Blurb to come
Grants: ONR Awards N000141712690 and N000141612216, NSF CMMI-1462825
Variations in material concentration resulting from a biochemical or radiological contaminant leakage, such as an oil spill in the ocean or a radioactive dispersal in the atmosphere, is dominated by turbulent mixing. The result is a highly anisotropic and unsteady sensory landscape where sensor measurements become the sporadic and intermittent which renders gradient based search strategies highly ineffective. This work develops information based search strategies for autonomous robots to search and localize the source of a biochemical contaminant dispersed in turbulent media. The approach has been validated using state-of-the-art 3D computational fluid models of the 2010 Deep Water Horizon oil spill developed by Dr. Alex Fabregat Tomás at CUNY.
Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited
energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. This project focuses on developing suitable graph search based techniques to compute energy and/or time optimal
paths for AMVs in two- and three-dimensional time-varying flows (2D+1 and 3D+1). This project has contributed novel techniques that can capture the kinematic actuation
constraints on the vehicles in our cost functions, generate optimal paths in different homotopy classes, and employ an adaptive discretization scheme to construct the search graph. Our current efforts are focused on how best to leverage coherent structure information into our strategies.
This work is a collaboration between Dr. Subhrajit Bhattacharya at Lehigh University.
This work leverages the complimentary mobility and sensing capabilities of a network of heterogeneous robots that operate in remote oceanic environments, to efficiently collect information and effectively manage the volume of data collected. We consider the deployment of minimally actuated active drifters or similarly power-constrained mobile sensors. The active drifters periodically offload their sensory information to more capable robotic vehicles routed in a coordinated fashion through the drifter ensemble. Different from their passive counterparts, active drifters can adapt, albeit in a limited fashion, their sampling strategies to maximize information gain. When coupled with more capable autonomous surface, underwater, or even remotely operated vehicles (ASVs, AUVs, or ROVs), active drifters can significantly increase the spatial sampling reach of ASVs, AUVs, and ROVs. On the other hand, ASVs, AUVs, and ROVs can complement the sensing capabilities of active drifters since they have larger sensor payloads and reach regions not easily accessible to the active drifters due to actuation limitations. However, due to their severely limited power budgets, active drifters
must have the ability to plan and execute energy aware motion control and coordination strategies for data harvesting and rendezvous with AVUs and ASVs for data exchange and upload.
This work focuses on developing motion planning and control strategies for teams of mobile sensors with limited actuation capabilities or power budgets, i.e., active drifters, to harvest data and rendezvous with other autonomous vehicles. The proposed paradigm maximizes the impact of small, power constrained mobile sensors by leveraging the surrounding environmental dynamics to reduce their energy requirements. The objectives of this work are:
Success of these endeavors will improve the autonomy and energy efficiency of various marine
platforms, directly affect the human’s abilities to navigate the oceans, increase the energy-efficiency of existing robotic sensor networks, and provide greater situational awareness for marine, coastal, and littoral applications. The research focuses on developing a general stochastic control framework for coordinated energy-aware motion planning and navigation that are important for power constrained unmanned systems. The expected outcomes include:
This is a collaborative effort with Dr. Herbert Tanner’s group at the University of Delaware.
Geophysical fluid dynamics (GFD) is the study of natural large-scale fluid flows, such as oceans, the atmosphere, and rivers. GFD flows are naturally stochastic and aperiodic, yet exhibit coherent structure. Coherent structures are important because they enable the estimation of the underlying geophysical fluid dynamics. While these transport controlling structures in GFD flows are inherently complicated and unsteady, their understanding is necessary for the design of robust underwater vehicle control and the prediction of various physical, chemical, and biological processes that in GFD flows. Nevertheless, the data sets that describe GFD flows are often finite-time and of low resolution and most transport controlling features in fluids are unstable and non-stationary, this renders the problem of “mapping” these features using teams of autonomous vehicles highly challenging.
The goals of this project are to overcome the theoretical and technical challenges and develop a general mathematical and control framework for distributed autonomous sensing and tracking of geophysical fluid dynamics in 2D space over time (2D+1) and in 3D space over time (3D+1). The key idea exploits the capability of the team to cover large regions in physical space to increase the spatio-temporal sampling resolution of the flow field. The data will then be processed in a distributed fashion by the team to obtain a global description of the flow dynamics that can be maintained and updated in real time. Objectives of this work include:
Grants: (ONR) Awards No. N000141211019 and No. N000141310731, NSF IIS-253917, NSF CMMI-1462825, NSF IIS-1724399.
Distributed autonomous assembly of general two (2D) and three dimensional (3D) structures is a complex task requiring robots to have the ability to: 1) sense and manipulate assembly components; 2) interact with the desired structure at all stages of the assembly process; 3) satisfy a variety of precedence constraints to ensure assembly correctness; and 4) ensure the stability and structural integrity of the desired structure throughout the assembly process. While the distributed assembly problem represents a class of tightly-coupled tasks that is of much interest in multi-robot systems, it is also highly relevant to the development of next generation intelligent, flexible, and adaptive manufacturing and automation. In this work, we address the assembly of a three dimensional structures by a team of robots. Specifically, we address the challenges of partitioning a complex assembly task into N loosely coupled tasks each executed by a single robot. Furthermore, we have developed online sensing capabilities that enable the team to determine the state of the structure during assembly to allow for the identification and correction of assembly errors.
This project provided undergraduate and graduate students a unique opportunity to work with an interdisciplinary and international team of researchers on the design and control of multi-agent robotic systems. The technical focus of the collaboration was centered around the design of robust multi-robot coordination strategies for execution of large scale cooperative tasks. Advances in embedded processor and sensor technology in the last thirty years have accelerate the demand for teams of robots in various application domains. Multi-agent robotic systems are particularly well-suited to execute tasks that cover wide geographic ranges, require significant parallelization, and/or depend capabilities that are varied in both quantity and difficulty. Example applications include littoral exploration and surveillance, rainforest health monitoring, autonomous transportation systems, warehouse automation, and hazardous waste clean-up.
Grant: NSF OISE 113011.
Daniel Mox, B.S./M.S. 2015, Drexel University, Ph.D. student at the University of Pennsylvania
Dennis Larkin, M.S. 2015, Drexel University
Emily LeBlanc, B.S. 2014, Temple University, Ph.D. student at Drexel University
James Milligan, B.S./M.S. 2013, Drexel University, SRI