Chris is a graduate student in the Aeronautics & Astronautics Department. He is broadly interested in guidance, navigation, and perception. His research vision is to couple the flexibility of learning-based methods of state estimation with the robustness and provability of convex optimization-based methods of guidance for safety-critical applications (e.g. Powered-Descent Guidance, Human-Robot Interactions). His specific areas of interest are:
Integrating contextual information into computer vision methods: Using non-vision-based sensors and data to aid computer vision methods to enable agents to make informed decisions in dynamic environments.
Advancing multi-modal sensor fusion methods: Introducing computer vision methods to efficiently use multiple modalities to ensure robustness in uncertain and adverse environments.
Perception constraints for real-time optimization-based trajectory planning: Formulating convex constraints to optimally use visual-based sensors in performing real-time environment-aware trajectory planning for autonomous agents.
Chris is currently a NASA Space Technology Graduate Research Opportunities (NSTRGO) fellow.
Publications
WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue
Broyles, D.*,
Hayner, C.*,
and Leung, K.
In IEEE/RSJ Int. Conf. on Intelligent Robots & Systems,
2022
Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and evacuating person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal imagers, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, more than 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications.Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and evacuating person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal imagers, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, more than 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications.
@inproceedings{BroylesHaynerEtAl2022,author={Broyles, D.* and Hayner, C.* and Leung, K.},booktitle={{IEEE/RSJ Int.\ Conf.\ on Intelligent Robots \& Systems}},title={{WiSARD}: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue},year={2022},arxiv={2309.04453},category={afsl},img={BroylesHaynerEtAl2022.jpg},html={https://sites.google.com/uw.edu/wisard/},owner={karenl7}}
HALO: Hazard-Aware Landing Optimization for Autonomous Systems
Hayner, C. R.,
Buckner, S. C.,
Broyles, D.,
Madewell, E.,
Leung, K.,
and Açıkmeşe, B.
In Proc. IEEE Conf. on Robotics and Automation,
2023
With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover’s entry, descent, and landing on Mars, awareness and reasoning regarding potentially hazardous landing sites is paramount. This paper presents a coupled perception-planning solution which address the real-time hazard detection, optimal landing trajectory generation, and contingency planning challenges encountered when landing in uncertain environments. The perception and planning components are addressed by the proposed Hazard-Aware Landing Site Selection (HALSS) framework and Adaptive Deferred-Decision Trajectory Optimization (Adaptive-DDTO) algorithm respectively. The HALSS framework processes point clouds through a segmentation network to predict a binary safety map which is analyzed using the medial axis transform to efficiently identify circular, safe landing zones. The Adaptive-DDTO algorithm address the need for contingency planning during target failure scenarios through adaptively recomputed multi-target trajectory optimization. Overall, Adaptive-DDTO achieves 18.16% increase in terms of landing success rate and 0.4% decrease in cumulative control effort compared to its predecessor, DDTO, while computing near real-time solutions, when coupled with HALSS, in a simulated environment.
@inproceedings{HaynerBucknerEtAl2023,author={Hayner, C. R. and Buckner, S. C. and Broyles, D. and Madewell, E. and Leung, K. and A\c{c}{\i}kme\c{s}e, B.},booktitle={{Proc.\ IEEE Conf.\ on Robotics and Automation}},title={{HALO}: Hazard-Aware Landing Optimization for Autonomous Systems},year={2023},arxiv={2304.01583},category={afsl},img={HaynerBucknerEtAl2023.png},owner={karenl7}}
Active View Planning with Guaranteed Keypoint Coverage
Hayner, C. R.,
Pavlasek, N.,
Elango, P.,
Tiwary, A.,
Chung, B.,
Leung, K.,
and Açıkmeşe, B.
@inproceedings{HaynerPavlasekEtAl2024,author={Hayner, C.~R. and Pavlasek, N. and Elango, P. and Tiwary, A. and Chung, B. and Leung, K. and A\c{c}{\i}kme\c{s}e, B.},title={Active View Planning with Guaranteed Keypoint Coverage},year={2024},img={HaynerPavlasekEtAl2024.png},note={(preprint)},keywords={preprint},owner={karenl7}}
