Autonomous Planetary Vehicle Development Platform: New Perspectives

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.