Author ORCID Identifier

https://orcid.org/0000-0003-0472-0630

Semester

Fall

Date of Graduation

2022

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Jason N. Gross

Committee Co-Chair

Cagri Kilic

Committee Member

Cagri Kilic

Committee Member

Yu Gu

Committee Member

Guilherme A. S. Pereira

Abstract

Multi-robot systems provide more adaptability and robustness than conventional single-robot systems. This is because they have the ability to achieve partial completion of a task in the presence of failures due to the intrinsic inclusion of non-single-point failure. In multi-robot systems, cooperative localization refers to the use of relative measurements to improve the group’s overall localization performance. While centralized architectures in the system may result in unfeasible cases due to the high cost of computation and communication, decentralized methods distribute the computation among the robots in the group, enhancing the performance and making the system more efficient. Achieving a reliable localization performance is particularly challenging inGlobal Navigation Satellite System (GNSS) denied environments. Relying on GNSS only solutions for localization can suffer due to environmental restrictions since GNSS is often inaccessible or degraded for many locations, such as urban, indoor, subterranean, forested, and planetary environments. This thesis considers a decentralized cooperative localization algorithm that benefits from the use of different pseudo-measurement, such as Zero-Velocity Update (ZUPT) and Zero Angular Rate Update (ZARU), to improve the localization performance of the group. The Error-State Decentralized Extended Kalman Filter (ES-DEKF) utilizes proprioceptive (i.e., Inertial Measurement Unit (IMU), Wheel Encoders) and exteroceptive (i.e., Ultra-Wide Band (UWB), Camera) measurements to improve the localization performance of the system in a degraded GNSS environment. Multiple scenarios weretested in simulation and with real robotsto validatethe performance of the algorithm. Moreover, different heuristics of deciding when to utilize pseudo-measurements were compared to evaluate the benefit of these updates.

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