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You searched for +publisher:"Oregon State University" +contributor:("Hollinger, Geoffrey A."). Showing records 1 – 3 of 3 total matches.

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Oregon State University

1. Milliken, Lauren. Modeling human expertise for providing adaptive levels of robot shared autonomy.

Degree: MS, 2017, Oregon State University

In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot. We propose using a novel formulation of Partially Observable Markov Decision Processes (POMDPs) to represent a model of the user's expertise in controlling the robot. The POMDP uses observations from the user's actions and from the environment to update the belief of the user's skill and chooses a level of control between the robot and the user. The level of control given between the user and the robot is encapsulated in macro-action controllers. The macro-action controllers encompass varying levels of robot autonomy and reduce the space of the POMDP, removing the need to plan over separate actions. As part of this research, we ran two users study, developed a method to automatically generate macro-action controller values, and applied our user expertise model to provide shared autonomy on a semi-autonomous underwater vehicle. In our first user study, we tested our user expertise model in a robot driving simulation. Users drove a simulated robot through an obstacle-filled map while the POMDP model chose appropriate macro-action controllers based on the belief state of the user's skill level. The results of the user study showed that our model can encapsulate user skill levels. The results also showed that using the controller with greater robot autonomy helped users of low skill avoid obstacles more than it helped users of high skill. We designed a controller value synthesis method to generate the variables that control the levels of autonomy in the macro-action controllers. We found differences in how the users drive the robot using a decision tree generated from the data recorded in the first user study, and we used these differences to program simulated user ``bots'' that mimic users of different skill levels. The ``bots'' were used to test a range of variables for the controllers, and the controller variables were found from minimizing obstacles hit, time to complete maps, and total distance driven from the simulated data. For our second user study, we looked at users' satisfaction without robot autonomy, with the highest amount of autonomy, and with the autonomy chosen by our expertise model. We found users we classified as beginners ranked the autonomy more favorably than those ranked as experts. We implemented our expertise model on a Seabotix vLBV300 underwater vehicle and ran a trial off the coast of Newport, Oregon. During our trials, we recorded a user driving the vehicle to predetermined waypoints. When beginner actions were performed, the user expertise model provided an increased level of autonomy which either increased throttle when far from waypoints or decreased throttle when close to waypoints. This demonstrated an implementation of our algorithm on existing robot hardware in the field. Advisors/Committee Members: Hollinger, Geoffrey A. (advisor), Grimm, Cindy (committee member).

Subjects/Keywords: Robotics

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APA (6th Edition):

Milliken, L. (2017). Modeling human expertise for providing adaptive levels of robot shared autonomy. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/61907

Chicago Manual of Style (16th Edition):

Milliken, Lauren. “Modeling human expertise for providing adaptive levels of robot shared autonomy.” 2017. Masters Thesis, Oregon State University. Accessed September 22, 2019. http://hdl.handle.net/1957/61907.

MLA Handbook (7th Edition):

Milliken, Lauren. “Modeling human expertise for providing adaptive levels of robot shared autonomy.” 2017. Web. 22 Sep 2019.

Vancouver:

Milliken L. Modeling human expertise for providing adaptive levels of robot shared autonomy. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1957/61907.

Council of Science Editors:

Milliken L. Modeling human expertise for providing adaptive levels of robot shared autonomy. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61907


Oregon State University

2. Fernández, Daniel C. Model Predictive Control for Underwater Robots in Ocean Waves.

Degree: MS, Robotics, 2015, Oregon State University

Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This thesis proposes a method using Model Predictive Control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a Linear Wave Theory (LWT) solver to approximate the component fluid dynamics under a wave field. Wave data from deployed ocean buoys is used to construct the simulated wave field. The MPC state estimator is used to optimize a set of control actions by gradient descent along a prediction horizon. The optimized control input minimizes a global cost function, the squared distance from the target state. The robot then carries out the optimized trajectory with an emphasis on real-time execution. Several prediction horizons are compared, with a horizon of 0.8 seconds selected as having a good balance of low error and fast computation. The controller with the chosen prediction horizon is simulated and found to show a 74% reduction in position error over traditional feedback control. Additional simulations are run where the MPC takes in noisy measurements of the wave field parameters. The MPC algorithm is shown to be resistant to sensor noise, providing a mean position error 44% lower than the noise-free feedback control case. Advisors/Committee Members: Hollinger, Geoffrey A. (advisor), Hatton, Ross L. (committee member).

Subjects/Keywords: model predictive control; Remote submersibles  – Automatic control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fernández, D. C. (2015). Model Predictive Control for Underwater Robots in Ocean Waves. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/57267

Chicago Manual of Style (16th Edition):

Fernández, Daniel C. “Model Predictive Control for Underwater Robots in Ocean Waves.” 2015. Masters Thesis, Oregon State University. Accessed September 22, 2019. http://hdl.handle.net/1957/57267.

MLA Handbook (7th Edition):

Fernández, Daniel C. “Model Predictive Control for Underwater Robots in Ocean Waves.” 2015. Web. 22 Sep 2019.

Vancouver:

Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1957/57267.

Council of Science Editors:

Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/57267


Oregon State University

3. Skeele, Ryan. Planning Under Uncertainty for Unmanned Aerial Vehicles.

Degree: MS, Robotics, 2016, Oregon State University

Unmanned aerial vehicle (UAV) technology has grown out of traditional research and military applications and has captivated the commercial and consumer markets, showing the ability to perform a spectrum of autonomous functions. This technology has the capability of saving lives in search and rescue, fighting wildfires in environmental monitoring, and delivering time dependent medicine in package delivery. These examples demonstrate the potential impact this technology will have on our society. However, it is evident how sensitive UAVs are to the uncertainty of the physical world. In order to properly achieve the full potential of UAVs in these markets, robust and efficient planning algorithms are needed. This thesis addresses the challenge of planning under uncertainty for UAVs. We develop a suite of algorithms that are robust to changes in the environment and build on the key areas of research needed for utilizing UAVs in a commercial setting. Throughout this research three main components emerged: monitoring targets in dynamic environments, exploration with unreliable communication, and risk-aware path planning. We use a realistic fire simulation to test persistent monitoring in an uncertain environment. The fire is generated using the standard program for modeling wildfire, FARSITE. This model was used to validate a weighted-greedy approach to monitoring clustered points of interest (POIs) over traditional methods of tracking a fire front. We implemented the algorithm on a commercial UAV to demonstrate the deployment capability. Dynamic monitoring has limited potential if if coordinated planning is fallible to uncertainty in the world. Uncertain communication can cause critical failures in coordinated planning algorithms. We develop a method for coordinated exploration of a multi-UAV team with unreliable communication and limited battery life. Our results show that the proposed algorithm, which leverages meeting, sacrificing, and relaying behavior, increases the percentage of the environment explored over a frontier-based exploration strategy by up to 18%. We test on teams of up to 8 simulated UAVs and 2 real UAVs able to cope with communication loss and still report improved gains. We demonstrate this work with a pair of custom UAVs in an indoor office environment. We introduce a novel approach to incorporating and addressing uncertainty in planning problems. The proposed Risk-Aware Graph Search (RAGS) algorithm combines traditional deterministic search techniques with risk-aware planning. RAGS is able to trade off the number of future path options, as well as the mean and variance of the associated path cost distributions to make online edge traversal decisions that minimize the risk of executing a high-cost path. The algorithm is compared against existing graphsearch techniques on a set of graphs with randomly assigned edge costs, as well as over a set of graphs with transition costs generated from satellite imagery data. In all cases, RAGS is shown to reduce the probability of… Advisors/Committee Members: Hollinger, Geoffrey A. (advisor), Tumer, Kagan (committee member).

Subjects/Keywords: Planning; Drone aircraft

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Skeele, R. (2016). Planning Under Uncertainty for Unmanned Aerial Vehicles. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/59456

Chicago Manual of Style (16th Edition):

Skeele, Ryan. “Planning Under Uncertainty for Unmanned Aerial Vehicles.” 2016. Masters Thesis, Oregon State University. Accessed September 22, 2019. http://hdl.handle.net/1957/59456.

MLA Handbook (7th Edition):

Skeele, Ryan. “Planning Under Uncertainty for Unmanned Aerial Vehicles.” 2016. Web. 22 Sep 2019.

Vancouver:

Skeele R. Planning Under Uncertainty for Unmanned Aerial Vehicles. [Internet] [Masters thesis]. Oregon State University; 2016. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1957/59456.

Council of Science Editors:

Skeele R. Planning Under Uncertainty for Unmanned Aerial Vehicles. [Masters Thesis]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59456

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