Research Papers

Task Assignment and Trajectory Planning Algorithm for a Class of Cooperative Agricultural Robots

[+] Author and Article Information
Ni Li, Charles Remeikas

Department of Mechanical and
Aerospace Engineering,
University of Central Florida,
Orlando, FL 32816

Yunjun Xu

Associate Professor
Department of Mechanical and
Aerospace Engineering,
University of Central Florida,
Orlando, FL 32816
e-mail: yunjun.xu@ucf.edu

Suhada Jayasuriya

Department of Mechanical
Engineering and Mechanics,
Drexel University,
Philadelphia, PA 19104

Reza Ehsani

Citrus Research and Education Center,
University of Florida,
Lake Alfred, FL 33850

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 26, 2013; final manuscript received October 15, 2014; published online December 10, 2014. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 137(5), 051004 (May 01, 2015) (9 pages) Paper No: DS-13-1326; doi: 10.1115/1.4028849 History: Received August 26, 2013; Revised October 15, 2014; Online December 10, 2014

Agricultural field operations, such as harvesting for fruits and scouting for disease, are labor intensive and time consuming. With the recent push toward autonomous farming, a method to rapidly generate trajectories for a group of cooperative agricultural robots becomes necessary. The challenging aspect of solving this problem is to satisfy realistic constraints such as changing environments, actuation limitations, nonlinear heterogeneous dynamics, conflict resolution, and formation reconfigurations. In this paper, a hierarchical decision making and trajectory planning method is studied for a group of agricultural robots cooperatively conducting certain farming task such as citrus harvesting. Within the algorithm framework, there are two main parts (cooperative level and individual level): (1) in the cooperative level, once a discrete reconfiguration event is confirmed and replanning is triggered, all the possible formation configurations and associated robot locations for specific farming tasks will be evaluated and ranked according to the feasibility condition and the cooperative level performance index; and (2) in the individual level, a local pursuit (LP) strategy based cooperative trajectory planning algorithm is designed to generate local optimal cooperative trajectories for agricultural robots to achieve and maintain their desired operation formation in a decentralized manner. The capabilities of the proposed method are demonstrated in a citrus harvesting problem.

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Liu, L., Crowe, T. G., and Roberge, M., 2009, “Sensor-Based Scouting Algorithms for Automatic Sampling Tasks in Rough and Large Unstructured Agricultural Fields,” Trans. ASABE, 52(1), pp. 285–294. [CrossRef]
Billingsley, J., Oetomo, D., and Reid, J., 2009, “Agricultural Robotics,” IEEE Rob. Autom. Mag., 16(4), pp. 16–19. [CrossRef]
Edan, Y., Rogozin, D., Flash, T., and Miles, G., 2000, “Robotic Melon Harvesting,” IEEE Trans. Rob. Autom., 16(4), pp. 831–835. [CrossRef]
Edan, Y., Flash, T., Peiper, U., Shmulevich, I., and Sarig, Y., 1991, “Near-Minimum-Time Task Planning for Fruit-Picking Robots,” IEEE Trans. Rob. Autom., 7(1), pp. 48–56. [CrossRef]
Xie, Y., and Alleyne, A., 2014, “Two Degree of Freedom Control Synthesis With Applications to Agricultural Systems,” ASME J. Dyn. Syst. Meas. Control, 136(5), pp. 051006-1–051006-11. [CrossRef]
http://www.rhea-conference.eu/2014/ (Last accessed on Feb. 20, 2014).
Muraro, R. P., Summary of 2008–2009 Citrus Budget for the Southwest Florida Production Region, University of Florida, IFAS, CREC, Lake Alfred, FL.
Li, M., Imou, K., Wakabayashi, K., and Yokoyama, S., 2009, “Review of Research on Agricultural Vehicle Autonomous Guidance,” Int. J. Agric. Biol. Eng., 2(3), pp. 1–26.
O’Connor, M., Bell, T., Elkaim, G., and Parkinson, B., 1996, “Automatic Steering of Farm Vehicles Using GPS,” 3rd International Conference on Precision Agriculture, Minneapolis, MN, June 23–26, pp. 767–777.
Linker, R., and Blass, T., 2008, “Path-Planning Algorithm for Vehicles Operating in Orchards,” Biosyst. Eng, 101(2), pp. 152–160. [CrossRef]
Moorehead, S. J., Wellington, C. K., Gilmore, B. J., and Vallespi, C., 2012, “Automating Orchards: A System of Autonomous Tractors for Orchard Maintenance,” IEEE/RSJ International Conference on Intelligent Robots and Systems Workshop on Agricultural Robotics, Vilamoura, Portugal.
Hamner, B., Bergerman, M., and Singh, S., 2012, “Specialty Crop Automation with Autonomous Vehicles,” International Conference on Robotics and Automation, St. Paul, MN, May 14–18, pp. 1829–1835.
Shiller, Z., and Gwo, Y. R., 1991, “Dynamic Motion Planning of Autonomous Vehicles,” IEEE Trans. Robot. Autom., 7(2), pp. 241–249. [CrossRef]
Pitla, S. K., Luck, J. D., and Shearer, S. A. S., 2010, “Multi-Robot System Control Architecture (MRSCA) for Agricultural Production,” American Society of Agricultural and Biological Engineers International Meeting, Pittsburgh, PA, June 20–23.
Noguhi, N., Will, J., Ishii, K., and Reid, J., 2002, “Development of Master-Slave Robot System Obstacle Avoidance Algorithm,” Automation Technology for Off-Road Equipment Conference, Chicago, IL, July 26–27, pp. 432–441.
Choi, H. L., Brunet, L., and How, J. P., 2009, “Consensus-Based Decentralized Auctions for Robust Task Allocation,” IEEE Trans. Rob., 25(4), pp. 912–926. [CrossRef]
Ren, W., and Beard, R. W., 2008, Distributed Consensus in Multi-Vehicle Cooperative Control—Theory and Application, Springer-Verlag, London, UK.
Girard, A. R., de Sousa, J. B., and Hedrick, J. K., 2001, “An Overview of Emerging Results in Networked, Multi-Vehicle Systems,” 40th IEEE Conference on Decision and Control, Orlando, FL, Dec. 4–7, pp. 1485–1490.
Xue, D., Yao, J., Chen, G., and Yu, Y. L., 2010, “Formation Control of Networked Multi-Agent Systems,” IET Control Theory Appl., 4(10), pp. 2168–2176. [CrossRef]
Cortes, J., Martinez, S., and Bullo, F., 2006, “Robust Rendezvous for Mobile Autonomous Agents Via Proximity Graphs in Arbitrary Dimensions,” IEEE Trans. Autom. Control, 51(9), pp. 1289–1298. [CrossRef]
Olfati-Saber, R., and Murray, R. M., 2004, “Consensus Problems in Networks of Agents With Switching Topology and Time-Delays,” IEEE Trans. Autom. Control, 49(9), pp. 1520–1533. [CrossRef]
Kim, Y. S., and Mesbahi, M., 2006, “On Maximizing the Second Smallest Eigenvalue of a State Dependent Graph Laplacian,” IEEE Trans. Autom. Control, 51(1), pp.116–120. [CrossRef]
Wang, J., and Xin, M., 2010, “Multi-Agent Consensus Algorithm With Obstacle Avoidance Via Optimal Control Approach,” Int. J. Control, 83(12), pp. 2606–2621. [CrossRef]
Xu, Y., Xin, M., Wang, J., and Jayasuriya, S., 2012, “Hierarchical Control of Cooperative Nonlinear Dynamical Systems,” Int. J. Control, 85(8), pp. 1093–1111. [CrossRef]
Seyboth, G. S., Schmidt, G. S., and Allgower, F., 2012, “Cooperative Control of Linear Parameter-Varying Systems,” American Control Conference, Montreal, Canada, June 27–29, pp. 2407–2412.
Qu, Z., Wang, J., and Hull, R. A., 2008, “Cooperative Control of Dynamical Systems with Application to Autonomous Vehicles,” IEEE Trans. Autom. Control, 53(4), pp. 894–911. [CrossRef]
Dong, W. J., and Farrell, J. A., 2008, “Cooperative Control of Multiple Nonholonomic Mobile Agents,” IEEE Trans. Autom. Control, 53(6), pp.1434–1448. [CrossRef]
Lou, J., Cooper, J., Cao, C., and Pham, K., 2012, “Cooperative Adaptive Control of a Two-Agent System,” American Control Conference, Montreal, Canada, June 27–29, pp. 2413–2418.
Su, H., Chen, G., Wang, X., and Lin, Z., 2010, “Adaptive Second-Order Consensus of Networked Mobile Agents With Nonlinear Dynamics,” Automatica, 46(2), pp. 368–375. [CrossRef]
Karkee, M., 2009, “Modeling, Identification, and Analysis of Tractor and Single Axle toward Implement System,” Dissertation, Iowa State University, Ames, IA.
Xu, Y., Remeikas, C., and Pham, K., 2014, “Local Pursuit Strategy Inspired Cooperative Trajectory Planning Algorithm for a Class of Nonlinear Constrained Dynamical Systems,” Int. J. Control, 87(3), pp. 506–523. [CrossRef]
Blackmore, B. S., Fountas, S., Vougioukas, S., Tang, L., Sørensen, C. G., and Jørgensen, R., 2004, “A Method to Define Agricultural Robot Behaviors,” Mechatronics & Robotics Conference, Aachen, Germany, Sept. 13–17, pp. 1197–1200.
Lee, K. H., Ehsani, R., and Schueller, J. K., 2007, “Forward Movement Synchronization of Two Vehicles in Parallel Using a Laser Scanner,” Appl. Eng. Agric., 23(6), pp. 827–834. [CrossRef]
Udumala Savary, S. K. J., Ehsani, R., Salyani, M., Hebel, M. A., and Bora, G. C., 2011, “Study of Force Distribution in the Citrus Tree Canopy During Harvest Using a Continuous Canopy Shaker,” Comput. Electron. Agric., 76(1), pp. 51–58. [CrossRef]
Pepy, R., Lambert, A., and Mounier, H., 2006, “Path Planning Using a Dynamic Vehicle Model,” Information and Communication Technologies, Damascus, Syria, Apr. 24–28, pp. 781–786.
Ozdal, M. M., and Wong, M. D. F., 2009, “Global Routing Formulation and Maze Routing,” Handbook of Algorithms for Physical Design Automation, C. J. Alpert, D. P. Mehta, and S. S. Sapatnekar, eds., Taylor & Francis Group, Boca Raton, FL.
Cormen, T., Leiserson, C., Rivest, R., and Stein, C., 2009, Introduction to Algorithms, The MIT Press, Cambridge, MA.
Piegl, L., and Tiller, W., 1997, The NURBS Book, 2nd ed., Springer-Verlag, New York.
Rao, C. V., Wright, S. J., and Rawlings, J. B., 1998, “Application of Interior-Point Methods to Model Predictive Control,” J. Optim. Theory Appl., 99(3), pp. 723–757. [CrossRef]
Dunbar, W. B., 2007, “Distributed Receding Horizon Control of Dynamically Coupled Nonlinear Systems,” IEEE Trans. Autom. Control, 52(7), pp. 1249–1263. [CrossRef]
Hristu-Varsakelis, D., and Shao, C., 2004, “Biologically-Inspired Optimal Control: Learning From Social Insects,” Int. J. Control, 77(18), pp. 1549–1566. [CrossRef]
Fahroo, F., and Ross, I. M., 2001, “Costate Estimation by a Legendre Pseudospectral Method,” J. Guid. Control Dynam., 24(2), pp. 270–275. [CrossRef]
http://www.oxbocorp.com/Products/Citrus.aspx (Last accessed on Mar. 3, 2014).


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Fig. 1

Algorithm architecture

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Fig. 2

Citrus orchard layout used in the simulation

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Fig. 3

Three available formation configuration options in the simulation

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Fig. 4

Generated trajectories for the followers and virtual leader in phases 1 through 7. (a) Phase 1 through phase 4 and (b) phase 5 through phase 7.

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Fig. 5

Followers’ speed profiles during phase 1

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Fig. 6

Followers’ heading angle profiles during phase 1

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Fig. 7

Followers’ SCP profiles during phase 1

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Fig. 8

Followers’ bias profiles in the x-direction and y-direction during phase 1

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Fig. 9

Agricultural robots’ control profiles during phase 1. (a) Tire frictional force Fi,xf,k2+Fi,yf,k2 and (b) aerodynamic resistance force Fi,yr,kin the lateral direction.




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