Autonomous systems, such as Unmanned Aerial
Vehicles (UAVs), are expected to run complex reinforcement
learning (RL) models to execute fully autonomous positionnavigation-time tasks within stringent onboard weight and power
constraints. We observe that reducing onboard operating voltage
can benefit the energy efficiency of both the computation and
flight mission, however, it can also result in on-chip bit failures
that are detrimental to mission safety and performance. To this
end, we propose BERRY, a robust learning framework to improve
bit error robustness and energy efficiency for RL-enabled autonomous systems. BERRY supports robust learning, both offline
and on-board the UAV, and for the first time, demonstrates the
practicality of robust low-voltage operation on UAVs that leads to
high energy savings in both compute-level operation and systemlevel quality-of-flight. We perform extensive experiments on 72
autonomous navigation scenarios and demonstrate that BERRY
generalizes well across environments, UAVs, autonomy policies,
operating voltages and fault patterns, and consistently improves
robustness, efficiency and mission performance, achieving up to
15.62% reduction in flight energy, 18.51% increase in the number
of successful missions, and 3.43× processing energy reduction.
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Autonomous Flying With Neuromorphic Sensing
Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.
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- Award ID(s):
- 1734744
- PAR ID:
- 10280823
- Date Published:
- Journal Name:
- Frontiers in Neuroscience
- Volume:
- 15
- ISSN:
- 1662-453X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fnins.2021.672161
