{"id":16340,"date":"2024-10-20T20:02:44","date_gmt":"2024-10-20T18:02:44","guid":{"rendered":"https:\/\/www.digitalfutures.kth.se\/?page_id=16340"},"modified":"2024-10-22T09:45:57","modified_gmt":"2024-10-22T07:45:57","slug":"designing-interaction-aware-heterogeneous-multi-robot-systems","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/postdoc-fellowships\/postdoc-fellowships-ongoing-projects\/designing-interaction-aware-heterogeneous-multi-robot-systems\/","title":{"rendered":"Designing Interaction-Aware Heterogeneous Multi-Robot Systems"},"content":{"rendered":"
\n

September 2024 – August 2026<\/p>\n

Objective
\n<\/em>The objective of this project is to learn distributed control policies for multi-robot systems that scale on-demand, in multi-laterally evolving complex dynamic environments.<\/p>\n

Background
\n<\/em>Multi-robot systems such as drone swarms offer unparalleled advantages in assisting ground-based human-robot teams in humanitarian, and disaster-response missions thanks to their ability to operate in remote, communication-denied environments. However, in practical deployments such systems must learn how to balance various auxiliary objectives on-demand for maximizing the collective utility in highly dynamic environments. For example, a robust drone swarm control policy must prioritize the most severely affected area in a disaster-response event, and direct more robots to tackle it, on-the-fly.<\/p>\n

In this project, we aim to design a framework that can help us learn such control policies while taking into account the robot\u2019s varying capabilities. Upon designing such a framework, we further understand that we can minimize the human bias in hand-engineered task prioritizing.<\/p>\n

About the Digital Futures Postdoc Fellow
\n<\/em>Malintha Fernando<\/strong> received his Ph.D in 2023 from Indiana University, Bloomington (USA). His doctoral research focused on designing cooperative scalable control policies for multi-drone systems that are robust to communication failures. Such policies lend themselves to many Urban Air Mobility (UAM) applications such as autonomous parcel delivery, where decentralized decision-making under local information is required to achieve the necessary scalability over the geographical span and in the number of vehicles.<\/p>\n

Prior to joining KTH, Malintha worked as a visiting lecturer in Machine Learning at Indiana University. He has completed an internship at Open Robotics, Mountain View, and completed his undergraduate education from University of Moratuwa, Sri Lanka.<\/p>\n

Main supervisor
\n<\/em>Silun Zhang<\/strong>, Assistant Professor, Division of Optimization and System Theory, Department of Mathematics, KTH<\/p>\n

Co-supervisor
\n<\/em>Petter \u00d6gren<\/strong>, Professor, Division of Robotics, Perception and Learning (RPL) at KTH, Digital Futures Fellow<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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