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With their exceptionally maneuverable flapping flight, bats could serve as a model for enhancing the flight abilities for future drones. However, bat flight is extremely complex and there are many engineering restrictions that prevent a flapping-flight drone from replicating the many degrees of freedoms present in biology. Hence, to make design choices of which properties in a bats wing kinematics should be reproduced, the present research has evaluated two metrics from information and complexity theory to identify which regions of the bat flight apparatus are most complex and where coupling across features of the bat flight kinematics exists. The values were the complexity metric as a measure of variability and mutual information as a measure of coupling. Both measures were applied to ten experimentally obtained digital models of the flight kinematics in Ridley's leaf-nosed bats as well as the simulated kinematics of a flapping-flight drone inspired by the same bat type. The pilot results obtained indicate that both measures could be useful to discover which elements of flight kinematics should be looked into for understanding and reproducing the maneuvering flight of bats. However, a functional interpretation will require complementary, e.g., aerodynamic metrics. / Master of Science / Bats have incredible capabilities to execute flight maneuvers and navigate cluttered natural environments. They have evolved to hunt and evade predators in dense vegetation, which makes them suitable as a model for future aerial drones which will need to perform well in both man-made and natural environments. However, creating a drone with the flight abilities of a bat has many challenges due to current engineering and technology limitations. To accomplish this goal, the key features of bat flight must be examined in detail and decisions must be made on what aspects are most important to replicate in a bio inspired drone. Algorithms from the areas of information and complexity theory were applied to gain greater insight into the complex flights of bats. Ten digital models of Ridley's leaf-nosed bats generated from video recordings were analyzed as well as the simulated motion of a flapping-flight drone inspired by the same bat type. The pilot results showed that these measures could provide insight into replicating the flight of bats, but more flight sequences need to be analyzed, and the digital models of the bats will continue to be refined.

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