Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.1109/ICRA48506.2021.9561590 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
Keywords | DocType | Volume |
Motion and Path Planning, Aerial Systems: Applications, Reinforcement Learning | Conference | 2021 |
Issue | ISSN | Citations |
1 | 1050-4729 | 0 |
PageRank | References | Authors |
0.34 | 8 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Bardienus Pieter Duisterhof | 1 | 0 | 1.35 |
Srivatsan Krishnan | 2 | 96 | 6.86 |
Jonathan J. Cruz | 3 | 0 | 0.34 |
Colby R. Banbury | 4 | 0 | 0.34 |
William Fu | 5 | 2 | 1.12 |
Aleksandra Faust | 6 | 68 | 14.83 |
Guido C. H. E. de Croon | 7 | 18 | 5.94 |
Guido de Croon | 8 | 26 | 6.66 |