Event - Speakers

Stjepan Bogdan

University of Zagreb, Croatia

Unmanned Aerial System for Inspection and Maintenance of Industrial and Civil Structures

In this talk, we give an overview of the implementation of an unmanned aerial system in missions related to the inspection and maintenance of industrial and civil structures. We will present a hardware architecture of the unmanned aerial system, together with a brief introduction of underlying algorithms and software components. Examples include mapping of buildings in residential areas, an inspection of wind turbine blades, mapping of a scrap yard, and sensors mounting on a bridge.

Stjepan Bogdan received his PhDEE from the University of Zagreb, Croatia. Currently he is a Full Professor at the Laboratory for Robotics and Intelligent Control Systems (LARICS), Faculty of Electrical Engineering and Computing, University of Zagreb. His research interests include autonomous systems, aerial robotics, multi-agent systems, intelligent control, and discrete event systems. He is a co-author of 4 books and has published more than 200 conference and journal papers. He participated as the Principal Investigator and a researcher in 30 national and international scientific projects. He served as a Program Committee member and Associate Editor of major control and robotics conferences (ICRA, IROS, CDC, ECC, ACC, ECMR, CASE, ICUAS) and journals. He was Program Chair of IEEE International Symposium on Intelligent Control, ISIC2011, General Chair of IEEE Multi-conference on Systems and Control, MSC2012, General Chair of 2018 Mediterranean Conference on Control and Automation, and Honorary Chair of IEEE 2022 Int´l Conference on Unmanned Aircraft Systems (ICUAS2021). He was General Chair of IEEE 2021 AIRPHARO Workshop. He is a member of KoREMA, Croatian Academy of Technical Sciences (HATZ) and IEEE senior member. He is appointed as a member of IEEE Technical Committee on Intelligent Control, IEEE Technical Committee on Aerial Robotics, and was representative of Croatia at European Union Control Association (EUCA). He was representative of Croatia in H2020 Program committee on Space and was a vice-chair of Croatian Robotics Society. He is Program Committee member of Horizon Europe Cluster 4 – Digital, Industry and Space.

Antonella Barisic

University of Zagreb - UNM

Robust Aerial Detection of Drones In Real Environments

Unmanned aerial vehicles (UAVs), popularly known as drones, are pushing the boundaries of remote sensing, high-speed emergency response, infrastructure inspection, and many other areas. To ensure safe and efficient operations, we need to provide standardized rules and systems for monitoring airspace. In this talk, I will present research on UAV monitoring aimed at protecting specific areas of interest such as airports, hospitals, and public gatherings. I will talk about our findings in deploying robotic systems in the real world and how we address the imaging conditions of aerial detection under challenging illumination, blur, and in cluttered environments.

Antonella Barišić is a researcher and 3rd-year Ph.D. student at the LARICS Laboratory in the Department of Control and Computer Engineering, Faculty of Electrical Engineering and Computing at the University of Zagreb. She is also a Research Scientist for the Mobile Adaptive/Reactive Counter UAS System (MARCUS) proyect in the Department of Electrical and Computer Engineering at the Univerisity of New Mexico.

Nicola Bezzo

University of Virginia

Proactive, Resilient, and Assured Autonomous UAVs

Unmanned aerial vehicles (UAVs) are rapidly finding their way into our society with applications ranging inspection, aerial photography and delivery and logistics. While these systems are becoming increasingly more agile, a big challenge still remains on how to make them safer and resilient against unknown, unforeseen, untrained conditions at runtime. In this talk I will present our recent work on assured autonomy for resilient control and planning of aerial vehicles discussing our fast runtime monitoring and reachability analysis techniques together with demonstrations on real quadrotors. I will then conclude my talk with a discussion about current and future research directions and challenges on UAV motion planning and control.

Nicola Bezzo is an Assistant Professor in Systems Engineering and Electrical and Computer Engineering, University of Virginia (UVA), Charlottesville, VA, USA, where he also holds a courtesy appointment in Computer Science. He is one of the co-founders of the Link Lab and he directs the Autonomous Mobile Robots Lab, UVA. He received the B.S. and M.S. degrees (Hons.) (summa cum laude) in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2006 and 2008, respectively, and the Ph.D. degree in electrical and computer engineering from the University of New Mexico, Albuquerque, NM, USA, in 2012. Prior to joining UVA he was a postdoctoral fellow at the University of Pennsylvania, in the PRECISE Center. His research interests include resilient motion planning and control of autonomous mobile robots, assured autonomy, cyber physical system cyber-security, and heterogeneous robotic systems. Dr. Bezzo is the recipient of the 2010 Gold Medal from the Politecnico School of Engineering, the 2016 Robotics and Automation Magazine Best Paper Award, the Best Paper Award at the 2014 CPSWeek International Conference on Cyber-Physical Systems and the 2022 Amazon Faculty Research Award. His research is funded by DARPA, ONR, NSF, and industries like MITRE, Leidos, and CoStar.

Claus Danielson

University of New Mexico

Spacecraft Attitude Control using Invariant Sets

This talk will present an algorithm for spacecraft attitude motion planning. We will begin with an overview of the invariant-set motion-planning algorithm and its previous application to autonomous driving and spacecraft orbital motion planning. We will then present the adaption of the invariant-set motion-planner for quaternion-based spacecraft attitude motion planning. We will present a computationally efficient method for non-convex collision detection in SO(3). Furthermore, we will present a novel method for quasi-uniform sampling of the quaternion-space.

Dr. Claus Danielson joined the Department of Mechanical Engineering at the University of New Mexico as an Assistant Professor in August 2020. He received his Doctorate in 2014 from the Model Predictive Control Laboratory at the University of California, Berkeley. He received his masters and Bachelor of Science in mechanical engineering from Rensselaer Polytechnic Institute and the University of Washington, respectively. From 2014 to 2020, he was a Principal Research Scientist at Mitsubishi Electric Research Laboratories in Cambridge, MA. His research interests are in motion planning and constrained control. His specialty is developing methods for exploiting structure in large-scale or complex planning, control, and optimization problems. He has applied his research to a variety of fields include autonomous vehicles, robotics, spacecraft guidance and control, heating ventilation and air conditioning, energy storage networks, adaptive optics, atomic force microscopy, and cancer treatment.

John Ericksen

University of New Mexico

Project VolCAN: Studying Volcanoes with Aerial Robots

The VolCAN project is an interdisciplinary collaboration at UNM studying volcanic plumes using UAV swarms. In this talk, we discuss the current and future UAV flight algorithms that enable better predictions and understanding through gas concentration and flux surveys.

John Ericksen is a software developer with Honeywell Federal Manufacturing and Technologies and a computer science Ph.D. student at the University of New Mexico with the Moses Biological Computation Lab. Working with the earth and planetary sciences department, John's research focus is on autonomous airborne robot swarms used to sample volcanic C02 plumes. The goal of this is to link volcanic C02 output with volcanic behavior to better understand the precursors to life-threatening eruptions. John has also published on a variety of other research topics including software architecture, evolutionary complex systems, and intelligent swarm robotics.

Nima Fazeli

University of Michigan

Tactile Reasoning and Deformable Object Manipulation for OSAM

In this talk, we’ll provide an overview of our recent advances in tactile reasoning and its importance in autonomous operations and potential for OSAM. Next, we’ll demonstrate an extension of our results to deformable objects. Finally, we’ll discuss outstanding challenges and paths forward to next-gen OSAM operations.

Nima is an assistant professor with the newly established Robotics Department at the University of Michigan and the director of the Manipulation and Machine Intelligence laboratory (MMint). Nima and his group focus on developing algorithms and models that enable robots to master their physical interaction with their environments. Prior to UM, Nima completed his PhD and postdoctoral training at MIT where he developed a robotic system that learned to play Jenga.

Lu Li

Carnegie Mellon University

Space University Research Initiative (SURI) - Break the Paradigm: Rethink Space Logistics and Mobility

The Air Force Research Laboratory (AFRL) and The Air Force Office of Scientific Research (AFOSR) recently awarded a team of researchers from Carnegie Mellon University, Texas A&M University, University of New Mexico, and Northrop Grumman Corporation to study and explore disruptive solutions to fundamentally change the landscape of space robotics. The team is aim to combine expertise in artificial intelligence, robotics (hard and soft), additive manufacturing, astrodynamics, estimation theory, control, and space systems, to address the non-existent capability of On-orbit Servicing, Assembly, and Manufacturing (OSAM) in the geosynchronous equatorial orbit (GEO) belt. Success for this program means advancing transition-capable fundamental and applied research for OSAM and preparing it to transition to the AFRL Space Vehicles Directorate, Northrop Grumman Corporation, and other partners.

Lu Li is a research scientist at the Robotics Institute at Carnegie Mellon School of Computer Science. His research is focusing on cross-disciplinary robotic system science and engineering, including the study of novel mechanisms, electronics, algorithms, and system architectures for real-world challenges. Lu Li has over 10 years of experience in research and developing biologically inspired mechatronics, such as biomimetic tactile and visual sensors for aerospace non-destructive inspection, capable animal-shaped robots for disaster search & rescue missions, and neuroprosthesis restoring muscle functions for wounded veterans. Currently, Lu Li serves as the research and technical lead for the multi-institute Space University Research Initiative (SURI) project, titled Breaking the “Launch Once, Use Once” Paradigm.

Nina Mahmoudian

Purdue University

Robust Energy-Aware Multi-UAV Operations for Area Coverage

This talk presents enabling tools for the coverage problem with energy-constrained multi-robot systems. The approach is flexible to plan missions for a variety of applications, scalable to simultaneously plan trajectories for many workers and charging robots, and robust to adapt to unplanned failures. Unplanned failures are detrimental to a mission’s success, so automatically detecting failures and replanning greatly enhances the system’s reliability.

Dr. Nina Mahmoudian is an associate professor of Mechanical Engineering at Purdue University. Previously, she was the Lou and Herbert Wacker Associate Professor in Autonomous Mobile Systems with the Department of Mechanical Engineering at Michigan Tech. She received her Ph.D. in Aerospace Engineering from Virginia Tech. Dr. Mahmoudian is a recipient of the 2015 NSF-CAREER and 2015 ONR-YIP awards. She is serving as editor of ICRA and associate editor of RA-L.

Mark Mueller

University of California, Berkeley

Pushing aerial robot capabilities through novel designs

Changing the mechanical design of an aerial robot can lead to surprising and novel capabilities. I will present three designs of vehicles that use novel designs to accomplish otherwise hard/impossible objectives. The first uses angular momentum for increased disturbance rejection; the second uses a passively morphing structure to change shape mid-flight; and the last is a team of aerial robots capable of indefinite flights.

Mark W. Mueller is an assistant professor in the Mechancial Engineering Department at UC Berkeley since 2016, where his research focuses on the nexus of design, dynamics, and control of aerial robots. He received his undergraduate degree at the University of Pretoria, South Africa; and completed his doctorate at the ETH Zurich, Switzerland.

David Novick

Sandia National Labs

The Mobile Adaptive/Reactive Counter UAS System (MARCUS)

The Mobile Adaptive/Reactive Counter UAS System (MARCUS) program is developing counter unmanned aircraft systems (cUAS) technology to better address current and future threats to national security posed by low, slow and small threats. By adding sensors and effectors to mobile aerial systems and ground platforms, MARCUS improves the detection range and effectiveness of ground-based cUAS and enables efficient response to threat small UAS (sUAS). The MARCUS system includes three major elements: (i) detection and identification of a potential threat UAS, (ii) tracking and assessment, and (iii) neutralization of the threat UAS.

David Novick, PhD, is the Director of Operations and Chief UAS pilot for Sandia National Laboratories UAS Aviation Operations Unit (UAOU). For over a decade, he has worked on numerous projects which cover cUAS, test and evaluation using red team UAS, and autonomy.

Meeko Oishi

University of New Mexico

Stochastic optimal control for spacecraft maneuvering under non-Gaussian disturbances

Uncertainties due to unexpected failures may follow heavy-tailed distributions, in which low-likelihood events that have severe consequences are more frequent than would be expected from a Gaussian distribution. However, most methods for optimization and stochastic optimal control presume a Gaussian structure to the uncertainty. Our recent work has posited alternatives, based in characteristic functions and in quantile approximation, and has shown to be computationally tractable for satellite maneuvering problems.

Meeko Oishi received the Ph.D. (2004) and M.S. (2000) in Mechanical Engineering from Stanford University (Ph.D. minor, Electrical Engineering), and a B.S.E. in Mechanical Engineering from Princeton University (1998). She is a Professor of Electrical and Computer Engineering at the University of New Mexico. Her research interests include human-centric control, stochastic optimal control, and autonomous systems. She previously held a faculty position at the University of British Columbia at Vancouver, and postdoctoral positions at Sandia National Laboratories and at the National Ecological Observatory Network. She was a Visiting Researcher at AFRL Space Vehicles Directorate, and a Science and Technology Policy Fellow at The National Academies. She is the recipient of the NSF CAREER Award and a member of the 2021-2023 DoD Defense Science Study Group.

Christopher Petersen

Air Force Reasearch Laboratory

Space Robotics: Role and Challenges in Orbital Logistics

As opposed to many terrestrial vehicles, a common difficulty with satellites is that they are often designed as specialized one-offs, which as a consequence does not facilitate any logistics chain. To overcome this and enable satellite resupply, manufacture, and servicing, on-orbit robotics is crucial but is difficult due to the unknown and communication limited domain. This talk will briefly discuss the dynamical, control-focused, and operational challenges in on-orbit robotics and where the community can assist in advancing these needs together with AFRL.

At AFRL/RV and USSF, Dr. Chrispy is a Research Aerospace Engineer and a Deputy Program Manager. While in the government, he has worked on 10+ satellite experiments, developing, deploying, and executing GNCA algorithms for ground and on-orbit use. Currently he serves as the Deputy Program Manager of the Autonomous Demonstrations and Orbital eXperiments (ADOX) Portfolio, which is a series of satellite demonstrations focused on autonomy technologies to enable satellite inspection, XGEO (beyond geosynchronous orbit, or cislunar) space domain awareness and logistics in GEO including advanced propulsion and refueling. For his accomplishments, in 2021 he was awarded the AFRL Early Career Award. In Fall 2022, he will be joining the Mechanical and Aerospace Department at the University of Florida.

Sean Phillips

Air Force Reasearch Laboratory

On Advanced in Collaborative Control In Satellite Systems

Due to decreased costs in launching and manufacturing satellite systems, the space domain has become more congested and, indeed, contested. In this presentation, some recent developments in distributed and collaborative control for satellite systems will be presented.

Sean Phillips Sean Phillips is a Research Mechanical Engineer at the Air Force Research Laboratory in the Space Vehicles Directorate. He holds the title of Research Assistant Professor (LAT) at the University of New Mexico in Albuquerque, NM. He received his Ph.D in the Department of Computer Engineering at the University of California – Santa Cruz in 2018. He received his B.S. in Mechanical Engineering from the University of Arizona in 2011 and his M.S. in Mechanical Engineering from the same university specializing in Dynamics and Controls in 2013.

Davide Saldaña

Lehigh University

Fast Operation and Adaptation Using Modular Aerial Robots

During emergencies in urban scenarios, tasks like manipulation and transportation need to be performed rapidly to save human lives. Instead of using large and task-specific robots, we propose modular robot swarms, composed of hundreds of aerial modules that can rapidly adapt to achieve aerial tasks. Those modular robots must be able to rapidly change their shape and actuation capabilities to perform adaptable operations in time-critical situations. The potential and versatility of the modular aerial systems are mainly in their ability to change their shape by creating and removing physical connections between modules. In this presentation, we describe the main concepts in this area and our recent results on aerial vehicles that can self-assemble, self-adapt, and self-reconfigure in midair. Our work is mainly focused on co-designing algorithms, dynamical models, and robot hardware that enhances adaptability, scalability, and resiliency.

David Saldaña is an Assistant Professor in Computer Science and Engineering at Lehigh University. He worked as a Post-Doctoral Researcher at the GRASP Laboratory, University of Pennsylvania. His main research is focused on modular aerial robots, multi-robot systems, and robot swarms. He received his B.Sc.(2010) and M.Sc (2012) in Informatics Engineering from the National University of Colombia, and his Ph.D. in Computer Science, Artificial Intelligence and Robotics at UFMG, Brazil (2017). His current projects include enhancing resiliency in large-scale robot networks and co-designing algorithms, dynamical models, and robot hardware for modular robots.

Afroza Shirin

The University of Texas at El Paso

Non-linear Dynamics and Control to Quantify the Maneuverability of Space Vehicles and Robots

A multi-step optimization procedure is designed to quantify the maximum maneuverability of space vehicles and robots

Afroza Shirin received her Ph.D. in mechanical engineering from the University of New Mexico. Dr. Shirin is currently a Research Associate in the Aerospace Center at the University of Texas at El Paso. In Fall 2022, she will be joining the Mechanical Engineering Department at UTEP.

Jonathon E. Slightam

Sandia National Labs

Fundamentals of Airborne Manipulation – Passivity Analysis of Quadrotor Aircraft for Physical Interactions

The broad dissemination of unmanned aerial vehicles (UAVs), specifically quadrotor aircraft, has accelerated their successful use in a wide range of industrial, military, and agricultural applications. The physical contact required to perform aerial manipulation (AM) tasks results in dynamic coupling with the environment, which may lead to instability with devastating consequences for a UAV in flight. Considering these concerns, this work determines whether off-the shelf flight controllers for quadrotor UAVs are suitable for AM applications by investigating the passivity and coupled-stability of quadrotors using common flight controllers.

Jonathon E. Slightam is a Senior Member of Technical Staff at Sandia National Laboratories. He earned his Ph.D. in mechanical engineering from Marquette University and joined Sandia’s unmanned systems and autonomy research and development department in 2019. Jonathon’s research focuses on the modeling, design, and control of aerial and ground systems to realize next generation teleoperational and autonomous mobile manipulation.

Steven J. Spencer

Sandia National Labs

Fundamentals of Airborne Manipulation – Ongoing Work

This talk discusses ongoing work at Sandia National Labs for enabling general applications of aerial manipulation. Our primary approach is to develop passivity based controllers to guarantee stable interaction with the natural environment. We will present on work to date on developing controllers, simulations, and hardware, and briefly touch on future directions.

Steve Spencer holds a Ph.D. in Mechanical and Aerospace Engineering from the University of California, Irvine. For nearly a decade, he has worked in the Autonomy and Unmanned Systems group at Sandia National Laboratories. His research has included the design and control of robotic systems, multi-agent control, energy harvesting controllers, and energy-efficient design. He currently leads Sandia’s efforts for enabling manipulation from small autonomous aerial platforms.

Lydia Tapia

University of New Mexico

Multi-agent Learning: From Shared Spaces to Shared Thoughts

Learning for multiple agents coordinating to solve a task can be complex. Do you have each agent learn their job separately? Do you have them learn together in a shared space representation? While Reinforcement Learning (RL) provides promise in enabling straight-forward solutions for task learning through trial and error in an environment, it is notoriously computationally expensive. This expense can be especially high for multiple agents in shared spaces. Coordination for multiple agents can also happen through a shared learned model, with limitations. We explore the power of these shared models through problem setups where muti-agent learning via RL can occur with reduced computational expense.

Lydia Tapia is an Associate Professor in and Incoming Chair of the Department of Computer Science at the University of New Mexico. She received her Ph.D. in Computer Science from Texas A&M University and her B.S. in Computer Science from Tulane University. Her research contributions are focused on the development of computationally efficient algorithms for the simulation and analysis of high-dimensional motions for robots and molecules. Specifically, she explores problems in computational structural biology, motion under stochastic uncertainty, and reinforcement learning. Lydia is the recipient of the 2016 Denice Denton Emerging Leader ABIE Award from the Anita Borg Institute, a 2016 NSF CAREER Award for her work on simulating molecular assembly, and the 2017 Computing Research Association Committee on the Status of Women in Computing Research (CRA-W) Borg Early Career Award.

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