My publications


Nanjangud S, Blacker P, Bandyopadhyay S, Gao Y (2018) Robotics and AI-Enabled On-Orbit Operations With Future Generation of Small Satellites, Proceedings of the IEEE 106 (3) pp. 429-439 Institute of Electrical and Electronics Engineers (IEEE)
The low-cost and short-lead time of small satellites
has led to their use in science-based missions, earth observation,
and interplanetary missions. Today, they are also key instruments
in orchestrating technological demonstrations for on-orbit
operations (O3) such as inspection and spacecraft servicing with
planned roles in active debris removal and on-orbit assembly.
This paper provides an overview of the robotics and autonomous
systems (RASs) technologies that enable robotic O3 on smallsat
platforms. Major RAS topics such as sensing & perception,
guidance, navigation & control (GN&C) microgravity mobility
and mobile manipulation, and autonomy are discussed from the
perspective of relevant past and planned missions.
Blacker P., Bridges C. P., Hadfield S. (2019) Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs, Proceedings of the 13th NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2019) Institute of Electrical and Electronics Engineers (IEEE)

Interest is increasing in the use of neural networks
and deep-learning for on-board processing tasks in the space
industry [1]. However development has lagged behind terrestrial
applications for several reasons: space qualified computers
have significantly less processing power than their terrestrial
equivalents, reliability requirements are more stringent than the
majority of applications deep-learning is being used for. The long
requirements, design and qualification cycles in much of the space
industry slows adoption of recent developments.

GPUs are the first hardware choice for implementing neural
networks on terrestrial computers, however no radiation
hardened equivalent parts are currently available. Field Programmable
Gate Array devices are capable of efficiently implementing
neural networks and radiation hardened parts are
available, however the process to deploy and validate an inference
network is non-trivial and robust tools that automate the process
are not available.

We present an open source tool chain that can automatically
deploy a trained inference network from the TensorFlow framework
directly to the LEON 3, and an industrial case study of
the design process used to train and optimise a deep-learning
model for this processor. This does not directly change the
three challenges described above however it greatly accelerates
prototyping and analysis of neural network solutions, allowing
these options to be more easily considered than is currently

Future improvements to the tools are identified along with a
summary of some of the obstacles to using neural networks and
potential solutions to these in the future.

Nanjangud Angadh, Blacker Peter C., Young Alex, Saaj Chakravarthini M., Underwood Craig I., Eckersley Steve, Sweeting Martin, Bianco Paolo (2019) Robotic architectures for the on-orbit assembly of large space telescopes, Proceedings of the Advanced Space Technologies in Robotics and Automation (ASTRA 2019) symposium European Space Agency (ESA)
Space telescopes are our ?eyes in the sky? that enable unprecedented
astronomy missions and also permit Earth
observation integral to science and national security. On
account of the increased spatial resolution, spectral coverage,
and signal-to-noise ratio, there is a constant clamour
for larger aperture telescopes by the science and surveillance
communities. This paper addresses a 25 m modular
telescope operating in the visible wavelengths of the electromagnetic
spectrum; such a telescope located at geostationary
Earth orbit would permit 1 m spatial resolution of
a location on Earth. Specifically, it discusses the requirements
and architectural options for a robotic assembly
system, called Robotic Agent for Space Telescope Assembly
(RASTA). Aspects of a first-order design and initial
laboratory test-bed developments are also presented.