Toward Robust Multi-Agent Autonomous Underwater Inspection with Consistency and Global Optimality Guarantees
Abstract
Teams of autonomous robotic systems have the potential to have a dramatic positive effect on our society. In the underwater domain specifically, collaborative multi-agent autonomous systems have the potential to lead to significant increases in efficiency, safety, and data quality. However, while autonomous systems have been widely accepted within structured environments such as manufacturing plants and distribution facilities, they have not been nearly as widely adopted in unstructured environments. The primary reason for this is that the reliability of autonomous systems in unstructured environments has not yet reached a level where it is cost and time effective to widely adopt such platforms. One key element of the reliability of autonomous systems is the robustness of navigation and localization algorithms to common failure cases such as outlier measurements, bad initialization, and inaccurate characterizations of uncertainty. Accordingly, this thesis proposes methods for simultaneous localization and mapping (SLAM), multi-agent map merging, trajectory alignment, and uncertainty characterization that seek to address some of these failure cases.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2019
- Accession Number
- AD1124126
Entities
People
- Joshua G. Mangelson
Organizations
- University of Michigan