July 21 2022
As an avid maths and physics enthusiast, I am always curious about how accurately our scientific and mathematical theories model the real world. Are they reliable enough to emulate the chaotic reality of ground truth? This question inspired me to embark upon a new summer project wherein I plan to build the 'BlueRay', an autonomous underwater vehicle (AUV) to solve it. Using the CAD files of the AUV, I would simulate the robot in a virtual environment and train a Machine Learning (ML) model to complete specified tasks (such as moving the AUV to predefined trajectory, collecting data, taking pictures, etc.). After training the model, through the implementation of the robot in the real world, I would be able to test how successful the simulation would be at emulating real world conditions. Will the simulation prove to be of adequate representation of the real world to the extent the learning done would be transferrable? Or would the agent exploit patterns in the physics simulation that would cause the expected and real positions to diverge and become inconsistent? If this is something that fires your curiosity, follow my journey through 'Aditya's Tech Journal' where I will share the my successes, trials, and tribulations.
My first step, is to build hardware and software systems simultaneously to control the AUV. An important consideration when planning the construction of the AUV would be to construct a form that can easily and effectively be modelled using mathematics to reduce computations required in the simulation thus greatly reducing the time required and the recurrence of errors. An another key factor would be to ensure that the robot uses hardware and software systems that establish accurate and efficient control of the robot. This allows for the transferability of software between the robot's internal algorithms and simulation for control over thrusters and increased accuracy from the modelling. The AUV would also need to be able to accurately measure its position in space. Thus it would require the implementation of various sensors and potentially processing algorithms in order to enable comparison with its simulated counterpart.
The first challenge that serves as a prerequisite for the experiment would be to first find a mathematical model for the simulation that can match the movement of the AUV. It would be required that the state of the robot in the simulated and real world roughly match each other in minimal test. If the mathematical models are inconsistent at this level, it would be meaningless to proceed to the next step of training the AI as the model has already been invalidated as a candidate for accurately simulating the real world.
Photo Credit: BBAUV 4.0 - NUS
Photo Credit: Mantadroid - NUS
The premise of the experiment would be to use the robot's shape and software and emulate a virtual copy of the robot in a shared environment (a real environment that has been modelled digitally). Using this digital copy I would simulate scenarios far quicker than possible in the real world and using AI create an efficient algorithm for solving a specific task (e.g. getting the robot to a certain position). And finally I would draw conclusions about the accuracy of the mathematical/scientific models by running the AI generated algorithm on the real hardware. Using the on-board sensors and tracking the position of the AUV in space, I'll be able to determine if the AI was able to exploit a flaw in the simulation. I'll be able to do so by measuring the inconsistency/error results between the simulated and real positions of robot.Â
Photo Credit: ECA GROUP, A9-E AUV
Underwater robots are one of the most complex systems to design due to the plethora of constraints imposed by working underwater. Water pressure will force the hull to collapse in on itself and implode. Electronics must be kept dry in order to avoid damage and short-circuiting. Buoyancy must be taken into account when creating neutrally buoyant systems, or design ballast systems to raise and lower the drone. Digital communication is impeded by the water making it impossible to effectively communicate from the surface to the drone without using cables. All of these restrictions must be met in addition to the regular design challenges associated with building robots. It should be durable, power-efficient, agile, and responsive as well as having emergency protocols in place. Even in the case of a significantly less challenging environment such a swimming pool, creating an effective system can still be tough. AUVs are an important tool for scientists to access the various systems that exist underwater. Our oceans are the last unexplored frontier of our planet and creating modular systems to discover is of paramount importance going forward. The field of marine robotics has garnered a lot of research interesting in the scientific community. Reading and researching about this innovative field has been a source of inspiration and motivation for me.
Additional Resources/Further Reading:
https://www.sciencedirect.com/science/article/pii/S0025322714000747?via%3Dihub