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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

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. Our deep-RL algorithm finds a high-performance solution to a challenging problem, even in presence of high noise levels and generalizes across real and simulation environments with different obstacle configurations. We verify our approach with simulation and in-field testing on a Bitcraze CrazyFlie using only the cheap and ubiquitous Cortex-M4 microcontroller unit. The results show that by end-to-end application-specific system design, our contribution consumes almost three times less additional power, as compared to a competitive learning-based navigation approach onboard a nano quadcopter. Thanks to our observation space, which we carefully design within the resource constraints, our solution achieves a 94% success rate in cluttered and randomized test environments, as compared to the previously achieved 80%. We also compare our strategy to a simple finite state machine (FSM), geared towards efficient exploration, and demonstrate that our policy is more robust and resilient at obstacle avoidance as well as up to 70% more efficient in source seeking. To this end, we contribute a cheap and lightweight end- to-end tiny robot learning (tinyRL) solution, running onboard a nano quadcopter, that proves to be robust and efficient in a challenging task.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
Subtitle of host publicationProceedings
PublisherIEEE
Pages7242-7248
Number of pages7
ISBN (Electronic)978-1-7281-9077-8
ISBN (Print)978-1-7281-9078-5
DOIs
Publication statusPublished - 2021
EventICRA 2021: IEEE International Conference on Robotics and Automation - Hybrid at Xi'an, China
Duration: 30 May 20215 Jun 2021

Conference

ConferenceICRA 2021
CountryChina
CityHybrid at Xi'an
Period30/05/215/06/21

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