The flight navigation strategy of moths can help drones to navigate unfamiliar and complex environments, say researchers who used real insect flight trajectories to develop decision-making programme for autonomous vehicles.
The researchers from University of Washington in Seattle and Boston University said that by using real data from animal flight paths, they can programme bio-inspired drones that will be able to navigate autonomously in cluttered space.
To understand how real moths plan their route, the researchers mounted 8 hawk moths on metal rods connected to a torque meter.
In front of each moth, they projected a moving forest scene created from beams of light for the moth to navigate.
The flight navigation strategy of moths can help drones to navigate unfamiliar and complex environments. Pixabay
They captured data from the moth flight and built a mathematical model to describe the moth trajectory through the virtual forest.
The flight data were translated into a decision-making programme that could be used to control a drone, said Loannis Paschalidis from Boston University and Thomas Daniel at University of Washington in the open-access journal PLOS Computational Biology.
They compared how the drone and the moth performed in simulations of the same forest layout, as well as new configurations with different densities of trees.
The researchers found that hawk moths mainly rely on the pattern created by the apparent motion of objects caused by their flight.
However, the flight programmes optimized for drones performed 60 per cent better in the simulated forest "because they also incorporated information about the exact location of objects in their surroundings into their navigational decisions".
Although the researchers were able to optimize the strategy used by moths to improve performance in certain environments, the moths' strategy was more adaptable, performing well in a variety of different forest layouts.
"The moth model performed best in dense forests, suggesting that hawk moths have evolved a flight strategy adapted to the thick forests they often encounter," the authors wrote. (IANS)