Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-21T20:34:00.871Z Has data issue: false hasContentIssue false
  • English
  • Français

A vector-format fuzzy logic approach for online robot motion planning in 3D space and its application to underwater robotic vehicle

Published online by Cambridge University Press:  01 May 2006

X. J. Wu ,
J. Tang ,
Q. Li  and
K. H. Heng
X. J. Wu
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
J. Tang*
Affiliation:
Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
Q. Li
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
K. H. Heng
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Due to its inherent advantages such as reasoning in the format of heuristic rules based on human experience and less stringent requirement on environmental description, fuzzy logic is a promising tool for the robot motion planning in 3-dimensional dynamic environment. In general, in the Cartesian space, the variables used in characterizing the motion of a mobile robot, such as position, velocity, and force relative to other objects or coordinate frames, contain both the magnitude and the pointing information. In previous studies, the fuzzy reasoning on the pointing information was often developed based on the decomposition of the pointing vector followed by conventional fuzzy logic technique on individual vector components. Consequently, when multiple pointing variables are involved, the number of fuzzy variables that need to be considered simultaneously becomes large and the rule base may become very complex, which diminishes the advantages of the fuzzy reasoning approach. In this research, we tackle this issue by implementing a new fuzzy reasoning approach based on vector-format fuzzy variables. To achieve this, a set of new membership functions is defined for the vector-format fuzzy variables, followed by the establishment of a series of new vector-based fuzzification, fuzzy inference, and defuzzification procedures. By treating the multidimensional variables as unitary linguistic variables, the number of fuzzy variables in the fuzzy propositions and therefore the scale of the rule base can be reduced considerably. As an application example, the proposed new fuzzy reasoning approach for motion planning is applied to an Underwater Robotics Vehicle (URV) operating in an oceanic environment, where the pointing of the goal and the pointing vectors of the obstacles are treated as vector-type fuzzy variables, which leads to a compact and significantly simplified rule base. The motion planner can successfully guide the URV to move in the complicated dynamic environ-ment in a real-time fashion, which clearly demonstrates the effectiveness and robustness of the new fuzzy logic approach.

Keywords

Motion planningFuzzy logicVector format3D spaceUnderwater robotics vehicle
Type
Article
Information
Robotica , Volume 25 , Issue 3 , May 2006 , pp. 325 - 339
Copyright
Copyright © Cambridge University Press 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Seraji, H. and Howard, A., “Behavior-based robot navigation on challenging terrain: A fuzzy logic approach,” IEEE J. Robot. Autom., 18, 308–321 2002.CrossRefGoogle Scholar
2.Surmann, H. and Peters, L., “MORIA-A Robot with fuzzy controlled behaviour,” Stud. Fuzziness Soft Comput., 61, 343–365 2001.CrossRefGoogle Scholar
3.Tsourveloudis, N. C., Valavanis, K. P. and Hebert, T., “Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic,” IEEE Trans. Robot. Autom., 17, 490–497 2001.CrossRefGoogle Scholar
4.Gracanin, D., Valavanis, K. P., Tsourveloudis, N. C. and Matijasevic, M., “Virtual-environment-based navigation and control of underwater vehicles,” IEEE Robot. Autom. Mag., 6, 52–63 1999.CrossRefGoogle Scholar
5.Sng, H. L., Gupta, G. S. and Messom, G. H., “Strategy for Collaboration in Robot Soccer,” The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA'02), New Zealand 2002 p. 347.Google Scholar
6.Antonelli, G., Chiaverini, S., Finotello, R. and Schiavon, R., “Real-time path planning and obstacle avoidance for RAIS: An autonomous underwater vehicle,” IEEE J. Ocean. Eng., 26, 216–227 2001.CrossRefGoogle Scholar
7.Smith, R. C. and Cheeseman, P., “On the representation and estimation of spatial uncertainty,” Int. J. Robot. Res., 5, 56–68 1987.CrossRefGoogle Scholar
8.Smith, R., Self, M. and Cheeseman, P., “Estimating Uncertain Spatial Relationships in Robotics,” In: Autonomous Robot Vehicles (Springer-Verlag, Berlin, Heidelberg, 1990) pp. 167–193.CrossRefGoogle Scholar
9.Wang, Y. and Chirikjian, G. S., “Error propagation on the Euclidean group with applications to manipulator kinematics,” IEEE Trans. Robot., 22, 591–602 2006.CrossRefGoogle Scholar
10.Raju, G. V. S., Zhou, J. and Kisner, R. A., “Hierarchical fuzzy control,” Int. J. Control, 54, 1201–1216 1991.CrossRefGoogle Scholar
11.Llata, J. R., Sarabia, E. G., Arce, J. and Oria, J. P., “Fuzzy controller for obstacle avoidance in robotic manipulators using ultrasonic sensors,” 5th International Workshop on Advanced Motion Control (AMC'98), Coimbra 1998 pp. 647–652.Google Scholar
12.Grumm, D., “Optimizing Functions Using ASCFIT,” available at: http://www.adass.org/adass/proceedings/adass98/grummdm/.Google Scholar
13.Yen, J. and Langari, R., Fuzzy Logic, Intelligence, Control and Information (Prentice-Hall, Englewood Cliffs, NJ, 1999).Google Scholar
14.Koh, T. H., Lau, M. W. S., Low, E., Seet, G., Swei, S. and Cheng, P. L., “Preliminary Studies of the Modeling and Control of a Twin-Barrel Underactuated Underwater Robotic Vehicle,” 7th International Conference on Control, Automation, Robotics and Vision, Singapore 2002.Google Scholar
15.Fossen, T. I., Guidance and Control of Ocean Vehicles (Wiley, Chichester, England, 1994).Google Scholar
16.Cheng, P. L., Modeling and Control for the Improvement of Operator's Dexterity in the Operation of an Underwater Vehicle (Nanyang Technological University, Singapore, 2000).Google Scholar
17.Mathworks, , Virtual Reality Toolbox User's Guide (The Mathworks, Natick, MA, 2002).Google Scholar