07/16/2024
By Ryan Donald

The Francis College of Engineering, Department of Electrical and Computer Engineering invites you to attend a Master's Thesis defense by Ryan Donald on “Methods for Measuring and Increasing Robot Autonomy in Dynamic Environments."

Candidate Name: Ryan Donald
Degree: Master’s
Defense Date: Friday, July 26, 2024
Time: 1 p.m. ET
Location: BAL-302 and via Zoom.
Thesis/Dissertation Title: Methods for Measuring and Increasing Robot Autonomy in Dynamic Environments

Committee:

  • Advisor: Reza Azadeh, Ph.D., Miner School of Computer and Information Sciences, University of Massachusetts Lowell
  • Paul Robinette, Ph.D., Department of Electrical and Computer Engineering, University of Massachusetts Lowell
  • Orlando Arias, Ph.D., Department of Electrical and Computer Engineering, University of Massachusetts Lowell

Abstract:

Achieving success in real-world environments hinges significantly on enhancing the level of autonomy in robots performing their tasks. Improving robot autonomy, however, is not straightforward and comes with a number of challenges. Two of these challenges include (a) the development of metrics to quantitatively measure autonomy, and (b) the generation of behaviors that enable robots to handle uncertain situations more effectively. To measure the level of autonomy in robots, the development of accurate and reproducible metrics is essential. In this thesis, we investigate existing metrics for defining and measuring robot autonomy and develop new ones for both non-contextual and contextual autonomy measures. These measures are evaluated with seven small Unmanned Aerial System (sUAS) platforms. For the non-contextual evaluations, each platform's specifications and autonomous behaviors are gathered through datasheets and real-world verification. We then evaluate a number of methods for combining feature-dependent scores into a single score for each sUAS and proposed a Non-Contextual Autonomy Potential (NCAP) coordinate to produce a single metric for non-contextual autonomy. In the contextual evaluations, we utilize a set of preexisting tests and data for each sUAS. We design several cascaded Fuzzy Inference Systems (cFIS) which we then utilize to score the performance of each sUAS within these tests. We then calculate a predictive score based on the scores of each sUAS within each of the individual tests. The second challenge deals with the development of autonomous behaviors for robots. These behaviors must allow the robot to safely perform their tasks in environments which can rapidly change and may not be as structured as a laboratory environment. Dynamic environments are especially difficult, as they involve moving obstacles, goals, and forces in the inertial frame of the system. Towards this goal, we propose a framework for improving robot performance, and hence its autonomy, during an inspection task in dynamic environments. Our framework which allows the robot to predict the motion of the environment and make decisions accordingly. Our framework consists of (a) a Learning from Demonstration (LfD) module for encoding the robot's movements and skill reproduction, (b) an Unscented Kalman Filter (UKF) for state estimation, and (c) a Hidden Markov Model (HMM) for high-level decision making. We evaluate the performance of this framework in a number of simulation and real-world experiments and show that this framework improves the performance of the robot. Additionally, we determine that the weighted product method of combining scores into a single score is the best method as it allows for easy combinations of additional metrics to existing data. Our non-contextual autonomy evaluation shows the benefits and drawbacks of each combination method, and an evaluation based upon the NCAP coordinate, and determines a ranking of the systems based upon their NCAP coordinate. In the contextual autonomy evaluation, we show how our cFIS are able to combine data from multiple different experiments to produce an overall autonomy evaluation of a system, while also having the adaptability to include various different tests. Lastly, we showcase how our framework can improve the autonomy of robots within dynamic environments.