07/05/2024
By Hamid Osooli

The Richard A. Miner School of Computer & Information Sciences, Department of Computer Science, invites you to attend a Master's Thesis defense by Hamid Osooli on “A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams.”

Candidate name: Hamid Osooli
Date: Thursday, July 18, 2024
Time: 1 p.m. ET
Location: DAN 309 and via Zoom

Thesis title: A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams

Committee:

  • Reza Azadeh (Supervisor), Miner School of Computer and Information Sciences, University of Massachusetts Lowell
  • Kshitij Jerath (Committee member), Mechanical and Industrial Engineering Department, University of Massachusetts Lowell
  • Maru Cabrera (Committee member), Miner School of Computer and Information Sciences, University of Massachusetts Lowell

Abstract:
In post-disaster scenarios, search and rescue operations often require the coordinated efforts of multiple robots and humans to perform a variety of challenging tasks. Existing planners, while effective in certain aspects, frequently overlook critical elements such as information gathering, task assignment, and comprehensive planning. Furthermore, previous works that consider robot capabilities and victim requirements often suffer from significant time complexity due to repetitive and inefficient planning steps.

This thesis addresses these limitations by proposing a comprehensive framework that encompasses scouting, task assignment, and path-planning steps. The proposed Multi-Stage Multi-Robot Task Assignment framework leverages detailed information about robot capabilities, victim requirements, and the historical performance of robots to optimize task assignments, thereby enhancing the overall success rate of rescue missions. An iterative process is integrated to ensure that primary objectives are achieved while considering problem constraints.

The thesis employs off-the-shelf path-planning methods while introducing a hierarchical game theory-based framework for the scouting step. This innovative approach enables agents to make more informed decisions, optimizing their actions based on the relative information provided by their teammates. The incorporation of game theory significantly accelerates the accomplishment of the rescue mission, reducing completion time by 66% compared to scenarios where agents do not communicate their information, and by 58% compared to scenarios where agents only communicate information without suggesting actions to teammates.

Additionally, the thesis develops and evaluates an environment for multi-agent search and rescue that leverages multi-agent reinforcement learning (MARL). The environment is designed to enhance the communication and coordination capabilities of the agents, allowing them to effectively locate and rescue victims. The agents are trained using Q-learning and Deep Q-Networks (DQN), with different aspects of this environment evaluated and detailed in the results section.

The proposed framework is validated through extensive testing on four different maps, where it is compared to a state-of-the-art baseline. The results highlight the superior performance of the proposed task assignment algorithm, achieving a 97% improvement over the baseline in terms of planning time.