The theory proposes a framework, where more time and energy can be preserved as robots and autonomous vehicle move to collect information.

The researchers at Northwestern University's Robotics and Biosystem have formulated a new theory which predicts the movement of animals using sensing to search. The research paper titled, "Tuning movement for Sensing in an uncertain world" is published in the journal eLife. Researchers cite that this theory can be applied to improve the performance of robots for collecting information and autonomous vehicles.

The newly proposed theory is termed as the energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movements predicted energetic cost. This theory, predicts the small and seemingly extraneous movements that sensory organs or animals undergo as they near or track a target of interest.

The researchers state that the underlying sensory sampling strategy gambles on the chance of obtaining more information at a given location through carefully controlled sensor motion that balances two factors that typically pushes in the opposite directions. These two factors are proportionally bet on the expected information gain and minimizing the energy expended for motion.

The researchers have applied this theory on four different species and have used three different senses including electro sense, vision and smell, so that the behavioural change in sensing can be analyzed and demonstrated. Usually, the animals, especially insects rely on moving their organs while encountered with an uncertain situation or while searching food. But researchers believe that this new theory will shed some light on the amount of energy required for such movements. The proposed theory combines the metabolic cost of motion with informatics module.

Malcolm A Maclver, who spearheaded the research, is a professor of biomedical and mechanical engineering at Northwestern's McCormick School of Engineering. He states, "Animals make their living through movement. To find food and mates and to identify threats, they need to move. Our theory provides insight into how animals gamble on how much energy to expend to get the useful information they need."

The theory provides a unified solution to the problem of not spending too much time and energy moving around to sample information while getting enough information to guide movement during tracking and related exploratory behaviours. It also addresses the challenges of the most existing theories where the complete control framework in animal sensing remains underspecified.

The experiment is initially done on the electro-sensing capability of South American Gymntoid electric fish. Researchers have also analyzed the datasets of previous experiments on blind eastern American mole, American Cockroach and hummingbird.

Todd D Murphey, the co-author of the new theory states, "While most theories predict how an animal will behave when it largely already knows where something is, ours is a prediction for when the animal knows very little — a situation common in life and critical to survival. "

The algorithm used in the model shows that animal trade the energetically costly operation of movement to gamble the locations in space.

Malcolm adds, "When you look at a cat's ears, you'll often see them swiveling to sample different locations of space. This is an example of how animals are constantly positioning their sensory organs to help them absorb information from the environment. It turns out a lot is going on below the surface in the movement of sense organs like ears, eyes and noses."

The new theory aims to deliver a model that preserves more time and energy as the robots and autonomous vehicle moves to collect information.