Available at: https://digitalcommons.calpoly.edu/theses/3032
Date of Award
6-2025
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Siavash Farzan
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
This research presents a novel approach to dynamic task allocation in heterogeneous multi-robot systems with integrated fault detection capabilities. As multiple industries are becoming more reliant on multi-robot systems for tasks, maintaining operational efficiency despite robot failures becomes critical. We propose a framework that combines optimization-based task allocation with a Kalman filter that estimates task progress for anomaly detection to identify unreliable agents and dynamically redistribute tasks. Observing values such as the normalized innovation squared (NIS), covariance, and progress rate, the algorithm can designate a robot as faulty. Embedding information about which robots are faulty in the task algorithm allows for the system to change based on robots performances. The addition of an adaptive Q matrix allows for the system to be flexible as the tasks are rearranged. Simulated tests were done to validate the approach by forcing failure conditions, including sensor noise, measurement bias, stale progress, communication faults, and task abandonment. Results show that stale progress and task abandonment are the easiest to detect, measurement bias and measurement noise are dependent on the magnitude of the fault, and communication failures heavily lag in the detection time.