Semester
Summer
Date of Graduation
2024
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
Yu Gu
Committee Co-Chair
Jason Gross
Committee Member
Jason Gross
Committee Member
Guilherme Pereira
Abstract
The advancement of autonomous search holds significant promise for applications ranging from emergency response to planetary exploration. This thesis investigates strategies to enhance autonomous search performance in large-scale environments. The main contribution of this work is its practical application in real-world scenarios, where efficient search methods are essential for managing vast amounts of data, particularly in large environments. Effective search planning requires navigating complexities such as limited prior information and managing large state spaces, necessitating advanced strategies to plan with this limited information. Additionally, balancing exploration and exploitation is crucial for optimizing the search process, as it ensures thorough coverage of the search area while efficiently utilizing resources. These challenges highlight the need for innovative approaches as traditional methods often fall short in extensive environments. State-of-the-art tools such as Monte Carlo Tree Search (MCTS) play a crucial role in advancing autonomous search technologies. MCTS is effective at handling large, complex state spaces and balancing exploration and exploitation, making it well-suited for scenarios with vast search spaces and limited prior information. However, MCTS can be computationally expensive and limited in its planning horizon, leading to potential suboptimal decisions in long-term scenarios. To address these challenges, this thesis introduces a novel approach that formulates the search problem as a belief Markov decision process with options (BMDP-O). This formulation integrates sequences of actions to navigate between regions of interest (ROIs), enabling efficient scaling to large environments. This allows for more efficient planning by breaking down the search into manageable segments and focusing resources on the most promising regions. The proposed method proves particularly efficient in environments with multiple ROIs. A robot can search within an ROI and transition to nearby ROIs without extensive searching in low-probability areas. This strategy ensures a balanced and efficient search in extensive environments. Results demonstrate that this method finds objects faster in large environments compared to traditional MCTS while still maintaining computational efficiency.
Recommended Citation
Collins, Matthew A., "Autonomous Object Search Planning in Large-Scale Environments" (2024). Graduate Theses, Dissertations, and Problem Reports. 12602.
https://researchrepository.wvu.edu/etd/12602