"What is the issue being addressed?
There is no way to efficiently explore unknown environments. Phoenix addresses this with a method that uniquely combines innovations in hardware, sensing, and artificial evolution to produce swarms of evolving motes that efficiently explore, map and sense in environments which would otherwise remain unknown. We show the success of our project through a demonstration of how Phoenix maps a pipeline.
Why is it important for society?
Whenever humankind explores a new environment, we always acquire new knowledge. That knowledge is intrinsically important because it broadens our collective understanding of natural phenomena and may entail social implications regarding quality of life, health, and wellbeing. Consider the following challenges:
– Mapping pipelines to find obstructions, leaks or faults to more efficiently deliver drinking water, prevent contamination, or monitor water quality deep within pipeline networks.
– Exploring underground channels, which cannot be otherwise accessed without damaging them to more efficiently extract oil or natural gas or to the search of natural CO2 storage locations.
– Measuring from the depths of glaciers or inside volcanos to better model climate change.
However, there are places which, even today, cannot be reached by even the most advanced sensors. Phoenix targets those places.
What is the overall objective?
The objective of Phoenix is to develop a method to explore unknown and inaccessible environments. This involves: 1) the design of versatile agents (i.e. mote) technology, 2) the definition of techniques to formalize expert knowledge to influence the evolution of agents and their ""rebirth"" and 3) the development of a co-evolutionary framework to jointly optimize sensor motes and environment models.
Phoenix also sheds light on emergent properties of self-organization, local adaptation and division of labor in autonomous systems.
Conclusion
Although not all system components are fully integrated and some steps are still manual, the feasibility of Phoenix was confirmed through demonstrators. In the main demonstrator, agents explore an unknown pipe loop system and a human interface provides input to the system, a co-evolution method optimizes the agents based on extracted information from the environment, and uses this to configure instincts in agents. While the hardware in the agents uses off-the-shelf components, the implementation of the instincts, as well as the adaptable parameters of the sensors are made consistent with the developed miniaturized hardware. This enables, in a future step, to seamlessly replace the off-the-shelf components with the miniaturized IC based hardware. Three additional demonstrators have shown the hardware development contributions of Phoenix, aiming for miniaturization and energy reduction. Ultrasound electronics and transducers were implemented that are smaller and more efficient than prior-art, and we validated that communication and ranging can be performed underwater up to several meters of distance. Moreover, adaptable sensors and instincts were integrated and also confirmed their functionality, at a size and energy budget that is orders of magnitude below off-the-shelf components. With these components, it is shown feasible to reduce the size of the black ball agent (6 cm in diameter) to a cm-sized (or smaller) ball, AND to operate the device for a much longer amount of time.
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