UAV Assisted USV Visual Navigation for Marine Mass Casualty Incident Response Conference Paper uri icon

abstract

  • 2017 IEEE. This research teams an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment and automate marine mass casualty incident search and rescue in emergency response phase. The demand for real-time responsiveness of those missions requires fast and comprehensive situational awareness and precise operations, which are challenging to achieve because of the large area and the flat nature of the water field. The responders, drowning victims, and rescue vehicle are far apart and all located at the sea level. The long distances mean responders cannot clearly discern the rescue vehicle and victims from the surrounding water. Furthermore, being at the same elevation makes depth perception difficult. Rescue vehicle and victims at different distances from the responder will always appear to be close together. This makes it almost impossible for the responders to accurately drive the USV to the victims in time. This paper proposes the use of a UAV to compensate for the lack of elevation of the responders and to automate search and rescue operations. The benefit of this system is two fold: 1) the UAV provides responders with an overhead view of the field, covers larger area than direct visual, and allows more accurate perception of the situation, and 2) it automates the rescue process so that the responders can focus on task-level needs instead of tediously driving the USV to the victims. Thirty autonomous navigation trials in 4 rescue scenarios prove the first known successful implementation of a small UAV visually navigating a USV.

name of conference

  • 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

published proceedings

  • 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

author list (cited authors)

  • Xiao, X., Dufek, J., Woodbury, T., & Murphy, R.

citation count

  • 58

complete list of authors

  • Xiao, Xuesu||Dufek, Jan||Woodbury, Tim||Murphy, Robin

publication date

  • January 2017