Autonomous exploration in unknown urban environments for unmanned aerial vehicles

David Hyunchul Shim, Hoam Chung, H. Jin Kim, Shankar Sastry

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

89 Citations (Scopus)


In this paper, we present an autonomous exploration method for unmanned aerial vehicles in unknown urban environment. We address two major aspects of exploration-gathering information about the surroundings and avoiding obstacles in the flight path- by building local obstacle maps and solving for conflict-free trajectory using model predictive control (MPC) framework. For obstacle sensing, an onboard laser scanner is used to detect nearby objects around the vehicle. An MPC algorithm with a cost function that penalizes the proximity to the nearest obstacle replans the flight path in real-time. The adjusted trajectory is sent to the position tracking layer in the UAV avionics. The proposed approach is implemented on Berkeley rotorcraft UAVs and successfully tested in a series of flights in urban obstacle setup.

Original languageEnglish
Title of host publicationCollection of Technical Papers - AIAA Guidance, Navigation, and Control Conference 2005
PublisherAmerican Institute of Aeronautics and Astronautics
Number of pages8
ISBN (Print)1563477378, 9781563477379
Publication statusPublished - 1 Jan 2005
Externally publishedYes
EventAIAA Guidance, Navigation, and Control Conference 2005 - San Francisco, United States of America
Duration: 15 Aug 200518 Aug 2005


ConferenceAIAA Guidance, Navigation, and Control Conference 2005
Country/TerritoryUnited States of America
CitySan Francisco

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