Xiaobo Tan - US grants

Ionic Polymer-Metal Composites (IPMCs), informally known as artificial muscles, are an innovative class of smart materials that generate large bending motions under low actuation voltages. As a first step towards the realization of the full potential of IPMCs, the proposed research aims to develop a control-oriented model for IPMC actuators that captures essential dynamics and nonlinearities. It addresses a problem of practical interest that is largely unexplored in existing IPMC modeling work, which typically deals with either complex continuum models (infeasible for real-time control) or simplified linear models (valid only within limited actuation ranges). This project will explore a novel model structure that accounts for major actuation mechanisms and incorporates two nonlinear modules, stress-strain hysteresis and nonlinear dependence of the electrical behavior on the IPMC bending curvature. Given the explicit physical interpretations of individual modules, model identification will be performed by exploiting the separation of energy domains and time scales. Integrating the core nonlinearities, the proposed model is expected to provide a basis for fast, precision, and energy-efficient control of IPMC actuators.

As artificial muscles, IPMCs have a wide spectrum of potential applications in biomedical devices, biomimetic robotics, and micro- and nanomanipulation systems. One crucial barrier to these applications is the complicated, nonlinear, electromechanical behaviors of IPMCs. By capturing the key characteristics in a succinct yet adequate way, the proposed research will facilitate the design of effective controllers ("brains") for robots, devices, and systems enabled by these artificial muscles. This can lead to agile prosthetic devices, active bioinstrumentation tools (for example, steerable catheters), and dexterous micro- and nanomanipulators, with potential impacts on health care and micro/nano-manufacturing. In this project the PI will also hold appealing "artificial muscles in action" demos and lectures for K-12 students, and inspire the interest of young students, especially female students, in science and engineering.

PROJECT SUMMARY
This CAREER proposal describes an integrated research and education program that will build a
foundation for achieving the PIs career goals: to deliver smaller and smarter systems by developing
novel modeling and control methodologies, and to train tomorrows control engineers with crossdisciplinary
perspectives. In particular, the proposed research aims to fully realize the potential of
Ionic Polymer-Metal Composites (IPMCs), informally known as artificial muscles, in manipulation of
delicate, microscale objects (e.g., capture and transport of single biological cells and assembly of 3D
MEMS structures), by developing modeling, sensing, and control strategies to address time-varying,
nonlinear behaviors of IPMCs in actuation and sensing. The research will have four core thrusts:
1. Development of a control-oriented model capturing essential dynamics and nonlinearities in
IPMCs, including both hysteresis and nonlinear feedback coupling from the bending curvature
of an IPMC actuator to its electrical behavior.
2. Investigation of two original sensing approaches for IPMCs: one exploiting IPMCs built-in
sensory capability using nonlinear compensation, the other utilizing the curvature-to-electrical
behavior coupling observed by the PIs group.
3. Development of control schemes targeting the major nonlinearities in IPMC actuators, including
adaptive inverse control methods for hysteretic, dynamical systems to accommodate possible
variation of IPMC behaviors.
4. Design and fabrication of a biomimetic micromanipulator with IPMCs functioning simultaneously
as structures, actuators, and sensors, and validation of the proposed modeling, sensing,
control methods through manipulation of microbeads and biological cells (in collaboration with
a biomedical engineer at Michigan State).
Intellectual Merit:
The proposed research will enable fast, precision control of IPMC actuators throughout their full
actuation ranges by identifying and accommodating major nonlinearities in the control design. The
developed sensing schemes can potentially eliminate the need for external sensors, resulting in smaller
systems. Theoretical and experimental investigation of scaling laws will help understand the capabilities
and limitations of micro IPMC actuators and sensors, and offer insight into design of active
dithering schemes to overcome adhesion, a critical problem in micromanipulation. The proposed
project will thus promote the development of compact, dexterous IPMC-based micromanipulation
systems while motivating formulations and solutions of new problems in modeling and control.
Broader Impacts:
The proposed research will provide an innovative approach to manipulation of biological cells and micro
devices, facilitating advances in biological studies, biotechnology, and microtechnology. Through
collaboration with Environmental Robots Inc., the developed control and sensing schemes will be
applied to a number of IPMC-based biomedical applications (e.g., implantable micropumps for drug
delivery), with potential impacts on health care. Integrating with the research program, the PI will
establish an interdisciplinary curriculum on Smart Materials and Systems including a senior design
program involving industrial partners (the PI has secured seed funding from SPIE) and a graduate
course Smart Sensors and Actuators in Micro and Nanosystems. As a faculty advisor to the undergraduate
research program hosted by the Diversity Programs Office at Michigan State, the PI
will involve women and minority students in developing biomimetic microrobots incorporating smart
sensors and actuators, and further use these microrobots as appealing, hands-on educational kits to
inspire the interest of K-12 students in science and engineering.

The increasing interaction between computing technology and the physical world requires that systems be able to adapt to changing conditions, compensate for hardware and software failures, fend off attacks, and optimize performance, all with minimal human intervention. Robust operation is especially important among collections of small devices, such as micro-robots and sensors that need to perform complex distributed tasks despite adverse operating conditions. Digital evolution offers a means to produce robust computational behaviors and customize them for target hardware and environments. In digital evolution, populations of self-replicating computer programs are subject to random mutations in dynamic environments, leading to evolution by natural selection.

This infrastructure project will construct a testbed to support digital evolution of complex distributed behaviors and their evaluation on heterogeneous computing systems. The testbed will include terrestrial mobile robots, custom-built robotic fish, and stationary sensors, as well as a rack-mounted parallel processor for on-line software evolution. The testbed will support research projects addressing energy-efficient mobility control for swarms of mobile devices; adaptive communication protocols; and self-adaptive and self-healing software. To maximize its impact, the testbed will be integrated with existing facilities, creating a rich computing and communication fabric for experimental research.

Proposal Number 0820220

Title: ORCHID: Harnessing Digital Evolution in Design High-Assurance Adaptive Systems

PIs: Betty Cheng, Philip McKinley, Charles Ofria, and Xiaobo Tan

A robust cyber-infrastructure must be able to monitor the environment and its own behavior, adapt to changing conditions, and protect itself from component failures. The hallmark of the Orchid project is to introduce the fundamental biological principle, evolution, into the development process for adaptive real-world software systems. The project will use and extend the Avida digital evolution software platform to address three primary tasks: (1) exploiting the automatic generation of software models and search capacity of digital evolution to enable the developer to identify viable system configurations; (2) generating novel strategies to adapt from one system behavior to another in response to changing environmental conditions; and (3) providing assurance for adaptive systems by revealing latent properties within a given configuration in order to distinguish generated configurations and remove unwanted behavior. A prototype system will be developed and used to conduct an experimental case study in the design of self-adaptive aquatic mobile sensor networks for homeland security and environmental monitoring. In addition, an instructional system, Avida-EDAS, will be developed to enable students to evolve models of adaptive software, conduct experiments to assess the impact of adverse environmental conditions, and observe the effects of different adaptation strategies on system execution.

The research objective of this award is to develop a novel multi-time-scale nonlinear and adaptive control framework for hysteretic systems, and thus to enable robust, precision, and high-bandwidth control of smart material-actuated systems. Smart materials, such as piezoelectric materials and shape memory alloys, exhibit strong coupling of complex hysteretic behavior with the nonlinear dynamics of structures and fluids that are driven by smart material actuators, especially at medium-to-high drive levels. The latter, together with the uncertainties in both hysteresis and dynamics, makes it challenging to precisely control smart material-actuated systems. In this research, a multi-time-scale averaging theory for hysteretic systems will be established. This will, for the first time, provide a framework for merging adaptive hysteresis compensation with a plethora of nonlinear and adaptive control methods for hysteresis-free systems through time-scale separation. In addition, a general, parallel paradigm for hysteresis inversion and adaptation will be developed based on reconfigurable computing hardware, to enable efficient implementation of the proposed theory. The developed theory and algorithms will be validated in the control of a piezoelectric actuator-driven nanopositioning system.

The proposed project can positively impact a number of application areas of smart materials, such as micro- and nanotechnology, biomedical devices, robotics, and aerospace and automotive industries. The interdisciplinary project will offer valuable training experience for talented graduate and undergraduate students, especially those from underrepresented groups. It will also enrich existing and newly developed courses on smart materials and controls at Michigan State University, such as Smart Material Sensors and Actuators, and Adaptive Control. The PIs will also proactively seek opportunities to transfer the developed technology to the nanopositioning and scanning probe microscopy (SPM) industry.

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."

This award provides funding for a 3 year standard award to support a Research Experiences for Teachers (RET) in Engineering Site program at Michigan State University (MSU) entitled, "RET Site on Bio-Inspired Technology and Systems (BITS)", under the direction of Dr. Xiaobo Tan.

This RET Site program aims to establish a strong partnership between MSU, the hosting institution, the NSF Engineering Research Center (ERC) for Wireless Integrated MicroSystems (WIMS), the co-hosting institution, school districts, and industry on advancing pre-college science and engineering education, by training a cadre of leaders of middle and high school teachers in the areas of STEM. The three year program will provide a 7 week summer experience for a total of 26 middle and high school STEM teachers from Holt Public Schools, Utica Community Schools, and the Detroit Area Pre-College Engineering Program (DAPCEP). These teachers will be given the opportunity to participate in cutting-edge research on BITS, with "one-on-one" mentoring from engineering faculty. Working with PIs, faculty mentors, and a teacher development specialist from the College of Education, teachers will also develop innovative, standards-compliant curriculum modules and participate in a number of professional development activities: workshops, seminars, and field trips to industry and national labs. Extensive follow-up activities are planned throughout the academic year to ensure translation of the lab experience to classroom practice and to foster and strengthen long-term partnerships between engineering faculty and teachers. The Site will also engage industrial partners to explore a potentially sustainable paradigm for RET that is supported primarily by industry.

This Site is expected to enrich the professional development of a number of future leaders in STEM education, result in innovative curriculum for science and technology courses, and most importantly, peak the interest of middle and high school students in scientific inquiry.

The goal of the AquaSWARM project is to design and develop small, energy-efficient, autonomous underwater robots as sensor-rich platforms for dynamic, long-duration monitoring of aquatic environments. A novel concept of gliding robotic fish is investigated, which merges the energy-efficient design of underwater glider with the high maneuverability of robotic fish. Gliding motion, enabled by pitch and buoyancy control, is exploited to realize dive/ascent and large-distance horizontal travel. Soft actuation materials-based flexible tail fins are used to achieve maneuvers with high hydrodynamic efficiency. The research is focused on understanding gliding design for small robotic fish, and addressing the energy efficiency issue from a systems perspective. Schools of such autonomous robots are deployed in lakes at the Michigan State University Kellogg Biological Station to detect harmful algal blooms (HABs) and validate models for HAB dynamics.

The project is expected to result in cost-effective, underwater robots that can perform uninterrupted, long-duration (several months), long-travel (hundreds of miles) operation in aquatic environments. This will provide a novel, viable, versatile, cyber-physical infrastructure for aquatic environmental monitoring, with applications ranging from understanding the impact of global warming, to environmental protection, drinking water reservoir safety, and seaport security. The project also offers an interdisciplinary training environment for graduate and undergraduate students, and provides outreach opportunities to inspire pre-college students and train highly qualified teachers. Robotic fish-based HAB detection will also be used as a tool to engage communities at local lakes and stimulate their interest in novel technology and environmental issues.

The objective of this research is to establish a principled framework for the design and operation of aquatic sensor networks consisting of resource-limited nodes. The proposed approach is to exploit adaptation and collaboration among nodes to holistically deal with or even leverage uncertainties in sensing, communication, and mobility. Main research thrusts include online sensor and fusion calibration for dynamic environments, model-driven radio power adaptation to achieve assured communication performance, and exploitation of node mobility and fluid motion in the joint optimization of sensing, networking, and control to realize efficient coverage and tracking. The proposed methodology will be validated in detection and tracking of harmful algal blooms at the MSU Kellogg Biological Station using networks of robotic fish.

The project will result in a unifying design framework for aquatic sensor networks to achieve energy-efficient operation with assured spatiotemporal sensing performance. Some methodologies developed in this project, e.g., exploiting the seemingly undesirable environmental disturbances, could apply to aerial and terrestrial sensor networks and thus benefit those fields as well.

The project is expected to bring aquatic sensor networks much closer to their envisioned applications, and positively impact monitoring of lakes and other ecosystems, tracking of oil spills and pollutants, and surveillance of ports and rivers. The project will enrich two graduate-level courses and provide interdisciplinary training for graduate and undergraduate students. The project will also offer an excellent opportunity to reach out to K-12 students and schools through interactive lectures, robotic fish competitions, and participation in a teacher training program at MSU.

The oil spill in the Gulf of Mexico is expected to have devastating impact on the environment, ecosystem, and local economy for many years to come. Monitoring and tracking the oil plume is critical for cleanup efforts, beach closure warnings, protection of sensitive areas, and understanding of the spill?s environmental and ecological impacts. There is an urgent need for new, efficient, and economical technology for ubiquitous monitoring of the oil spill. The proposed one-year RAPID project responds to this need and aims to develop and deploy a school of small, cost-effective, energy efficient gliding robotic fish for dynamic and continuous monitoring of coastal areas of the Gulf for detection and tracking of oil plumes, both on and under the sea surface. This grand goal will be achieved through intensive research and development efforts in three areas:
- Design and development of gliding robotic fish. Through gliding mechanism design, tail-glider integration, and packaging scheme development, we will achieve the desired specifications on the maximum dive depth, travel speed, duration of continuous operation, and reliability.
- Realization of autonomy, through onboard instrumentation, modeling and control of robot dynamics, and design of communication and coordination protocols. The autonomy will enable robots to work reliably in bumpy waters, avoid obstacles, and maintain network connectivity.
- Demonstration and deployment in the Gulf. Gliding robotic fish equipped with compact crude oil sensors will be deployed to detect and track oil plumes in the Gulf, with technology transfer pursued at the same time for wide availability of the developed sensing platform.

Intellectual Merit. With the pressing need from the ongoing Gulf crisis, this project is expected to rapidly advance gliding robotic fish-based mobile sensing technology, from the current preliminary laboratory prototypes to robust and cost-effective products that will succeed in real, harsh environments such as the Gulf of Mexico. In particular, the project will result in novel mechanism designs for small gliders, advanced control algorithms for gliding robotic fish, and valuable packaging solutions that are critical for the platform reliability. This project could usher in a new era in aquatic environmental monitoring and is thus potentially transformative.

Broader Impacts. The developed technology in this RAPID project will impact the cleanup and monitoring efforts for the Gulf oil spill for many years to come, by offering flexible, distributed, dynamic sampling coverage with low overhead. It will also impact many other areas, such as monitoring of aquatic ecosystems, harbor and port security, and drinking water safety. The project will be integrated with many outreach activities that the PI has already been involved in. In particular, a related exhibit Swimming with Robotic Fish will be presented at the inaugural US Science and Engineering Festival on the National Mall in October 2010, to pique the interest of thousands of young students in science and engineering.

The Evolution Park provides a testbed for applying evolutionary computation to the development and control of autonomous robotic systems. Mixed populations are created, including some robots and some natural systems to facilitate a form of biomutualism where biology, engineering and computer science inform one another in synergistic and mutually beneficial ways. The 3D printer enables the realization of physical bodies (morphologies) that evolve concurrently with their control systems. The fabricated bodies are coupled with electroactive polymer materials to produce artificial organisms capable of locomotion without motors. Specially instrumented aquatic environments allow robotic fish to be used as stimuli to elicit behavioral responses in living fish under conditions manipulated by the experimenter. A collection of high-performance graphics workstations with large monitors facilitate fine-grained, interactive analysis of evolved behaviors in simulated robots, as well as analysis of video data captured by underwater cameras.

This infrastructure enables compelling new research directions in evolutionary robotics and squarely builds on previous work by the PIs on group communication and cooperative behavior, parallel processing, wireless networking, autonomic computing, distributed algorithms, high-assurance software, complex behavior in natural organisms, sensors and actuators, robotic fish, bio-inspired robots and swarms, as well as speciation and the evolution of communication. These advances benefit many applications in science and engineering, public safety, and national defense. The Evolution Park supports several innovative educational and outreach activities. Such endeavors provide a framework for hands-on experiments that are integrated into university courses, teacher training workshops, summer camps and after-school programs for K-12 students.

Exploiting advances in underwater robotics, sensor networks, signal processing, and biophysical modeling, the goal of this award is to create a novel paradigm for monitoring and understanding aquatic ecosystems and thus enable sustainable management of water resources. In this paradigm, schools of autonomous gliding robotic fish adaptively sample the water environment. The collected measurements are used to reconstruct high-resolution data fields with advanced multidimensional signal processing algorithms. The reconstructed data fields, along with the data samples, facilitate the monitoring of aquatic ecosystems and enable high-fidelity, mechanistic modeling of the underlying biophysical processes for accurate forecast. The objectives of this award include addressing fundamental problems at the interfaces between the building blocks of the paradigm, and demonstrating a proof of concept for the latter. Specifically, five highly integrated research thrusts are pursued: (1) developing path-planning and control algorithms for the robots to realize information-driven, energy-efficient sampling, (2) developing robust communication protocols and effective in-network parameter-estimation algorithms, (3) establishing tensor sparsification-based frameworks for data-field reconstruction using limited data samples, (4) exploiting reconstructed data fields and network-estimated sub-models to create accurate mechanistic models, and (5) evaluating and demonstrating the integrative paradigm in the monitoring and prediction of Harmful Algal Blooms.

This award is expected to result in a new, holistic framework for monitoring, understanding, and managing freshwater and marine environments, with a myriad of applications in oil spill response, ecosystem monitoring, and drinking water safety, to name a few. The project provides interdisciplinary training opportunities for students, including those from underrepresented groups. Robotic fish demos, museum exhibits, and teacher-training activities are offered to engage K-12 students, teachers, and the public, and to pique their interest in science and engineering. Besides the dissemination of research findings through conference presentations, publications, and workshops, commercialization of the developed technologies is pursued to facilitate their practical adoption.

The research objective of this project is to investigate modeling and control methods for vanadium dioxide-based microactuators, to enable robust, precise, and efficient control of these actuators. Vanadium dioxide is a smart material, whose unique actuation potential has not been noticed until recently, and it has advantages of high volumetric energy density, superior durability, and excellent repeatability. However, pronounced hysteresis from phase transition, together with sophisticated, coupled electro-thermo-mechanical dynamics, presents significant challenges in the control of vanadium dioxide-based microactuators. The research approach consists of creating a model that captures hysteresis across multiple physical domains with high fidelity and minimal complexity, developing algorithms for self-sensing and for controlling systems with non-monotonic hysteresis, designing and fabricating vanadium dioxide-based multi degree-of-freedom micromanipulators, and experimentally validating the developed modeling and control methods.

By exploiting the synergy between control theory and novel actuation materials, the project will advance the state of the art in both. If successful, the results from this award will facilitate the realization of the full potential of vanadium dioxide-based actuators in areas such as microsurgery, microrobotics, memory technology, and microassembly, with positive impact on biotechnology and micro/nano-manufacturing. Besides curriculum enrichment, the project will provide unique interdisciplinary training opportunities for graduate and undergrad students. Both PIs have a strong track record in recruiting and advising female and minority students, with the co-PI himself being a successful role model for Hispanic students. A number of outreach activities will be developed to engage K-12 students and the public, to pique their interest in science and engineering. Examples of these activities include teacher training, interactive demos, and ?dancing with the micro stars?, where students act as choreographers and make arrays of vanadium dioxide-coated micro-cantilevers ?dance? to music through laser scan programming.

This award renews an exemplary Research Experiences for Teachers (RET) Site on Robotics Engineering for Better Life and Sustainable Future at Michigan State University. The renewed site will continue to develop a strong partnership between MSU and schools in the greater Lansing-Detroit-Grand Rapids area on advancing pre-college science and engineering education by training a cadre of leaders of middle and high school teachers in the areas of Science, Technology, Engineering, and Mathematics (STEM). The program will recruit teachers from schools in Mid- and Southeast Michigan with a focus on those serving socioeconomically challenged populations and students from groups traditionally underrepresented in science and engineering. RET participants will attend a 6-week summer institute, to participate in cutting-edge research on robotics engineering, with mentoring from engineering faculty who lead vibrant robotics related research programs. Working with PIs, faculty mentors, a curriculum development specialist and an editor of TeachEngineering.org, teachers will develop innovative, standards-compliant curriculum modules and participate in a number of professional development activities. Extensive follow-up activities are planned throughout the academic year to ensure the translation of lab experience into classroom practice, and to foster and strengthen long-term partnership between engineering faculty and teachers. A third-party professional program evaluator will track and evaluate the program and provide feedback for improvement. The evaluator will also conduct longitudinal studies on participants to assess the longer-term impact of the RET program.

Intellectual Merit. Under the coherent theme of Robotics Engineering for Better Life and Sustainable Future, the proposed RET Site will expose teachers to leading robotics research spanning biorobotics, evolutionary robotics, nanorobotics, brain-machine interface, biomechanics, and human-robot interaction, and to the profound changes robotic technologies will bring to personal care, medical procedures, environmental monitoring and exploration, and entertainment and gaming. The interdisciplinary nature of robotics engineering will provide a fertile ground for developing creative course modules in biology, physics, chemistry, and technology that align with state and national standards, which will excite pre-college students and liven up classroom learning.

Broader Impacts. The proposed RET Site project is expected to enrich the professional development of a number of future leaders in STEM education, about half being females with a similar ratio for minorities. It will also result in innovative curriculum for science and technology courses, and pique the interest of middle and high school students in scientific inquiry. Through the partnership with schools in Lansing, Detroit, and Grand Rapids, and the all-girls Regina High School, the proposed project will positively influence the learning and career paths of young students, especially students from underserved districts and underrepresented groups in Michigan and beyond for years to come, thus contributing to a technology-savvy workforce that is much needed by America.

Monitoring and understanding aquatic environments is critical to water sustainability. The goal of this award is to establish a theoretical framework and provide an enabling technology for robust underwater collaborative sensing with small, inexpensive robots. Inspired by the source-seeking behavior of live fish, computationally efficient algorithms are developed for cooperative tracing of the gradients of environmental fields, and their robustness is analyzed in the presence of localization error and changing communication topology. The algorithms are experimentally validated in thermal source seeking and tracing with a group of energy-efficient and highly maneuverable gliding robotic fish, which are enhanced in this project with optical communication and localization capabilities. Advanced controllers are developed for these robots to realize three-dimensional maneuvering and to track reference paths planned through collaborative sensing algorithms. This award offers fundamental understanding of limits and robustness properties of collaborative sensing by resource-limited robots, and contributes to the knowledge base in underwater communication and ranging for small robots. It enables technological advances for persistent sampling of versatile aquatic environments including coastal waters, lakes, and rivers, with a myriad of applications such as oil spill response, ecological monitoring, and port and drinking water security. The findings from this project are disseminated through publications, software sharing, and technology commercialization. The project provides interdisciplinary training opportunities for students, including those from underrepresented groups. Outreach activities, including museum/aquarium exhibits and teacher training, are developed to pique the interest of K-12 students, teachers, and the public in science and engineering.

This PFI: AIR Technology Translation project focuses on translating the gliding robotic fish technology recently developed at Michigan State University to meet the market need for an inexpensive mobile sensing technology for various aquatic environments. The translated gliding robotic fish technology has the following unique features: low cost, compact size, high maneuverability, long operational duration, and adaptability to versatile applications. It provides exemplary advantages in performance, cost-effectiveness, and portability when compared to the leading competing technologies in this market place, including propeller-driven autonomous underwater vehicles (AUVs) and ocean gliders. The project accomplishes its goals by (1) developing a modularized payload architecture to facilitate convenient customization and cost-effective maintenance of the product, (2) developing a solar and wave-energy harvesting system to enable continuous, long-duration field operation, and (3) demonstrating a prototype of a gliding robotic fish incorporating these advances. The partnership engages the environmental monitoring industry and potential users of the technology to provide guidance in prototype development and market analysis as they pertain to the potential to translate the gliding robotic fish technology along a path that may result in a competitive commercial reality.

The potential economic impact is expected to be over $200M for the next 15 years, which will contribute to the U.S. competitiveness in the autonomous underwater sensing market. The societal impact, long term, will be enhanced capabilities in monitoring water quality and aquatic ecosystems, in inspecting underwater structures such as subsea drilling infrastructure for the oil and gas industry, and in securing ports and maritime borders.

Tracking Fish Movement with a School of Gliding Robotic Fish

This project is focused on developing the technology for continuously tracking the movement of live fish implanted with acoustic tags, using a network of relatively inexpensive underwater robots called gliding robotic fish. The research addresses two fundamental challenges in the system design: (1) accommodating significant uncertainties due to environmental disturbances, communication delays, and apparent randomness in fish movement, and (2) balancing competing objectives (for example, accurate tracking versus long lifetime for the robotic network) while meeting multiple constraints on onboard computing, communication, and power resources. Fish movement data provide insight into choice of habitats, migratory routes, and spawning behavior. By advancing the state of the art in fish tracking technology, this project enables better-informed decisions for fishery management and conservation, including control of invasive species, restoration of native species, and stock assessment for high-valued species, and ultimately contributes to the sustainability of fisheries and aquatic ecosystems. By advancing the coordination and control of gliding robotic fish networks and enabling their operation in challenging environments such as the Great Lakes, the project also facilitates the practical adoption of these robotic systems for a myriad of other applications in environmental monitoring, port surveillance, and underwater structure inspection. The project enhances several graduate courses at Michigan State University, and provides unique interdisciplinary training opportunities for students including those from underrepresented groups. Outreach activities, including robotic fish demos, museum exhibits, teacher training, and "Follow That Fish" smartphone App, are specifically designed to pique the interest of pre-college students in science and engineering.

The goal of this project is to create an integrative framework for the design of coupled robotic and biological systems that accommodates system uncertainties and competing objectives in a rigorous and holistic manner. This goal is realized through the pursuit of five tightly coupled research objectives associated with the application of tracking and modeling fish movement: (1) developing new robotic platforms to enable underwater communication and acoustic tag detection, (2) developing robust algorithms with analytical performance assurance to localize tagged fish based on time-of-arrival differences among multiple robots, (3) designing hidden Markov models and online model adaptation algorithms to capture fish movement effectively and efficiently, (4) exploring a two-tier decision architecture for the robots to accomplish fish tracking, which incorporates model-predictions of fish movement, energy consumption, and mobility constraints, and (5) experimentally evaluating the design framework, first in an inland lake for localizing or tracking stationary and moving tags, and then in Thunder Bay, Lake Huron, for tracking and modeling the movement of lake trout during spawning. This project offers fundamental insight into the design of robust robotic-physical-biological systems that addresses the challenges of system uncertainties and competing objectives. First, a feedback paradigm is presented for tight interactions between the robotic and biological components, to facilitate the refinement of biological knowledge and robotic strategies in the presence of uncertainties. Second, tools from estimation and control theory (e.g., Cramer-Rao bounds) are exploited in novel ways to analyze the performance limits of fish tracking algorithms, and to guide the design of optimal or near-optimal tradeoffs to meet multiple competing objectives while accommodating onboard resource constraints. On the biology side, continuous, dynamic tracking of tagged fish with robotic networks represents a significant step forward in acoustic telemetry, and results in novel datasets and models for advancing fish movement ecology.

Advances in computing and manufacturing have led to rapid developments in autonomous robots. For sophisticated tasks such as search and rescue, it is often critical to integrate human knowledge and perception skills with the capabilities offered by robots. Taking underwater search and rescue as a motivating context, this project focuses on developing a principled design framework for optimizing the performance of a mixed human-robot team comprised of multiple human operators and heterogeneous robots. By enabling efficient and reliable human-robot interactions, this work will facilitate the use of robots in hazard response, environmental monitoring, mobility of goods and humans, healthcare, manufacturing, and many other applications of societal impact. The project will provide training opportunities for graduate and undergrad students, including those from underrepresented groups. It will also provide research training to high school students and K-12 teachers. An open-source robotic fish educational kit and demos of EEG-mediated human-robot interactions will be developed to pique the interest of K-12 students in science and engineering. The project will further produce an underwater robotics testbed available for use by the broader robotics and control community.

This research will develop a generalizable framework for rigorous and systematic design of autonomy supervised by a team of interacting human operators, which will enable the leveraging of human operators' adaptivity in complex scenarios while mitigating performance deterioration due to loss of situational awareness. The framework will consist of two tightly coupled modules. The first module will involve optimal task allocation and scheduling for event-triggered human team supervision, which will be formulated as a semi-Markov decision process (SMDP) for a complex queueing network capturing task processing by a team of human operators with different skill sets. Human cognitive dynamics will be incorporated via practical models, and efficient algorithms for solving the SMDP are examined while uncertainties introduced by stochasticity in cognitive processes and variability among human operators are accommodated. The second module of the framework will deal with informative path planning for autonomous robots that optimally balances the explore-exploit trade-off in their search for targets of interest, by solving a multi-armed bandit problem that incorporates mobility constraints of the robots. The framework will be experimentally evaluated in field trials emulating underwater search and rescue, which will involve a group of gliding robotic fish and remotely operated vehicles (ROVs), supervised by a team of two human operators.

This project develops the theory and algorithms for autonomous navigation of mobile sensing platforms, such as unmanned ground, aerial, and underwater vehicles, so that the collected information is maximized while constraints on movement capabilities and energy expenditure are accommodated. This work facilitates the use of autonomous robots in environmental monitoring, search and rescue, surveillance and security, among other applications of societal importance. The algorithms are evaluated in field trials using unique gliding robotic fish. The project provides training for both graduate and undergraduate students, including those from underrepresented groups. Through showcasing at the Museum of Science and Industry in Chicago and offering of an open-source robotic fish education kit, the project promotes the interest of K-12 students and the general public in science and engineering. The project further facilitates transfer of software and hardware for robotic sensing to the market.

The goal of this project is to bridge the gap between the theory and practice in information-driven mobile sensing and to develop a principled theoretic and algorithmic framework for autonomous exploration in uncertain, specifically underwater, environments. The approach exploits the concept of ergodic exploration, where the underlying optimization problem is solved using methods from nonlinear optimal control. The research consists of: 1) Establishing a rigorous theoretical framework for ergodic exploration for guaranteeing solution well-posedness and stability, and developing synthesis methods for real-time control; 2) exploring active probing of flow conditions via auxiliary measurement of tracer agents to mitigate environmental uncertainty; 3) investigating collaborative exploration schemes that strike balance among performance, complexity, and robustness; and 4) conducting field experiments to validate the framework using a group of gliding robotic fish to monitor harmful algal blooms and localize sources of chemical spills.

Abstract
This grant will support research that will contribute novel methodologies related to multi-arm soft robots, advancing its practical use in applications involving close collaboration with humans. Soft robots have great promise in versatile applications involving interactions with humans, such as elder care, collaborative surgery, work/life assistance, and collaborative fruit harvesting. Taking apple-picking as a motivating case study, the goal of this project is to develop a novel soft robot system equipped with multiple soft arms, termed soft multi-arm robot or SMART, and to advance its practical use in applications involving close collaboration with humans. This award supports fundamental research that addresses the major challenges in soft robot design and fabrication, motion planning and control, environment and human perception, and human-robot interaction. The new designs and methodologies will enable safe, efficient, and robust cooperation between multiple soft robot arms and humans. The soft multi-arm robot system can not only improve production efficiency (for example, in assisting fruit harvesting), but also contribute to meeting the nation?s urgent need to take care of the elderly population (for example, in elder care and assisted living). Therefore, results from this research will benefit the U.S. economy and life quality. This research involves several disciplines including soft robotics, control, human-robot interaction, perception and learning, and agriculture automation. The multi-disciplinary approach also facilitates the participation of underrepresented groups in research and positively impacts engineering education.

The soft multi-arm robot system is expected to offer dexterity, efficiency, and intrinsic safety, and achieve productive collaboration with humans with an array of exciting potential applications. To achieve this goal, five synergistic research thrusts are pursued to overcome key scientific challenges: 1) designing and fabricating soft multi-arm robots to realize simultaneous actuation and stiffness-turning and enable dexterous manipulation, 2) advancing motion planning and control approaches for these soft robots to achieve robust manipulation in 3D space in the presence of stationary and dynamic obstacles, 3) formalizing trust-based human-robot interaction to realize efficient human-robot collaboration by explicitly accommodating the dynamics of human trust in the soft multi-arm robot policy, 4) developing orchard and human motion perception algorithms to robustly obtain 3D tree and human position/pose information to support the fruit harvesting application, and 5) evaluating the soft multi-arm robot system via extensive lab and field experiments in the context of collaborative apple harvesting. Collectively, advances from these research endeavors are expected to make soft multi-arm robots practically viable, especially for applications involving close collaboration with humans.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Autonomous aerial, ground, and underwater robots have emerged as promising platforms for a myriad of sensing applications. Aerial drones can monitor natural calamities like tornadoes and forest fires, and underwater robots can detect harmful algal blooms and track invasive fish species. The information to be gathered is typically governed by some nonlinear dynamic processes. A natural question to ask is, given a limited number of mobile sensors, how should they be dynamically placed to best observe the quantity of interest, especially with limited energy while requiring the robots to remain connected? The space of possible sensor placements is vast and constraints on energy and connectivity add to the challenges. This project will develop efficient algorithms to schedule the mobile sensors under these constraints and evaluate their performance in tracking a moving target through field experiments. The interdisciplinary nature of this research will be integrated with outreach and educational activities to broaden participation of K-12 and undergraduate students, especially from underrepresented groups.

The project combines the investigators? complementary expertise in control, network theory and fast randomized computation as the project will result in a fresh perspective and a generalizable, principled framework for scalable scheduling of mobile sensors with observability guarantees. The project goals will be realized through four integrated research thrusts that span theoretical investigation, algorithmic development, and experimental validation. Thrust 1 focuses on integrating a Gramian-based nonlinear observability metric with randomized sampling for efficient computation of near-optimal sensor placements under sensing and communication uncertainty. Thrust 2 extends the framework to accommodate energy and connectivity constraints, where special emphasis will be on distributed approaches for computation. Motivated by the fish-tracking application, the theory and algorithms developed in Thrusts 1 and 2 will be validated experimentally with a fleet of autonomous surface vehicles tracking a moving acoustic tag. Thrust 3 of the project involves the development of the experimental testbed, including the robots and their associated models and controllers, while Thrust 4 evaluates the developed mobile sensor scheduling algorithms via both simulation and field experiments in Higgins Lake, Michigan.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

In the US, agricultural drainage infrastructure benefits >22.6 Mha of cropland and is valued at ~$100B. As a proportion of total croplands, drained croplands produce a disproportionately large amount of grain but also release a disproportionately large amount of eutrophying nutrients to aquatic ecosystems. Drainage systems include individually-owned field drains that depend on the function of community-owned main drains. Climate change and agricultural intensification are causing farmers to increase the extent and intensity of drainage leading to a pressing need to balance productivity, profitability, and environmental quality when making drainage decisions. Further, because drainage systems include individually-owned and community-owned drains, decision-making involves complex techno-economic social issues together with understanding biophysical processes and requires balancing the needs of individual farmers, drainage communities, and surrounding regions. This project will develop an integrated decision-making platform to facilitate community decision making for precise prediction and management of drainage effects on water flow, crop production, farm net returns, and nutrient loss. The platform data will be made possible by new agricultural sensors and robots, innovations in behavioral economics and analytics tools. Development of the drainage decision-making platform will be guided by farmer stakeholders—including, the Iowa and Illinois Drainage Districts Associations, a national-level agricultural drainage management coalition, and directly with farmers—forming a continuous learning environment across scientists and farmers that fosters adoption of new technologies and transfer of the research process to the next generation of scientists, engineers, and agricultural professionals.

The project will build upon a suite of biophysical and social science advances in multiple areas, including bioinspired robotic snake sensors, in-situ soil nutrient sensors, computational modeling, and socioeconomics. The snake sensors will navigate through agricultural drainage networks to generate a high spatial resolution data stream about flow rates and nitrate concentrations throughout the belowground network. The soil sensors will enable continuous monitoring of nitrate dynamics. Process-based ecohydrological models, subsurface water transport models, and multiple spatiotemporal sensor outputs will be integrated to obtain high-resolution information about distributions of water and nitrate. Biophysical scenario analyses will assist decision-making for different agricultural management scenarios to balance resource use efficiency, profitability, and environmental performance. Socioeconomic science innovations will be integrated by learning how current systems are managed in the context of various heterogeneities across individuals and drainage districts, such as demographics, farm size, and presence of wetlands, and how new information provided by the proposed infrastructure interacts with human incentives and choices and consequent policy making.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.