Malintha Fernando (Ph.D.)
Visiting Lecturer | Machine Learning, Robotics
Indiana University, Bloomington
I am currently a full-time visiting lecturer with the department of Intelligent Systems Engineering of Indiana University. I teach the Machine Learning for Signal Processing (ENGR-E511) class in this Spring. My research focuses on designing decentralized autonomy for intereacting dynamical systems or systems of systems to act as a cohesive under stochastic observations and communication.
News
| Dec, 2023 | Honored to be the student speaker at the Luddy School of Informatics, Computing and Engineering’s winter graduation celebration. I focused on our responsibility toward the public on designing responsible and inclusive AI (News Story, Video). |
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| Nov, 2023 | Successfully defended my Ph.D. dissertation titled “Cooperative Multi-Agent Autonomy Under Communication Uncertainties for Aerial Robot Fleets”. |
| Oct, 2023 | Gave an invited talk at the University of Cambridge, UK. Thanks for inviting me, Prof. Amanda Prorok. |
| Aug, 2023 | Gave an invited seminar at Georgia Tech School of Interactive Computing. Thank you for a wonderful day, Prof. Gombolay and the CORE robotics team! |
| Jul, 2023 | Presented our work at RSS 2023, and the Workshop on Multi-Agent Planning and Navigation in Challenging Environments (MultiAct) in Daegu, South Korea. |
Selected Research Projects
Graph Attention Aerial Fleet Coordination
This work propose a multi-step partially-observable stochastic game formulation for coordinating fully-autonomous heterogeneous eVTOL fleet with graph-attention multi-agent reinforcement-learning (MARL). The paper got accepted to Robotics: Science and Systems, 2023 . [Paper].
Graphical Game-Theoretic Communication-Aware Coverage
Wireless communication plays a crucial role in distributed coordination control of robot swarms. In this work we propose a robust, real-time coverage control approach for UAV swarms to provide the wireless coverage for a ground robot team using a UAV team operating over large geographic region with the local communication. The two images show a static and mobile ground robot team, where the UAV fleet is changing their formation to maximize the coverage for the ground robots. The dynamic communication links among the UAVs are shown in grey.
Checkout the full demo on Youtube .
Real-Time Distributed Flocking Control
Murmurations are one of the most beatiful natural phenomena in the world, and characterizes the perfect swarm . In this work I tried to recreate the flocking characteristics UAVs using a variational inference approach in a distributed manner. The drones only uses their local observations to infer the control actions from feasible set; which makes this approach unique compared to the literature which we can guarantee the covergence and the dynamical feasibility.
Checkout the full demo on Youtube .
Multi-UAV Formation Control
Drone formation control has been gaining a lot of attention lately with applications ranging from entertainment to and defense industries. In this work I propose a rigid-body-based formation controlling approach with drone aggressive trajectory generation. The drones showed the ability to move at 2.5ms-1 (over 10 body-lengths a second) in a user defined formation with real-time receding horizon trajectory planning.
Checkout the formation control demo on Youtube .
Checkout the minimum-jerk trajectory generation demo on Youtube .
Selected Publications
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Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility2023 -
Online flocking control of UAVs with mean-field approximationIn 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021
