Automation Middleware and Algorithms for Robotic Underwater Sensor Networks(06/01/2008-05/30/2011)

Dr. Fumin Zhang
School of Electrical and Computer Engineering
Technology Square Research Bldg., Room 430A
85 Fifth Street NW
Atlanta, Georgia 30332-0250

Award Number: N00014--08--1--1007

LONG-TERM GOALS

The long term goals of the project are: (1) To establish systems and algorithms for controlled Lagrangian particle tracking that will be used to improve the accuracy of model based prediction of trajectories of controlled underwater vehicles subjected to ocean current. (2) To achieve a mission planning system for robotic underwater sensor networks that are able to perform automatic or semi-automatic adaptation to extreme ocean conditions and platform failure, deployment, and recovery.

OBJECTIVES

We develop a set of automation middleware that  implement a set of novel algorithms for robotic underwater sensor networks serving applications of ocean sampling and ocean model improvement. We design novel model adjustment, cooperative control, and distributed sensing algorithms that will be implemented through the automation middleware. The technical objectives include the following:

1. To investigate a new data assimulation procedure---the controlled Lagrangian particle tracking (CLPT)---and its ability to provide feedback adjustments on ocean modelling systems. To design a validation and adjustment algorithm for ocean models based on CLPT.
2. To develop an automatic middleware that integrates ocean models, robot models, and vehicle control systems towards more accurate prediction of the controlled trajectories of robots in the ocean.
3. To investigate cooperative filters and their ability to improve data quality collected by robotic underwater sensor networks.
4. To design automatic mission planning algorithms for  missions with multiple objectives and multiple resolutions. To design a set of efficient and effective control and navigation algorithms that utilize ocean flow to increase mobility with guaranteed sampling performance.
5. To develop a mission planning and optimization system that automatically generates control laws and mission definitions based on user input about mission goals and constraints.

Figure 1. Similar behaviors of the averaged CLPT error across all three ocean models

WORK COMPLETED

Objectives 1 and 2 achieved. Averaged CLPT errors have been computed for ROMS, NCOM, and HOPS ocean models by comparing glider trajectories in the 2006 ASAP experiment in Monterey Bay, CA with recently regenerated ocean predictions. This process is named as model reanalysis. Model reanalysis reports have been generated for different versions of both the NCOM and the ROMS models.  Such reports allow  ocean modeling teams to test hypothesis made towards improving ocean models, as shown in Figure 1. A rigorous analysis based on the stochastic systems theory has been used to explain the common trend shared by the CLPT error plot of all three models. We have rigorously proved that there exists a lower bound on the steady state CLPT error. Such lower bound is proportional to the grid size of ocean models. Therefore, increasing the spatial resolutions of ocean models will effectively reduce the CLPT error. Our results have been published in two conference papers (Szwaykowska and Zhang 2010, 2011a) with a journal version submitted (Szwaykowska and Zhang, 2011b).

Objective 3 achieved. The cooperative Kalman filtering method has been developed for two dimensional ocean fields. Theoretical results show the method is provably convergent (Zhang and Leonard 2009). A level curve tracking algorithm based on this method has been verified through simulation, see the left figure in Figure 2. We developed autonomy algorithms for mission planning that produces cooperative exploration behaviors under tidal current. See the right figure in Figure 2. Cooperative filtering and cooperative exploration algorithms have been extended to track small features in a three-dimensional field as shown in Figure 3. The number of vehicles have to increase comparing to the two dimensional case, the minimum number of vehicles required have been determined theoretically (Wu and Zhang 2010).

Figure 2. Cooperative exploration behaviors. Snapshots of the robot formation are plotted along the trajectory of the center of the formation. Left: tracking a temperature level curve using four robots. Right: autonomous mission planning under tidal current.

Figure 3. Demonstrations of three dimensional cooperative exploration algorithms.

Objective 4 and 5 achieved.  The middleware developed has been implemented as extensions to the Glider Coordinated Control System (GCCS). GCCS has been used to control two underwater gliders in an NSF funded project in Long Bay, SC.  Various path planning algorithms have been implemented and tested to allow gliders to hold positions near the  gulfstream where flow speed frequently exceeds glider horizontal speed. Figure 4 demonstrate the mission and path planned.

As lab based test-bed, we have developed a remotely operative vehicle (ROV) that has won a design excellence award in the 2009 MATE international ROV competition, and won the Martin Klein MATE Mariner Award in 2011. A robot ship is developed and placed 7th in the 4th AUVSI/ONR International RoboBoat Competition.

Figure 5. Left: A remotely operated vehicle ROV-Beta developed by PI’s undergraduate team. Right: Autonomous Ship Victoria in the 4th AUVSI and ONR International RoboBoat competition.

RESULTS

1. We discovered similar behaviors for all three ocean models regarding the averaged CLPT error in Figure 1. The error first grows exponentially until it reaches twice the grid size. After that, the error grows linearly. By establishing the theory of CLPT, we are able to explain this similarity and generalize this conclusion. We made the discovery that this behavior is unique to controlled Lagrangian particles i.e. marine robots under the guidance of finite resolution ocean models. We also theoretically proved that the bounded error growth is not a repeatable phenomena observed on drifters without active control. Further test and analysis on experimental data are planned in the Long Bay experiment on gliders to confirm our theory.
2. Cooperative filtering and exploration behavior are fundamental building blocks for autonomy of networked unmanned marine systems. Our theoratical developments show that cooperative filtering can be used  to explore both 2D and 3D scalar fields.
3. The automation middleware systems have been proved to be effective and convenient in automatic mission planning and path planning for autonomous marine robots. The middelware systems will see contiue usage and wider adoptions in ocean sensing experiments.

IMPACT/APPLICATIONS

The infrastructure we are developing will lead to the fully automated operation of underwater robotic sensor networks that are persistent and intelligent in a constantly changing ocean environment.  On top of the operation automation that results in autonomy, the data flow in and out of the autonomy is automated. This impacts not only the gathering of data, but also the assimilation of the gathered data and the improvements of ocean models.

REFERENCES

Boyd, S. and L. Vandenberghe (2004), “Convex Optimization,” Cambridge University Press.

Jazwinski, A. H. (1970), “Stochastic Processes and Filtering Theory,” Academic Press, New York.

Leonard, N. E., E. Paley, F. Lekien, R. Sepulchre, D. M. Fratantoni, and R. E. Davis. (2007), “Collective motion, sensor networks, and ocean sampling,”  Proc. of the IEEE, 95:1, 48-74.

Sawaragi, Y., H. Nakayama, et al. (1985), “Theory of Multiobjective Optimization,” New York, NY, Academic Press.

Stengel, R. F. (1994), “Optimal Control and Estimation,” New York, Dover Publications.

Zhang, F., K. Szwaykowska, et al. (2008), “Task Scheduling for Control Oriented Requirements for Cyber-Physical Systems,” Proceedings of RTSS 2008, 47-56.

REFERENCES

Jazwinski, A. H. (1970), “Stochastic Processes and Filtering Theory,” Academic Press, New York.

Leonard, N. E., E. Paley, F. Lekien, R. Sepulchre, D. M. Fratantoni, and R. E. Davis. (2007), “Collective motion, sensor networks, and ocean sampling,”  Proc. of the IEEE, 95:1, 48-74.

Stengel, R. F. (1994), “Optimal Control and Estimation,” New York, Dover Publications.

Zhang, F., K. Szwaykowska, et al. (2008), “Task Scheduling for Control Oriented Requirements for Cyber-Physical Systems,” Proceedings of RTSS 2008, 47-56.

Szwaykowska, K. and F. Zhang (2010). A Lower Bound for Controlled Lagrangian Particle Tracking Error. Proc. 49th IEEE Conference on Decision and Control (CDC 2010), 4353-4358.

Szwaykowska, K. and F. Zhang (2011), “A Lower Bound on Navigation Error for Marine Robots Guided by Ocean Circulation Models ,” in Proc. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), accepted.

Wu, W. and F. Zhang (2011), “Cooperative Exploration of Level Surfaces of Three Dimensional Scalar Fields,” Automatica, the IFAC Journal 47(9) 2044-2051.

Szwaykowska, K. and F. Zhang (2011b), “Trend and Bounds for Error Growth in Controlled Lagrangian Particle Tracking,” IEEE Journal of Oceanic Engineering, Submitted on May 6th, 2011.

PUBLICATIONS

Journal articles:

Kim, J., F. Zhang, and M. Egerstedt (2009), “Curve Tracking Control for Autonomous Vehicles with Rigidly Mounted Range Sensors,” Journal of Intelligent and Robotic Systems,  56(1-2): 177-197. [published, refereed]

Zhang, F. and N. E. Leonard (2010), “Cooperative Control and Filtering for Cooperative Exploration,” IEEE Transactions on Automatic Control, 55(3):650-663, 2010. [published, refereed]

Zhang, F. (2010), “Geometric Cooperative Control of Particle Formations,” IEEE Transactions on Automatic Control, 55(3):800-803, 2010. [published, refereed]

Kim, J., F. Zhang, and M. Egerstedt (2010), “An exploration strategy by constructing Voronoi Diagrams with provable completeness,” Autonomous Robots, 29(3):367-380, 2010. [published, refereed]

Wu, W. and F. Zhang (2010), “Cooperative Exploration of Level Surfaces of Three Dimensional Scalar Fields,” Automatica, the IFAC Journal 47(9) 2044-2051. [published, refereed]

Szwaykowska, K. and F. Zhang (2011b), “Trend and Bounds for Error Growth in Controlled Lagrangian Particle Tracking,” IEEE Journal of Oceanic Engineering. [submitted, refereed]

Refereed Conference Proceedings:

Kim, J., F. Zhang, et al. (2008), “Curve Tracking Control for Autonomous Vehicles with Rigidly Mounted Range Sensors,” in Proc. 47th IEEE conference on decision and control, 5036-5041.[published, refereed]

Zhang, F., K. Szwaykowska, et al. (2008), “Task Scheduling for Control Oriented Requirements for Cyber-Physical Systems,” in Proc. IEEE Real Time Systems Symposium( RTSS 2008), 47-56. [published, refereed]

Zhang, F., Z. Shi, and W. Wolf (2009), “A dynamic battery model for co-design in cyber-physical systems,” in Proc. of 2nd International Workshop on Cyber-Physical Systems (WCPS 2009), 51-56. [published, refereed]

Szwaykowska K., F. Zhang, and W. Wolf (2009), “Tracking error under time delay and asynchronicity in distributed camera systems,” in Proc. of American Control Conferences(ACC2009), 4886-4891. [published, refereed]

Kim, J., F. Zhang and M. Egerstedt (2009), “Simultaneous Cooperative Exploration and Networking Based on Voronoi Diagrams,” in Proc. 2009 IFAC Workshop on Networked Robotics, 1-6. [published, refereed]

Zhang, F. and  Z. Shi (2009), “Optimal and Adaptive Battery Discharge Strategies for Cyber-Physical Systems,” in Proc.48th  IEEE Conference on Decision and Control, 6232-6237,Shanghai, China, 2009. [published, refereed]

Kim,  J., F. Zhang, and M. Egerstedt (2009), “An exploration strategy based on construction of Voronoi diagrams,” in Proc.48th IEEE Conference on Decision and Control, 7024-7029, Shanghai, China, 2009. [published, refereed]

Yang, H. and F. Zhang (2009), “Geometric Formation Control for Autonomous Underwater Vehicles,” in Proc. 2010 IEEE Conference on Robotics and Automation, 4288-4293, 2009. [published, refereed]

Wu, W. and F. Zhang (2009), “Curvature Based Cooperative Exploration of Three Dimensional Scalar Fields,” in Proc. 2010 American Control Conferences (ACC 2010), 2909-2915. [published, refereed]

Szwaykowska, K. and F. Zhang (2010). A Lower Bound for Controlled Lagrangian Particle Tracking Error. Proc. 49th IEEE Conference on Decision and Control (CDC 2010) 4353-4358. [published, refereed]

Szwaykowska, K and F. Zhang (2011), “A Lower Bound on Navigation Error for Marine Robots Guided by Ocean Circulation Models ,” in Proc. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). [published, refereed]

HONORS/AWARDS/PRIZES
Recipient: Fumin Zhang
Institution: Georgia Institute of Technology
Award: 2009 NSF CAREER Award
Sponsor: National Science Foundation

Recipient: Georgia Tech Savannah ROV team (Led by Fumin Zhang and Justin Shapiro)
Institution: Georgia Institute of Technology
Award: Design elegance award of the 2009 MATE international ROV competition
Sponsor: Marine Advanced Technology Education (MATE) Center

Recipient: Fumin Zhang
Institution: Georgia Institute of Technology
Award: 2010 ONR YIP Award
Sponsor: Office of Naval Research

Recipient: Fumin Zhang
Institution: Georgia Institute of Technology
Award: 2010 Lockheed Inspirational Young Faculty Award
Sponsor: Lockheed Martin Co.

Recipient: Fumin Zhang
Institution: Georgia Institute of Technology
Award: 2011 Roger P. Webb Outstanding Junior Faculty Award
Sponsor: School of Electrical and Computer Engineering, Georgia Tech

Recipient: Fumin Zhang
Institution: Georgia Institute of Technology
Award: Distinguished Lecturer on Cyber-Systems and Control
Sponsor: Zhejiang University, China

Recipient: Georgia Tech Savannah Robotics (Supervised by Fumin Zhang)
Institution: Georgia Institute of Technology
Award: 2011 Martin Klein MATE Mariner Award
Sponsor: Marine Advanced Technology Education (MATE) Center