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Bioinspired engineering of exploration systems: A horizon sensor/attitude reference system based on the dragonfly ocelli for Mars exploration applications

dc.contributor.authorChahl, Javaan
dc.contributor.authorThakoor, S
dc.contributor.authorLe Bouffant, Naig
dc.contributor.authorStange, Gert
dc.contributor.authorSrinivasan, Mandyam V
dc.contributor.authorHine, B
dc.contributor.authorZornetzer, S
dc.date.accessioned2015-12-13T22:34:52Z
dc.date.available2015-12-13T22:34:52Z
dc.date.issued2003
dc.date.updated2015-12-11T09:24:30Z
dc.description.abstractBioinspired engineering of exploration systems (BEES) is a fast emerging new discipline. It focuses on distilling the principles found in successful, nature-tested mechanisms of specific crucial functions that are hard to accomplish by conventional methods, but are accomplished rather deftly in nature by biological organisms. The intent is not just to mimic operational mechanisms found in a specific biological organism but to imbibe the salient principles from a variety of diverse organisms for the desired crucial function. Thereby, we can build exploration systems that have specific capabilities endowed beyond nature, as they will possess a mix of the best nature-tested mechanisms for each particular function. Insects (for example, honey bees and dragonflies) cope remarkably well with their world, despite possessing a brain that carries less than 0.01% as many neurons as ours does. Although most insects have immobile eyes, fixed focus optics, and lack stereo vision, they use a number of ingenious strategies for perceiving their world in three dimensions and navigating successfully in it. We are distilling some of these insect-inspired strategies for utilizing optical cues to obtain unique solutions to navigation, hazard avoidance, altitude hold, stable flight, terrain following, and smooth deployment of payload. Such functionality can enable access to otherwise unreachable exploration sites for much sought-after data. A BEES approach to developing autonomous flight systems, particularly in small scale, can thus have a tremendous impact on autonomous airborne navigation of these biomorphic flyers particularly for planetary exploration missions, for example, to Mars which offer unique challenges due to its thin atmosphere, low gravity, and lack of magnetic field. Incorporating these success strategies of bioinspired navigation into biomorphic sensors such as the horizon sensor described herein fulfills for the first time the requirements of a variety of potential future Mars exploration applications described in this paper. Specifically we have obtained lightweight (∼6 g), low power (<40 mW), and robust autonomous horizon sensing for flight stabilization based on distilling the principles of the dragonfly ocelli. Such levels of miniaturization of navigation sensors are essential to enable biomorphic microflyers (<1 kg) that can be deployed in large numbers for distributed measurements. In this paper we present the first experimental test results of a biomorphic flyer platform with an embedded biomorphic ocellus (the dragonfly-inspired horizon sensor/attitude reference system). These results from the novel hardware implementation of a horizon sensor demonstrate the advantage of our approach in adapting principles proven successful in nature to accomplish navigation for Mars exploration.
dc.identifier.issn0741-2223
dc.identifier.urihttp://hdl.handle.net/1885/76321
dc.publisherWiley-VCH Verlag GMBH
dc.sourceJournal of Robotic Systems
dc.subjectKeywords: Closed loop control systems; Flight dynamics; Image sensors; Martian surface analysis; Navigation; Position control; Space applications; Stereo vision; Attitude reference system; Bioinspired engineering; Horizon sensor; Mars exploration; Systems engineeri
dc.titleBioinspired engineering of exploration systems: A horizon sensor/attitude reference system based on the dragonfly ocelli for Mars exploration applications
dc.typeJournal article
local.bibliographicCitation.lastpage42
local.bibliographicCitation.startpage35
local.contributor.affiliationChahl, Javaan, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationThakoor, S, California Institute of Technology Jet Propulsion Laboratory
local.contributor.affiliationLe Bouffant, Naig, California Institute of Technology Jet Propulsion Laboratory
local.contributor.affiliationStange, Gert, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationSrinivasan, Mandyam V, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationHine, B, NASA Ames Research Center
local.contributor.affiliationZornetzer, S, NASA Ames Research Center
local.contributor.authoruidChahl, Javaan, u3774890
local.contributor.authoruidStange, Gert, u7701206
local.contributor.authoruidSrinivasan, Mandyam V, u8513893
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080101 - Adaptive Agents and Intelligent Robotics
local.identifier.ariespublicationMigratedxPub5152
local.identifier.citationvolume20
local.identifier.doi10.1002/rob.10068
local.identifier.scopusID2-s2.0-0037232517
local.type.statusPublished Version

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