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Egospace Motion Planning Representations for Micro Air Vehicles

Citation

Fragoso, Anthony T. (2018) Egospace Motion Planning Representations for Micro Air Vehicles. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9GX48RJ. http://resolver.caltech.edu/CaltechTHESIS:10242017-193520989

Abstract

Navigation of micro air vehicles (MAVs) in unknown environments is a complex sensing and trajectory generation task, particularly at high velocities. In this work, we introduce an efficient sense-and-avoid pipeline that compactly represents range measurements from multiple sensors, trajectory generation, and motion planning in a 2.5–dimensional projective data structure called an egospace representation. Egospace coordinates generalize depth image obstacle representations and are a particularly convenient choice for configuration flat mobile robots, which are differentially flat in their configuration variables and include a number of commonly used MAV plant models. After characterizing egospace obstacle avoidance for robots with trivial dynamics and establishing limits on applicability and performance, we generalize to motion planning over full configuration flat dynamics using motion primitives expressed directly in egospace coordinates. In comparison to approaches based on world coordinates, egospace uses the natural sensor geometry to combine the benefits of a multi-resolution and multi-sensor representation architecture into a single simple and efficient layer. We also present an experimental implementation, based on perception with stereo vision and an egocylinder obstacle representation, that demonstrates the specialization of our theoretical results to particular mission scenarios. The natural pixel parameterization of the egocylinder is used to quickly identify dynamically feasible maneuvers onto radial paths, expressed directly in egocylinder coordinates, that enable finely detailed planning at extreme ranges within milliseconds. We have implemented our obstacle avoidance pipeline with an Asctec Pelican quadcopter, and demonstrate the efficiency of our approach experimentally with a set of challenging field scenarios. The scalability potential of our system is discussed in terms of sensor horizon, actuation, and computational limitations and the speed limits that each imposes, and its generality to more challenging environments with multiple moving obstacles is developed as an immediate extension to the static framework.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Micro air vehicles; Motion planning; Computer vision; Egospace
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Aeronautics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Murray, Richard M.
Thesis Committee:
  • McKeon, Beverley J. (chair)
  • Chung, Soon-Jo
  • Murray, Richard M.
  • Matthies, Larry H.
Defense Date:11 October 2017
Funders:
Funding AgencyGrant Number
Army Research LaboratoryUNSPECIFIED
Record Number:CaltechTHESIS:10242017-193520989
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:10242017-193520989
DOI:10.7907/Z9GX48RJ
Related URLs:
URLURL TypeDescription
https://doi.org/10.23919/ACC.2017.7963600DOIAdapted in part for Ch. 2 and 3.
http://www.fsr.ethz.ch/papers/FSR_2017_paper_58.pdfRelated DocumentAdapted in part for Ch. 4.
http://doi.org/10.1007/978-3-319-50115-4_44DOIAdapted in part for Ch. 2 and 4.
ORCID:
AuthorORCID
Fragoso, Anthony T.0000-0002-5805-9668
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10543
Collection:CaltechTHESIS
Deposited By: Anthony Fragoso
Deposited On:30 Oct 2017 22:35
Last Modified:09 Nov 2017 17:30

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