At the Water’s Edge: Installation and Optimization of Robotic Sensing Systems

This project is funded the USDA through grant USDA-NIFA2017-67021-25924


Wetlands are critical to the water cycle, yet they are a difficult environment to safely and effectively monitor. Robot systems have the potential to transform our understanding of complexwetlands systems by not only allowing faster and higher density sensing, but also by enabling new types of measurements and sample collections that cannot currently be performed without significantly disrupting these sensitive systems. This project proposes expanding current unmanned aerial vehicle (UAV) systems, developing novel water monitoring systems, and designing algorithms in order to enable: (1) accurate measurement of the dynamic wetlands channels, including topography and flow, without prior knowledge, (2) adaptive and autonomous installation of static and limited-mobility sensors, and (3) optimization of the overall robot and sensor system to improve information gain while constrained by limited energy and communications.

Goals and Objectives

This proposal will develop solutions that move systems closer to autonomous and adaptive monitoring of wetlands and other environments where sensing is needed at the land/water boundaries. To achieve this, we will:

    1. Design and implement UAV-based systems that measure wetlands channels including channel depth, topography, bathymetry, and water chemistry. Systems will focus on threechallenges: sensor fusion for improved topography, land and water differentiation, and bathymetric models of identified water regions.
    2. Develop algorithms and approaches to ensure sensing repeatability independent of environmental conditions.
    3. Design and implement algorithms to verify sensor node installation via UAV onboard sensors as UAV installs sensor node.
    4. Develop co-optimization planning schemes for single-vehicle multi-flight missions.
    5. Verify the systems and algorithms with our environmental engineers,incrementally incorporating the technical advances and assessing the capabilities of the systems through field studies conducted in wetlands in Nebraska and California.

  1. Educate students, scientists, and the public on the use, challenges, and need for robotics in wetlands systems through courses, workshops, and presentations.


  • Faculty
    • Carrick Detweiler (UNL PI) (Computer Science and Engineering
    • Justin Bradley (UNL Co-PI) (Computer Science and Engineering)
    • Elizabeth Basha (UP PI) (Engineering and Computer Science)
  • Students
    • Xinkai Zhang (PhD candidate) (Electrical and Computer Engineering)
    • Chandima Fernando (PhD precandidate) (Computer Science and Engineering)
    • Najeeb Najeeb (PhD candidate) (Computer Science and Engineering)
    • Adam Plowcha (PhD precandidate) (Computer Science and Engineering)
    • Pedro Albuquerque (MS) (Computer Science and Engineering)
    • Derek Eells
    • Cameron Franke
    • Andrew Garrett
    • Curtis Klein
    • Thomas Medeiros
    • Connor Morales
    • Tristan Watts-Willis


  1. S. Doebbeling, X. Zhang and J. Bradley. Co-Regulation of Computational and Physical Effectors in a Quadrotor UAS. Submitted to IEEE International Conference on Robotics and Automation (ICRA), 2018.
  2. K. Song, A. Brewer, S. Ahmadian, A. Shankar, C. Detweiler, and A. Burgin. Using Unmanned Aerial Vehicles (UAVs) to sample aquatic ecosystems. Limnology & Oceanography: Methods, accepted to appear, 2017.
  3. E. Basha, T. Watts-Willis, and C. Detweiler. Autonomous Meta-Classifier for Surface Hardness Classification from UAV Landings. In Proceedings of IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS), Vancouver, Canada, 2017.
  4. S. Doebbeling, J. Bradley. Toward a Cyber-Physical Quadrotor: Characterizing Trajectory Following Performanc. International Conference on Unmanned Air Systems (ICUAS), 2017
  5. N. Najeeb and C. Detweiler. Extending Wireless Rechargeable Sensor Network Life without Full Knowledge. Sensors. 2017; 17(7):1642.
  6. J.-P. Ore, C. Detweiler, and S. Elbaum. Dimensional Inconsistencies in Code and ROS Messages: a Study of 5.9M Lines of Code. In Proceedings of IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS), Vancouver, Canada, 2017.
  7. J.-P. Ore, and C. Detweiler. Sensing Water Properties at Precise Depths from the Air. In Proceedings of International Conference on Field and Service Robotics, Zurich, Switzerland, 2017.
  8. J.-P. Ore, C. Detweiler, and S. Elbaum. Lightweight Detection of Physical Unit Inconsistencies without Program Annotations. In Proceedings of the 2017 International Symposium on Software Testing and Analysis (ISSTA), Santa Barbara, CA, 2017
  9. D. Anthony, and D. Detweiler. (2017), UAV Localization in Row Crops. J. Field Robotics, 34:1275-1296. doi:10.1002/rob.21706