Global navigation satellite system (GNSS) plays an irreplaceable role in many areas such as unmanned autonomous systems. In this talk, the evolution of GNSS receiver architectures and the associated baseband signal processing algorithms will be discussed. In specific, the speaker will present his recent research works on vector tracking loop (VTL) and direct position estimation (DPE).

Human interaction with autonomous cyber-physical systems (CPS) is becoming ubiquitous in consumer products, transportation systems, manufacturing, and many other domains. My research aims to develop analytically computable tools to semantically understand and predict human-involved interactions using machine learning & AI techniques with support of data science toward eco-safe deployment of AI-based agents (e.g., autonomous cars) in the human-CPS such as smart cities.

Due to the rapid population aging globally, the current trend of robotic research has been shifting from traditional industrial robots that are separated from humans to human-centered robots that coexist, cooperate, and collaborate with humans. A major motivation for introducing compliance to human-centered robots is physical human-robot interaction.

Robots play significant roles in reducing human labor costs, increasing work efficiency and improving product quality. As the speed of product renewal accelerates, the demand for flexible lines is becoming more and more evident for small batches or multi-types production. However, most current robots still have large gaps in mechanism, perception and control capabilities compared with humans, making it difficult to adapt to the varying scenarios or tasks. In contrast, humans are better at handling incomplete, contradictory and ambiguous information. Therefore, communion with humans and the environment is an important approach to solving the problem of insufficient robot intelligence.

Robots are being increasingly deployed in the real world for applications such as warehouse automation, search and rescue, surveillance and reconnaissance, and environmental monitoring. However, when performing long-term tasks, robots need to deal with uncertain, changing environmental conditions. Robots may fail or if they are operating in adversarial environments, they may get attacked.I will describe resilient coordination algorithms to cope with adversarial attacks and applications to information gathering and target tracking with aerial robots. In addition, I will show how to use ideas from stock portfolio optimization to build risk-aware robot teams that can balance the trade-off between risk and reward. Further, I will describe how to employ graph neural networks to enable large-scale, decentralized multi-robot coordination and planning. In the end, I will discuss some future work on securing robot teams against adversarial attacks and uncertainties when the robots use machine learning techniques for perceptions and communications.

With over half a billion years of evolutionary history, fishes swim with high efficiency, great agility, and surprising stealth in the three-dimensional aquatic environment. Therefore, it is undoubtedly natural for engineers to turn to fish as a source of new ideas on underwater propulsive systems.

Over the past decades, roboticists, inspired by these biological systems, have constructed various fish-like robots that copy real fish morphology, locomotion, and movement.

Recently, the direction of bio-inspiration has started to flip to bio-understanding----using robotics in engineering to help answer questions in biology.

In this talk, I will first introduce how we construct and control the robotic fish following the paradigm of bio-inspiration. Then, I will give examples of applying real and virtual robots to help us explore why and how do fish school. Finally, I will briefly introduce my ongoing and future work in bio-inspiration and bio-understanding.

Visual localization refers to the recovery of theposition and orientation of cameras in known orunknown scenes, using only visual data. We humansuse vision as our primary source of informationfor localization, navigation, and exploration in ourenvironments. Similarly, a high-quality image with awide view of the surroundings often capturessufficient information to represent a locationuniquely. Therefore, using images to localize agentsin a map is an important research area of computervision and robotics. lmmediate consequences ofsolving the visual localization problem leads toexciting and potentially revolutionarymanyapplicationsincluding; humanassistance,autonomoussystems,andaugmented reality.Intuitively, by answering questions such as "Wheream lin the scene?”or “ How to render objects foraugmented reality? ”In this talk, I will present theproblem of visual localization in large scale scenesand recent progress in this direction. The talkcontentswill include,scenereconstruction.understanding,abstraction,andalgebraicframeworks to exploit them forlocalization.Different applications of the visual localizationsystems will also be demonstrate

The problem of verifying whether a high levespecification can be decomposed into localspecifications is of fundamental importancein top-down distributed supervisory controlbut unfortunately the state-of-art verificationalgorithm is of exponential time complexityIn this seminar, we talk about an approachfor reducing its verification complexity andsupporting its parallel verification by usingdivide-and-conquer reductions. We addressthe problemof generatingoptimalreductions automatically. A (partial) solutionto this problem is provided, which consists of1) an incremental algorithm for theproduction of candidate reductions and 2) areductionvalidationprocedure.Astrengthenedsubstitution-basedprooftechnique is used for automatic reductiona fixedvalidation,whiletemplate ofexamples is used forcandidate counterreduction refutation.

In the context of robotics and autonomous systems, theiterative linear quadratic regulator (iLQR) is known to bean effective approach to deal with the nonlineardynamical models in motion planning problems.However, it is the major shortcoming of the iLQR that themethod is not efficient when other complex constraintsare involved, such as the collision avoidance constraintTo overcome this rather significant impediment, a fastand efficient optimization algorithm is developed basedon the alternating direction method of multipliers(ADMM), such that the optimization problem is separatedinto severalmanageable sub-problems and theoptimization process can be accelerated to realizereal-timecomputation. In addition, leveraging thearchitecture of iLOR and neural network, another motionplanning algorithm is developed that avoids the priorknowledge of the system model. Depending solely onmeasurement data, it yields a completely non-parametricroutine for the establishment of the optimal policy. Asclearly indicated from the results attained in severalillustrative examples, these significant merits of theproposed algorithms are demonstrated rather evidently.

The diffraction limit is a fundamental barrier inoptical science and engineering. lt restricts theimaging resolution for both fundamental researchand applications in biology, chemistry, and materialscience industry. Microspheres have beendemonstrated as a powerful platform to challengethe diffraction limit. Microspheres can manipulatelight in a novel way that conventional opticalcomponents cannot achieve. In this presentation.Dr. Chen summarizes his contribution to thefundamental physics,system development,andpotential applications of this technology. The ~20nm observation power was demonstrated forlabel-free samples, which forms the foundation ofopticalsuper-resolutionimaging to observenano-structures in water, oil, and ambient air.