms is proposed. These modules are highly dependent upon each other, with each module relying on inputs from the other modules. This thesis focuses on approaches to the path planning and flight control modules. Flower perception is briefly demonstrated developing a map of flowers using results from previous work. With that map of flowers, APS path planning is defined as a variant of …
Vertical Take-Off And Landing Control Via Dual-Quaternions And Sliding Mode, Joshua Sonderegger
Vertical Take-Off And Landing Control Via Dual-Quaternions And Sliding Mode, Joshua Sonderegger
Doctoral Dissertations and Master's Theses
The landing and reusability of space vehicles is one of the driving forces into renewed interest in space utilization. For missions to planetary surfaces, this soft landing has been most commonly accomplished with parachutes. However, in spite of their simplicity, they are susceptible to parachute drift. This parachute drift makes it very difficult to predict where the vehicle will land, especially in a dense and windy atmosphere such as Earth. Instead, recent focus has been put into developing a powered landing through gimbaled thrust. This gimbaled thrust output is dependent on robust path planning and controls algorithms. Being able to …
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Faculty Publications
This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the nonlinear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement …