Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter
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 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 competing 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.
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 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 competing 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.
Research Areas
