Robot Motions that Mitigate Uncertainty
| dc.contributor.author | Toubeh, Maymoonah | en |
| dc.contributor.committeechair | Williams, Ryan K. | en |
| dc.contributor.committeechair | Tokekar, Pratap | en |
| dc.contributor.committeemember | Kochersberger, Kevin Bruce | en |
| dc.contributor.committeemember | Chantem, Thidapat | en |
| dc.contributor.committeemember | Abbott, Amos L. | en |
| dc.contributor.committeemember | Plassmann, Paul E. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2024-10-24T08:00:19Z | en |
| dc.date.available | 2024-10-24T08:00:19Z | en |
| dc.date.issued | 2024-10-23 | en |
| dc.description.abstract | This dissertation addresses the challenge of robot decision making in the presence of uncertainty, specifically focusing on robot motion decisions in the context of deep learning-based perception uncertainty. The first part of this dissertation introduces a risk-aware framework for path planning and assignment of multiple robots and multiple demands in unknown environments. The second part introduces a risk-aware motion model for searching for a target object in an unknown environment. To illustrate practical application, consider a situation such as disaster response or search-and-rescue, where it is imperative for ground vehicles to swiftly reach critical locations. Afterward, an agent deployed at a specified location must navigate inside a building to find a target, whether it is an object or a person. In the first problem, the terrain information is only available as an aerial georeferenced image frame. Semantic segmentation of the aerial images is performed using Bayesian deep learning techniques, creating a cost map for the safe navigation ground robots. The proposed framework also accounts for risk at a further level, using conditional value at risk (CVaR), for making risk-aware assignments between the source and goal. When the robot reaches its destination, the second problem addresses the object search task using a proposed machine learning-based intelligent motion model. A comparison of various motion models, including a simple greedy baseline, indicates that the proposed model yields more risk-aware and robust results. All in all, considering uncertainty in both systems leads to demonstrably safer decisions. | en |
| dc.description.abstractgeneral | Scientists need to demonstrate that robots are safe and reliable outside of controlled lab environments for real-world applications to be viable. This dissertation addresses the challenge of robot decision-making in the face of uncertainty, specifically focusing on robot motion decisions in the context of deep learning-based perception uncertainty. Deep learning (DL) refers to using large hierarchical structures, often called neural networks, to approximate semantic information from input data. The first part of this dissertation introduces a risk-aware framework for path planning and assignment of multiple robots and multiple demands in unknown environments. Path planning involves finding a route from the source to the goal, while assignment focuses on selecting source-goal paths to fulfill all demands. The second part introduces a risk-aware motion model for searching for a target object in an unknown environment. Being risk-aware in both cases means taking uncertainty into account. To illustrate practical application, consider a situation such as disaster response or search-and-rescue, where it is imperative for ground vehicles to swiftly reach critical locations. Afterward, an agent deployed at a specified location must navigate inside a building to find a target, whether it is an object or a person. In this dissertation, deep learning is used to interpret image inputs for two distinct robot systems. The input to the first system is an aerial georeferenced image; the second is an indoor scene. After the images are interpreted by deep learning, they undergo further processing to extract information about uncertainty. The information about the image and the uncertainty is used for later processing. In the first case, we use both a traditional path planning method and a novel path assignment method to assign one path from each source to a demand location. In the second case, a motion model is developed using image data, uncertainty, and position in relation to the anticipated target. Several potential motion models are compared for analysis. All in all, considering uncertainty in both systems leads to demonstrably safer decisions. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:38825 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121380 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Uncertainty | en |
| dc.subject | Robot Motion | en |
| dc.subject | Deep Learning | en |
| dc.title | Robot Motions that Mitigate Uncertainty | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
Files
Original bundle
1 - 1 of 1