Animal Sensing in Robotics


The researchers at North-western University’s Robotics and Biosystem come up with a new theory that can predict the motion of animals using sense to search. This new theory will help improve the performance of robots for collecting information.

This theory has been termed as the energy-constrained proportional betting, which means the probability of moving to a particular location is proportional to expected information on how it will balance against the movements’ predicted energetic cost. According to this theory, small and seemingly extraneous movements that animals or sensory organs undergo can be predicted as they come closer or track a target of interest.  

The researcher has experimented with this theory with three different sensings including smell, electro-sense, and vision on four different species, so as to learn about the behavioral changes. Usually, it is already discovered the animals especially insects reply on moving their organs when they encounter any uncertain situation or when they search for food. But this theory will shed light on the amount of energy it requires for such a movement. This theory combines the informatics module and the metabolic cost of movement.

This theory has initially experimented on the electro-sensing potential of South American Gymntoid electric fish. Malcolm A Maclver is a professor at Northwestern’s McCormick School of Engineering.  According to him, animals make their living through motion, they identify motions to find food, to mate, and sense threat. This theory gives an insight into how animals’ risks on how much energy they require to expend to get the useful information they need.

When we look at a cat’s ear, it twirls; it’s a way of positioning their sensory organs to help them absorb information from their surroundings. This is an example of how animals use their sensory organs in situations when needed. The algorithm used in the theory provides us with insight that animals trade the energetically expensive operation for their motions to gamble the location in space. While most of the theory is based on how animals behave when it already knows where something is, this theory predicts for when the animals know very little.


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