Below is an overview of some of the projects we are working on in the NIMBUS lab.  We are working on projects in the following areas:

  • Safe, precise and repeatable maneuvers
  • Failure detection and recovery
  • Extended flying autonomy
  • Adaptive sensing
  • Teaming and coordination
  • Capturing and synthesizing user expertise
  • Automated extraction of system specifications
  • Mission planning and analysis
  • Underwater robots and sensor networks
  • Reducing Failure Rates of Robotic System through Inferred Invariants Monitoring
  • Improving cyber-physical resource allocation
  • Human-robot interactions
  • Control and path planning in novel applications

Leveraging Environmental Monitoring UAS in Rainforests

Rainforest canopies are important ecosystems for diverse plant and animal life, however validating the model-based predictions for scientific decisions about these environments is difficult due to a lack of efficient data collection methods. Access is limited due to remoteness, dense foliage, and venomous wildlife, which constrain research to maintained trails and vegetation near the forest floor. Currently, most data is collected within 50 meters of trails and 5 meters from the ground surface due to these limitations, making spatially explicit measurements sparse at best. Unmanned Aerial Systems (UASs) have been used for sensor deployment and monitoring, but only recently has the ability to collect soil samples at precise locations in the ground been developed by the project team. Further development of the system will aid in obtaining soil, water, and leaf samples from coordinated sites to ensure coverage, enabling spatially distributed data collection in areas where it is currently too costly or dangerous.

Leaf Capture drone

Flying Digger

UAS Digging and In-Ground Sensor Emplacement

Using unmanned aircraft systems (UASs) as sensor platforms is a well-studied and well-implemented area.  UASs have even been used to place sensors on the ground or in water.  However, there are some instances where placing a sensor on top of the ground is not sufficient.  Placing a sensor into the ground is necessary for things like accurate seismic measurements or soil moisture content measurements.  We have developed a system that is capable of drilling a sensor into the ground via an auger mechanism and the weight of the UAS.

UAS Prescribed Fire Ignition

Prescribed fires are increasingly being used to combat wildfires and to improve the health of ecosystems by combating invasive species. Yet, the tools available for fire ignition (e.g., hand-tools, chainsaws, drip torches, and flare launchers), are antiquated, placing ground crews at great risk. The use of helicopter-based ignition, a high-risk activity, has helped in scale but the expense makes it inaccessible to most users. We have developed an Unmanned Aerial System for Firefighting (UAS-FF) to enhance fire ignition capabilities, while significantly reducing the risk to the firefighters. The UAS-FF precisely drops delayed ignition spheres and can be used to ignite fire lines in areas that are otherwise too difficult or dangerous to access with traditional methods. We have worked the FAA and fire agencies and are currently in the process of performing the first set of field experiments where the UAS-FF ignites fires as part of larger prescribed burns.

Fire-starting drone

Co-Aerial-Ecologist: Robotic Water Sampling and Sensing in the Wild

The goal of this research is to develop an aerial water sampling system that can be quickly and safely deployed to reach varying and hard to access locations, that integrates with existing static sensors, and that is adaptable to a wide range of scientific goals. The capability to obtain quick samples over large areas will lead to a dramatic increase in the understanding of critical natural resources. This research will enable better interactions between non-expert operators and robots by using semi-autonomous systems to detect faults and unknown situations to ensure safety of the operators and environment.

This research is partially funded as part of the National Robotics Initiative through  the USDA National Institute of Food and Agriculture (grant #2013-67021-20947).

Predictable Run-time Monitoring

We are working on a framework that can provide predictable run-time monitoring for UAV applications. Simply put, predictability in run-time monitoring requires that 1) detection latency is bounded from above and 2) resource for run-time monitoring is controlled. Previously, we developed the theory to do this, however, now we are working on applying this to our UAVs that are running ROS. This is particularly challenging since, as stated on the ROS website, ROS is not designed for real-time applications. Therefore, we are adapting our theoretical work to this real system to still guarantee real-time performance for ROS programs.

For formal proof of schedulability, we need to obtain some task parameters, such as periods (or minimum inter-arrival times), WCET (Worst-Case Execution Time). Unfortunately, this seemingly easy job is not easy. Due to cache effects and I/O operations, the WCET of a task could vary within a large range. If we simply use the measured maximum value of execution time as the WCET, chances are that it is so pessimistic that the system cannot pass the formal schedulability test (but it is still running well in practice!) In some real-time systems, people disable cache and limit I/O operations to get predictable timing info, but we want to avoid doing this as it reduces performance in practice.

This project is sponsored, in part, by NASA.

Flying drone figure

Flying Digger

Adaptive and Autonomous Energy Management on a Sensor Network Using Aerial Robots

This research introduces novel recharging systems and algorithms to supplement existing systems and lead to autonomous, sustainable energy management on sensor networks. The purpose of this project is to develop a wireless power transfer system that enables unmanned areal vehicles (UAVs) to provide power to, and recharge batteries of wireless sensors and other electronics far removed from the electric grid. We do this using wireless power transfer through the use of strongly coupled resonances. We have designed and built a custom power transfer and receiving system that is optimized for use on UAVs.  We are investigating systems and control algorithms to optimize the power transfer from the UAV to the remote sensor node.  In addition, we are investigating energy usage algorithms to optimize the use of the power in networks of sensors that are able to be recharged wirelessly from UAVs.

Applications include powering sensors in remote locations without access to grid or solar energy, such as: underwater sensors that surface intermittently to send data and recharge, underground sensors, sensors placed under bridges for structural monitoring, sensors that are only activated when the UAV is present, and sensors in locations where security or aesthetic concerns prevent mounting solar panels.  See the project webpage for more details.  This work is partially supported by the National Science Foundation.

Flying Digger

Crop Surveying Using Aerial Robots

Surveying crop heights during a growing season provides important feedback on the crop health and its reaction to environmental stimuli, such as rain or fertilizer treatments.  Gathering this data at high spatio-temporal resolution poses significant challenges to both researchers conducting phenotyping trials, and commercial agriculture producers. Currently, crop height information is gathered via manual measurements with a tape measure, or mechanical methods such as a tractor driving through the field with an ultrasonic or mechanical height estimation tool. These measurement processes are time consuming, and frequently damage the crops and field. As such, even though crop height information can be extremely valuable throughout the growing season, it is typically only collected at the end of growing season.

ROS Glass Tools

Robot systems frequently have a human operator in the loop directing it at a high level and monitoring for unexpected conditions.  In this project we aim to provide an open source toolset to provide an interface between the Robot Operating System (ROS) and the Google Glass.  The Glass acts as a heads up display so that an operator monitoring vehicle state can simultaneously view its actions in the real world.  In addition to monitoring the project aims to harness the voice recognition of the Glass to allow robot voice control.  The project also aims to be easily extensible so it can be used to monitor and control a multitude of robot systems using the Glass.  More information on the tools can be found at