This paper presents the design, architecture, and
constructions of a planetary autonomous exploration vehicle platform, which can
be used to develop and test Artificial Intelligence, based software and
generate a large dataset for the training of neural networks. It can also be
used for testing a wide range of sensors. Rovers will be at the frontier of
planetary exploration, capable of executing tasks without human supervision in
a harsh and unpredictable environment, and to do so it requires real-time
control over its actuators to keep it away from a risky situation. Due to the
limitations imposed by communication latency and small window of communication
through deep space satellites, existing Mars rovers are semi-autonomous. To
develop AI-based software for the rover, a low-cost alternative of a planetary
rover is required, which can facilitate data generation from different types of
sensors and actuators for a long duration and perform all possible scenarios
and actions. Presently this task is done using simulation or replicas of the
actual rovers used in planetary missions which are very costly. The proposed rover
design is a low-cost alternative, capable of powering, driving varieties of
sensors, scale up to new hardware and record data as specified by the user. It
can also be used to test the newly developed algorithm before being tested on
an actual rover. This platform can be used as a simulation platform for
software as the proposed platform is directly in contact with the environmental
factors.
Author(s) Details
Bishwajit Pal
Department of MCA (Visveshwaraya Technological University),
Dayananda Sagar College of Engineering, Bangalore, Karnataka, India.
Samitha Khaiyum
Department of MCA (Visveshwaraya Technological University),
Dayananda Sagar College of Engineering, Bangalore, Karnataka, India.
Please see the book here :- https://doi.org/10.9734/bpi/etert/v5
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