Have you ever been amazed by the extraordinary abilities of animals? Especially, the capability of some animals, like insects, to track and find the source of particular scents, even over great distances? Scientists and engineers have been inspired by these abilities and have worked to reverse engineer or mimic them in bots and artificial intelligence (AI) agents. In a recent study, researchers from the Universities of Washington and Nevada, Reno, have developed a novel strategy employing artificial neural networks (ANNs) to better comprehend the astonishing capacity of flying insects.
In this blog post, we will discuss the research conducted by the scientists and how their model might help in the development of autonomous agents that can track odors for future use in environmental monitoring, search-and-rescue missions, and other applications.
To train their plume-tracking agents, the researchers created a model of an odor coming from a source inside a windy arena with a total size of about 120 m2 using deep reinforcement learning (DRL). The simulator’s ability to produce plumes with different odor concentrations and wind patterns gave the scientists an extra edge in observing how the agent would act in specific circumstances. The researchers’ findings indicate that their model might reproduce the molecular mechanisms that regulate animal olfactory plume tracking.
The researchers envision their model influencing the development of autonomous agents that can track odors for future use in environmental monitoring, search-and-rescue missions, and other applications. By increasing the physical and biological integrity of their simulations and agents in their subsequent research, scientists hope to further enhance their model and make it more accurate at simulating actual olfactory plumes. Additionally, the scientists want to mimic other flying insects’ physiological traits and capacities.
ANN agents can be reverse-engineered to gain a greater understanding of how they function in addition to enabling significant technological breakthroughs, which may, in turn, influence neuroscience research. Thus, neuroscientists may also employ this model to investigate the molecular mechanisms underlying olfactory plume tracking.
This research is an example of how artificial intelligence can generate novel scientific discoveries and help us better understand the extraordinary abilities of animals. It is also an important step towards the development of autonomous agents that can track odors for future use in environmental monitoring, search-and-rescue missions, and other applications.
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