Bardienus Pieter Duisterhof

Email: bduister@cs.cmu.edu

I am a Ph.D. student at Carnegie Mellon University (CMU), in the Robotics Institute advised by Sebastian Scherer. Before coming to CMU, I completed a Bachelor's and Master's degree in Aerospace Engineering at Delft University of Technology, in the Netherlands. Advised by Guido de Croon, I studied efficient bio-inspired algorithms for autonomous flight of tiny flying drones. In 2019 I was a visiting student at Vijay Janapa Reddi's Edge Computing lab, at Harvard University, where we studied Deep Reinforcement Learning for tiny robots. My research focus continues to be on efficient AI for fully autonomous operation of resource-constrained miniature robots. At the intersection of computer vision, machine learning, and systems, I hope to contribute to a future where robots are affordable, safe, and useful.

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Research

Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments
Bardienus P Duisterhof, Shushuai Li, Javier Burgués, Vijay Janapa Reddi, Guido C.H.E. de Croon
IROS, 2021
arXiv / video / code

We have developed a swarm of autonomous, tiny drones that is able to localize gas sources in unknown, cluttered environments. Bio-inspired AI allows the drones to tackle this complex task without any external infrastructure.

Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter
Bardienus P Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust , Guido C.H.E. de Croon, Vijay Janapa Reddi
ICRA, 2021
paper / video / code

We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.

A Tailless Flapping Wing MAV Performing Monocular Visual Servoing Tasks
Diana A. Olejnik, Bardienus P Duisterhof, Matej Karásek , Kirk Y. W. Scheper, Tom van Dijk, Guido C.H.E. de Croon
Unmanned Systems, Vol. 08, No. 04, pp. 287-294 , 2020
paper / video

This paper describes the computer vision and control algorithms used to achieve autonomous flight with the ∼30g tailless flapping wing robot, used to participate in the International Micro Air Vehicle Conference and Competition (IMAV 2018) indoor microair vehicle competition.

Teaching
AE2235-I: Aerospace Systems & Control Theory
Media Coverage
Forbes
IEEE Spectrum Video Friday
Robohub
Bitcraze Blog
PiXL Drone Show
Awards
Best Graduate in Engineering, TU Delft, academic year 2020-2021
Best Graduate in Aerospace Engineering, TU Delft, academic year 2020-2021
Innovation Award, IMAV 2018 Autonomous Drone Race

Modified version of template from here