3D Simulation of Oil Concentration from a Leak

Information Based Search in Turbulent Flows

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.


Path Planning

Optimal Paths in Flows

Snapshot of a visualization of ocean surface currents for June 2006 through December 2007, generated using data compiled by the NASA Estimating the Circulation and Climate of the Ocean (ECCO) project. Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio https://svs.gsfc.nasa.gov/

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.


  1. D. Kularatne, M. A. Hsieh, and E. Forgoston.  “Using Control to Shape Stochastic Escape and Switching Dynamics,” Chaos 29, 053128 (2019); https://doi.org/10.1063/1.5090113.  Bibtex | PDF
  2. D. Kularatne, S. Bhattacharya, and M. A. Hsieh. “Going With The Flow: A Graph Based Approach to Optimal Path Planning in General Flows,” Autonomous Robots: RSS 2016 Special Issue, 42(7), Oct 2018, pp 1369 — 1387. Bibtex | PDF
  3. D. Kularatne, E. Forgoston, and M. A. Hsieh. “Exploiting Stochasticity for Navigation in Gyre Flows,” in the Proc. of the 2018 Robotics: Science and Systems (RSS 2018), Jun 2018, Pittsburgh, PA USA. Bibtex | PDF
  4. H. Hajieghrary, D. Kularatne, and M. A. Hsieh. “Differential Geometric Approach to Trajectory Planning: Cooperative Transport by a Team of Autonomous Marine Vehicles,” in the Proc. of the 2018 IEEE American Control Conference, Jun 2018, Milwaukee, WI USA. Bibtex | PDF
  5. D. Kularatne, H. Hajieghrary, and M. A. Hsieh. “Optimal Path Planning in Time-Varying Flows with Forecasting Uncertainties,” in the Proc. of the 2018 IEEE International Conference on Robotics and Automation (ICRA2018), May 2018, Brisbane, Australia. Bibtex | PDF
  6. D. Kularatne, S. Bhattacharya, and M. A. Hsieh. “Optimal Path Planning in Time-Varying Flows using Adaptive Discretization,” IEEE Robotics and Automation Letters (RA-L), 3(1), Jan 2018, pp. 458-465.  Bibtex | PDF
  7. D. Kularatne and M. A. Hsieh. “Tracking Attracting Manifolds in Flows,” Autonomous Robots, 41(8), Mar 2017, pp. 1575–1588. Bibtex | PDF
  8. D. Kularatne, S. Bhattacharya, M. A. Hsieh. “Time and Energy Optimal Path Planning on a Flow Field,” in the Proc. of the 2016 Robotics: Science and Systems (RSS2016), Jun 2016, Ann Arbor, MI USA. Bibtex | PDF

Path Planning for a Magnetic Millirobot

For actuation of small-scale robotic systems, magnetic control methods have garnered significant interest since magnetic fields can be selectively applied without affecting non-magnetic materials. Existing approaches for the control of single or multiple microrobots operating in magnetic fields have mostly focused on two aspects of the problem: 1) design of the physical geometry of the robot, and 2) design of devices to manipulate the local magnetic field. Instead of focusing on ways of manipulating the local magnetic field or the physical geometry of the robot, this project investigates how global design of the field topology can be leveraged for control and manipulation of microrobots. A significant advantage of leveraging topological features of the force vector field is that it does not require complete knowledge of the field and yet results in comparable performance to vehicles following optimal paths where the vector field has been explicitly accounted for.

We create a path planning and trajectory following strategy for a magnetic millirobot that leverages the nonlinearities in the external magnetic force field (MFF). The strategy creates a library of candidate MFFs and characterizes their topologies by identifying the unstable manifolds in the workspace. The path planning problem is then posed as a graph search problem where the computed path consists of a sequence of unstable manifolds segments and their associated MFFs. By tracking the robot’s position and sequentially applying the MFFs, the robot navigates along each unstable manifold until it reaches the goal.



  1. A. Mansfield, D. Kularatne, E. Steager, and M. A. Hsieh, “A Topological Approach to Path Planning for a Magnetic Millirobot,” Submitted to Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2020), Under Review, Oct 2020, Las Vegas, NV.

Synchronous Rendezvous in Geophysical Flows

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 eff ectively 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. Di fferent 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:

  1. To develop energy-aware motion control strategies for synchronous rendezvous by leveraging the dynamics of the fluidic environment;
  2. To synthesize distributed synchronous rendezvous strategies for a set of moving rendezvous
    points whose motions are dictated by surrounding flow field;
  3. To develop a stochastic modeling and control framework where the individual average behavior can be specifi ed and tuned to achieve the desired collective targets; and
  4. To validate the proposed strategies using the multi-robot Coherent Structure Testbed (mCoSTe).

Success of these endeavors will improve the autonomy and energy efficiency of various marine
platforms, directly aff ect 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:

  • Energy aware motion planning and control strategies for minimally actuated autonomous vehicles with limited power budgets;
  • New stochastic control tools to enable large collectives of autonomous sensing resources to rendezvous in dynamic environments; and
  • A novel modeling, control, and analysis framework for coordinating large collectives of collaborating autonomous unmanned systems subject to energy constraints.

This is a collaborative effort with Dr. Herbert Tanner’s group at the University of Delaware.

Distributed Sensing and Tracking of Geophysical Flows

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:

  1. Identifying and evaluating key kinematic features that control transport in oceanic surface flows of greatest relevance to autonomous vehicle navigation and control;
  2. Synthesizing distributed sensing and tracking strategies for a class of transport controlling features in 2D, 2D+1, 3D, and 3D+1 flows;
  3. Developing a general mathematical and control framework for teams of autonomous vehicles that leverages key transport controlling features in oceanic flows for improved navigation and monitoring of dynamic and uncertain environments;
  4. Developing a new control and coordination framework that leverages the disparate sensing, mobility, and computational capabilities within a heterogeneous teams of mobile robots for ocean monitoring;
  5. Experimentally validate the proposed strategies via a proof-of-concept robotic system with simulated and realistic flow data.


Two Robots Straddling a Manifold in the Multi-Robot (MR) Tank

Two robots driving on opposite sides of the stable manifold of saddle point in an actual time-invariant flow created in the MR tank. The robots are obtaining measurements of the flow field using their onboard flow sensors which are capable of measuring both the speed and direction of the current relative to the robot’s movements. Global position for the robots are provided using an external OptiTrack motion capture system.

Tracking Lagrangian Coherent Structures

Three-robot manifold/LCS tracking strategy validated on a periodic wind-driven double-gyre model, experimental data, and actual ocean data.

Full simulation run three robots tracking the LCS off the coast of Santa Barbara, CA.


  1. D. Kularatne and M. A. Hsieh. “Tracking Attracting Manifolds in Flows,” Autonomous Robots, 41(8), Mar 2017, pp. 1575–1588. Bibtex | PDF
  2. M. Michini, M. Ani Hsieh, E. Forgoston, and I. B. Schwartz. “Robotic Tracking of Coherent Structures in Flows,” IEEE Trans. on Robotics, Vol. 30, No. 3, 593-603, Jun 2014. Bibtex | PDF
  3. D. Kularatne and M. A. Hsieh. “Tracking Attracting Lagrangian Coherent Structures in Flows,” in the 2015 Robotics: Science and Systems (RSS 2015), July 2015, Rome, Italy. BibTeX | PDF
  4. M. Michini, M. A. Hsieh, E. Forgoston, and I. B. Schwartz, “Experimental Validation of Robotic Manifold Tracking in Gyre-Like Flows,” in the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS’14), Sep 2014, Chicago, IL USA. BibTeX | PDF
  5. M. Michini, H. Rastgoftar, M. A. Hsieh, and S. Jayasuriya, “Distributed Formation Control for Collaborative Tracking of Manifolds in Flows,” in the 2014 American Control Conference (ACC 2014), Jun 2014, Portland, Oregon USA. BibTeX | PDF

Task Partitioning for Distributed Assembly

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.


Autonomous Assembly

Two mobile manipulators assembling a 3-D structure. A planning algorithm partitions the assembly task into N subcomponents that can be executed by individual robots with minimal communication with other robots. See IROS 2011 paper for details.

Robotic assembly of Lincoln-log styled parts.

Municipal Park in Belo Horizonte, Brazil by James Milligan

Multi-Robot Systems for Large Scale Cooperative Tasks

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.

Project Alumni

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