Funding

Our research would not have been possible without the funding provided by a broad range of grants and industry collaborations. We are deeply thankful to all the entities listed below. Current supports include:

Previous supports include:

Current Projects

Habitats Optimized for Missions of Exploration (HOME)

The scope of the HOME Space Technology Research Institute (STRI)’s research will be driven by two primary operational requirements of NASA’s deep-space habitats: (1) Keep humans alive while they are resident, and (2) Keep the vehicle alive while they are not. A highly autonomous deep-space habitat for human crews requires three major classes of control: autonomy, robotics, and humans – the interactions and interdependencies between these three domains comprise the research landscape for the HOME STRI. The goal of the STRI is to develop validated technology to help NASA ensure safe human habitability during crewed deep-space missions, and to maintain the habitat to support future crews even while uninhabited.The HOME team is led by Stephen Robinson, principal investigator at UC Davis, in partnership with the University of Colorado Boulder, Carnegie Mellon University, the Georgia Institute of Technology, Howard University, Texas A&M University and the University of Southern California. Industry collaborators include Sierra Nevada Corporation, Blue Origin and Collins Aerospace Systems. Our lab will contribute to the development of early-stage technologies related to explainable AI as well as the understanding of trust in the context of human-autonomy integration.

XCPS: Transparent and Explainable Cyber-Physical Systems

Greater complexity in (semi-)autonomous systems is a double-edged sword. More sophisticated automation may actually increase, rather than decrease the difficulty for a human operator to comprehend the system’s behaviors. The problem may be exacerbated when the operator is denied a direct observation of the internal functioning of the system, thus having to surmise from sensor data what may be occurring. This can be a daunting task, particularly when some sensors are susceptible to failure. Many such systems, e.g., driverless cars, nuclear power plants, and spacecraft, are safety critical: a small error may lead to unintended, high-regret consequences. The goal of this project is to enable safety-critical (semi-)autonomous systems to inspect their own observations and consequently explain themselves in a language understandable by humans. This project is partially funded by NASA.

Timed Failure Propagation Graph (TFPG) generated automatically from data.

Cognitive-Aware Human-Autonomy Teaming

In the foreseeable future, humans will continue to play crucial roles in many, if not all, safety-critical missions, such as disaster relief, space exploration, and robot-assisted surgery. Intelligent machines (i.e., fully and semi-autonomous systems) should effectively function with their human teammates in a cognitively compatible manner. The goal of this project is to enable an intelligent machine to infer and predict its human teammate’s cognitive states (e.g., cognitive load, intention, and trust) and act accordingly (e.g., providing meaningful feedback to its human teammate) to guarantee performance of the collective human-machine team. This is part of a collaborated work with Dr. Sanjay Joshi at UC Davis and Dr.s Allie Anderson and Torin Clark at CU Boulder. The project is partially funded by NASA.

This video shows one set of data we have collected from a human subject while he was remotely controlling a group of five robots in a triangular formulation to acquire a sequence of targets randomly projected on the ground. The video on the left was recorded by an overhead camera while the one on the right was recorded by a pair of gaze tracking glasses. The subject's EEG activities were also recorded (not shown here).

Safety-Assured Apprenticeship Learning for Vision-Based Control of UAS in Riverine Environments

Many future military (e.g., scouting and patrolling) and civilian (e.g., search-and-rescue) missions require unmanned aerial systems (UAS) that are capable of flying inside complex and possibly GPS-denied environments (e.g., a forest) safely and effectively. The goal of this project is to tackle one technical challenge hampering the future development and deployment of such UAS: how to guarantee the safety of the UAS, e.g., avoiding collision with nearby obstacles, while maintaining its performance. A natural starting point is imitation learning, which allows an agent to learn what actions to take in an environment from the demonstrations of a human expert. But in a traditional imitation learning setting, it is possible, despite the fact that all expert demonstrations are technically safe, that the agent may generate an unsafe control policy. We aim to integrate imitation learning and formal methods at the fundamental level to develop safety-assured learning strategies. The project is funded by the NEPTUNE 2.0 Program of the ONR.

Smart Energy Management for Unmanned Aerial System Operation in Complex Military Missions

In recent years, unmanned aerial  systems (UAS) and other unmanned systems (UxS) have found their way onto the battleground to extend the reach of military forces, providing essential intelligence, surveillance and reconnaissance (ISR) as well as payload delivery and recovery. The increasing capability of such systems, particularly small and light-weight UxS swarms, has offered the Navy, the Marine Corps, and other military branches a significant opportunity to potentially reduce cost, increase system resiliency, and enhance operation effectiveness. However, one fundamental challenge impeding the wide deployment of small-scale UxS and UxS swarms is energy efficiency particularly in the context of complex military missions. Key performance indexes (KPI) of a mission, such as runtime, payload, and survivability, are often severely limited by the energy efficiency of the system. This project aims to tackle this challenge by investigating the fundamental physics governing the UAS energy performance and improving the mission KPIs through energy-oriented planning, control, and swarm collaboration.This is a collaborated work with Dr. Xinfan Lin. The project is funded by the NEPTUNE 1.0 Program of the ONR.

This video shows our outdoor experiment procedure with which we were able to collect data sufficient to model the effects of wind on the UAV dynamics.

Mitigating Ecological Destructive Events with a Network of UAVs

Our world is plagued with large-scale ecological disasters. Tragic examples include the Fukushima I Nuclear Power Plant calamity and the Deepwater Horizon oil spill. Today these disasters may be partially mitigated by teams of humans working in hazardous and labor intensive environments. To combat these destructive environmental processes, this project instead envisions the development of a network of UAVs that can combat these disasters, while keeping humans at a safe distance. We will use precision agriculture as a test bed to develop a multi-UAV system that will 1) use cameras mounted on UAVs to map out distribution of pest-infested plants in a heterogeneous grid pattern of crop plants with/without pest infestations, 2) transfer the map from the imaging UAV to a ground station (or a fixed wing aircraft) to compute an optimal mitigation solution, and 3) deploy actuating UAVs to implement the solution (release of natural enemies to the pest population on crops) to the plants with symptoms of pest infestation.

This is part of a collaborated work with Dr. Christian Nansen from the Department of Entomology and Nematology and Dr. Mohsen B Mesgaran from the Department of Plant Sciences. The project is partially funded by USDA and CDPR. Our project has been featured by two local news stations: ABC10 and CBS13, as well as ASME.

The video one the left shows experimental dispersion of vermiculite mixed with predatory mites. We are in the process of developing algorithms and control systems of the dispensing device so that we can precision-target the release of natural enemies by taking into account drone altitude and speed and account for wind speed and wind direction. The video on the right features one of our preliminary test runs to explore the idea of integrating drone, computer vision, and AI to identify weeds.

Exploring the Possibility of Using UAS with Low-Cost Sensors for Air Quality Monitoring

The goal of the project is to explore the possibility of using unmanned aerial systems with low-cost sensors to measure and subsequently study air quality. We have successfully conducted out first test (see video below) using a DJI S1000 Octocopter equipped with a low-cost air sensor package (BME280 and PMS7003) to measure the early-morning vertical stratification of the atmosphere at the Russell Sports Field of the UC Davis. Working closely with researchers from the UC Davis Air Quality Research Center, we are currently working on updating our UAS, including both its hardware and software, as well as the sensor package to accelerate forest management and support firefighting and post-fire rehabilitation in forests and rangelands.

Data-Driven Personalized Training of Next-Generation Workforce

Humans, in the role of either customers, designers, program managers, or workers, will always play a significant role in future manufacturing. However, the systems that the humans are interacting with are becoming increasingly complicated especially with the rise of large-scale industrial cyber-physical systems (CPSs), also called Industrial Internet of Things (IIoT). This project is aimed at developing a data-driven method of modeling and analyzing manual expertise of humans while they are interacting with CPSs as well as traditional machines. The insights we are going to gain from this project can not only help us to build machines and CPSs that can collaborate with humans more efficiently and effectively but also help us to facilitate the knowledge transfer among generations. This is a collaborated work with Dr. Barbara Linke.

The video on the left shows the gaze data of an expert performing a manual grinding task; while the video on the right shows the gaze data of a novice performing the same task. If you are a practitioner and have any insight you would like to share with us, please don't hesitate to contact either Zhaodan Kong or Gregory Bales. We would love to share with you the force data, the gaze data, and the tool pose data that we collected.