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A snake-based scheme for path planning and control with constraints by distributed visual sensors

Published online by Cambridge University Press:  09 August 2013

Y. Cheng ,
P. Jiang  and
Y. F. Hu
Y. Cheng*
Affiliation:
School of Engineering, Design and Technology, University of Bradford, Bradford BD7 1DP, UK
P. Jiang
Affiliation:
Department of Computer Science, University of Hull, Hull HU6 7RX, UK
Y. F. Hu
Affiliation:
School of Engineering, Design and Technology, University of Bradford, Bradford BD7 1DP, UK
*
*Corresponding author. E-mail: [email protected]
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Summary

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This paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment. Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is further proposed as a tracker of the reference path, taking into account the robot's non-holonomic constraint and limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance. Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control activities for a vehicle.

Keywords

Snake algorithmWireless visual sensorsMobile robot navigationDistributed robot system
Type
Articles
Information
Robotica , Volume 32 , Issue 4 , July 2014 , pp. 477 - 499
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution-NonCommercial-ShareAlike licence <http://creativecommons.org/licenses/by-nc-sa/3.0/>. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
Copyright © Cambridge University Press 2013

References

1.Siegwart, R. and Nourbakhsh, I. R., Introduction to Autonomous Mobile Robots (MIT Press, Cambridge, MA, 2004).Google Scholar
2.Murphy, R. R., Introduction to AI Robotics (MIT Press, Cambridge, MA, 2000).Google Scholar
3.Ratliff, N., Zucker, M., Bagnell, J. A. and Srinivasa, S., “CHOMP: Gradient Optimization Techniques for Efficient Motion Planning,” IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009) pp. 489–494.Google Scholar
4.Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P. and Schaal, S., “STOMP: Stochastic Trajectory Optimization for Motion Planning, Robotics and Automation,” IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011) pp. 4569–4574.Google Scholar
5.DeSouze, G. N. and Kak, A. C., “Vision for mobile robot navigation: A survey,” IEEE Trans. Pattern Anal. Machine Intell. 24, 237–267 (Feb 2002).Google Scholar
6.Estrin, D., Culler, D. and Pister, K., “Connecting the physical world with pervasive networks,” IEEE Pervasive Comput. 1, 59–69 (2002).Google Scholar
7.Poovendran, R., “Cyber-physical systems: Close encounters between two parallel worlds,” Proc. IEEE 98, 1363–1366 (2010).Google Scholar
8.Ilic, M. D., Xie, L., Khan, U. A. and Moura, J. M. F., “Modeling of future cyber-physical energy systems for distributed sensing and control,” IEEE Trans. Syst. Man Cybern. A 40, 825–838 (2010).CrossRefGoogle Scholar
9.Ha, Y. G., Sohn, J. C., Cho, Y. J. and Yoon, H., “Towards a ubiquitous robotic companion: Design and implementation of ubiquitous robotic service framework,” ETRI J. 27, 666–676 (2005).CrossRefGoogle Scholar
10.Saffiotti, A., Broxvall, M., Gritti, M., LeBlanc, K., Lundh, R., Rashid, J., “The PEIS-ecology project: Vision and results,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France (2008) pp. 2329–2335.Google Scholar
11.Guizzo, E., “Robots with their heads in the clouds,” IEEE Spectr. 48, 16–18 (2011).CrossRefGoogle Scholar
12.Sabri, L., Chibani, A., Amirat, Y. and Zarri, G. P., “Narrative Reasoning for Cognitive Ubiquitous Robots,” Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, CA (2011).Google Scholar
13.Jiang, P., Feng, Z., Cheng, Y., Ji, Y., Zhu, J., Wang, X., Tian, F., Baruch, J. and Hu, F., “A mosaic of eyes,” IEEE Robot. Autom. Mag. 14, 104–113 (2011).CrossRefGoogle Scholar
14.Tacconi, D., Miorandi, D., Carreras, L., Chiti, F. and Fantacci, R., “Using wireless sensor networks to support intelligent transportation systems,” Ad Hoc Netw. 8, 462–473 (2010).Google Scholar
15.Srini, V. P., “A vision for supporting autonomous navigation in urban environments,” Computer 39, 68–77 (2006).CrossRefGoogle Scholar
16.Mpitziopoulos, A., Konstantopoulos, C., Gavalas, D. and Pantziou, G., “A pervasive assistive environment for visually impaired people using wireless sensor network infrastructure,” J. Netw. Comput. Appl. 34, 194–206 (2011).CrossRefGoogle Scholar
17.Li, M., Liu, Y., Wang, J. and Yang, Z., “Sensor Network Navigation Without Locations,” Proceedings 28th IEEE International Conference on Computer Communications, Rio de Janeiro, Brazil (2009) pp. 2419–2427.Google Scholar
18.Howard, A., Mataric, M. J. and Sukhatme, G. S., “Relaxation on a Mesh: A Formalism for Generalized Localization,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Wailea, Hawaii (2001).Google Scholar
19.Huang, C. M. and Fu, L. C., “Multitarget visual tracking based effective surveillance with cooperation of multiple active cameras,” IEEE Trans. Syst. Man Cybern. B 41, 234–247 (Feb 2011).Google Scholar
20.Li, Q. and Rus, D., “Navigation protocols in sensor networks,” ACM Trans. Sensor Netw. 1, 3–35 (2005).Google Scholar
21.Sinopoli, B., Sharp, C., Schenato, L., Schaffert, S. and Sastry, S. S., “Distributed control applications within sensor networks,” Proc. IEEE 91, 1235–1246 (2003).Google Scholar
22.Meystel, A. (Ed.), Nested Hierarchical Control: An Introduction to Intelligent and Autonomous Control (Kluwer, Boston, MA, 1992).Google Scholar
23.Jiang, P., Ji, Y., Wang, X., Zhu, J. and Cheng, Y., “Design of a multiple bloom-filter for distributed navigation routing,” IEEE Trans. Syst. Man Cybern. Syst. Early online access articles (2013).Google Scholar
24.Kass, M., Witkin, A. and Terzopoulos, D., “Snake: Active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).CrossRefGoogle Scholar
25.Quinlan, S. and Khatib, O., “Elastic Bands: Connecting Path Planning and Control,” IEEE International Conference on Robotics and Automation Atlanta, GA, USA (1993) pp. 802–807.Google Scholar
26.McLean, A. and Cameron, S., “The virtual springs method: Path planning and collision avoidance for redundant manipulators,” Int. J. Robot. Res. 15, 300–319 (1996).Google Scholar
27.Cameron, S. (Ed.), Dealing with Geometric Complexity in Motion Planning (Practical Motion Planning in Robotics) (Wiley, New York, 1998) pp. 259–274.Google Scholar
28.Mclean, A. and Cameron, S., “Snake-Based Path Planning for Redundant Manipulators,” Robotics and Automation, IEEE International Conference on, Atlanta, GA, USA (1993) pp. 275–282.Google Scholar
29.Zhou, L. J., Teo, C. L. and Burdet, E., “A Nonlinear Elastic Path Controller for a Robotic Wheelchair,” Third IEEE Conference on Industrial Electronics and Applications, Singapore (2008) pp. 142–147.Google Scholar
30.LaValle, S. M., Planning Algorithms (Cambridge University Press, Cambridge, 2006).Google Scholar
31.Liang, T., Liu, J., Hung, G. and Chang, Y., “Practical and flexible path planning for car-like mobile robot using maximal-curvature cubic spiral,” Robot. Auton. Syst. 52, 312–325 (2005).CrossRefGoogle Scholar
32.Nelson, W. L., “Continuous steering-function control of robot carts,” IEEE Trans. Ind. Electron. 36, 330–337 (1989).CrossRefGoogle Scholar
33.Ge, S. S., Lai, X. and Mamun, A. A., “Sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints,” Robot. Auton. Syst. 55, 513–526 (2007).CrossRefGoogle Scholar
34.Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30, 846–894 (2011).Google Scholar
35.Laumond, J.-P., Jacobs, P. E., Taix, M. and Murray, R. M., “A motion planner for nonholonomic mobile robots,” IEEE Trans. Robot. Autom. 10, 577–593 (1994).CrossRefGoogle Scholar
36.Cheng, Y., Jiang, P. and Hu, Y. F., “A Distributed Snake Algorithm for Mobile Robots Path Planning with Curvature Constraints,” IEEE International Conference on Systems, Man and Cybernetics, Singapore (2008) pp. 2056–2062.Google Scholar
37.Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C., Introduction to Algorithms, 2nd ed. (MIT Press, Cambridge, MA, McGraw-Hill, New York, 2001).Google Scholar
38.Brockett, R., “Asymptotic stability and feedback stabilization,” In: Differential Geometric Control Theory (Brockett, R. W., Millman, R. S. and Sussmann, H. J., eds.) (Birkhauser, Boston, MA, 1983) pp. 181–191.Google Scholar
39.Wit, C. C. D., Khennouf, H., Samson, C. and Sordalen, O. J., “Nonlinear control design for mobile robots,” World Sci. Ser. Robot. Autom. Syst. 11, 121–156 (1993).Google Scholar
40.Godhavn, J. and Egeland, O., “A Lyapunov approach to exponential stabilization of nonholonomic systems in power form,” IEEE Trans. Autom. Control. 42, 1028–1032 (1997).Google Scholar
41.Aguiar, A., Atassi, A. and Pascoal, A., “Regulation of a Nonholonomic Dynamic Wheeled Mobile Robot with Parametric Modeling Uncertainty using Lyapunov Function,” Proceedings of the 39th IEEE Conference on Decision and Control, Sidney, Australia (Dec 2000).Google Scholar
42.De Witt, C. C. and Sordalen, O. J., “Exponential stabilization of mobile robots with nonholonomic constraints,” IEEE Trans. Autom. Control 37, 1791–1797 (1992).Google Scholar
43.Hespanha, J., “Stabilization of Nonholonomic Integrators via Logic-Based Switching,” Proceedings of the 13th World Congress of IFAC, Francisco, CA, USA (1996) 467–472.Google Scholar
44.Koh, K. and Cho, H., “A smooth path tracking algorithm for wheeled mobile robots with dynamic constraints,” J. Intell. Robot. Syst.: Theory Appl. 24, 367–385 (1999).Google Scholar
45.Wit, C. and Samson, C., “Path Following of a 2-DOF Wheeled Mobile Robot Under Path and Input Torque Constraints,” IEEE International Conference on Robotics and Automation, Sacramento, CA (1991) pp. 1142–1147.Google Scholar
46.Prado, M., Simo, A., Carabias, E., Perez, A. and Ezquerro, F., “Optimal velocity planning of wheeled mobile robots on specific paths in static and dynamic environments,” J. Robot. Syst. 20, 737–754 (2003).Google Scholar
47.Slotine, J. J. E. and Li, W., Applied Nonlinear Control (Prentice-Hall, Englewood Cliffs, NJ, 1991).Google Scholar
48.Jirenhed, D. A., Hesslow, G. and Ziemke, T., “Exploring Internal Simulation of Perception in Mobile Robots,” The Fourth European Workshop on Advanced Mobile Robots, Lund, Sweden (2001) pp. 107–113.Google Scholar
49.Gacia, C. E., Prett, D. M. and Morari, M., “Model predictive control: Theory and practice – A survey,” Automatica, 25, 335–348 (1989).Google Scholar
50.Mayne, D. Q., Rawlings, J. B., Rao, C. V. and Scokaert, P. O. M., “Constrained model predictive control: Stability and optimality,” Automatica, 36, 789–814 (2000).CrossRefGoogle Scholar
51.Klancar, G. and Skrjanc, I., “Tracking-error model-based predictive control for mobile robots in real time,” Robot. Auton. Syst. 55, 460–469 (2007).Google Scholar
52.Kuhne, F., Lages, W. F. and da Silva, J. M. G. Jr, “Model Predictive Control of a Mobile Robot Using Linearization,” Proceedings of Mechatronics and Robotics, Aachen, Germany (2004).Google Scholar
53.Kanjanawanishkul, K., Hofmeister, M. and Zell, A., “Path Following with an Optimal Forward Velocity for a Mobile Robot,” 7th IFAC Symposium on Intelligent Autonomous Vehicles, Lecce, Italy (2010) pp. 462–467.Google Scholar
54.Xi, Y. G. and Zhang, C. G., “Rolling path planning of mobile robot in a kind of dynamic uncertain environment,” Acta Autom. Sin. 28, 161–175 (2013).Google Scholar
55.Intel Corporation, “Intel mote 2 engineering platform data sheet,” Rev2.0, 2006 (online document, link: http://wsn.cse.wustl.edu/images/c/cb/Imote2-ds-rev2_2.pdf)Google Scholar
56.Omnivision Technologies Inc., “OV7620 single-chip CMOS VGA color digital camera,” Rev1.3, 2000 (online product specification, link: http://mxhaard.free.fr/spca50x/Doc/Omnivision/OV7620.pdf)Google Scholar
57.Chuang, J. H. and Ahuja, N., “An analytically tractable potential field model of free space and its application in obstacle avoidance,” IEEE Trans. Syst. Man Cybern. B 28, 729–736 (1998).CrossRefGoogle ScholarPubMed