Home > CSC-OpenAccess Library > Manuscript Information
EXPLORE PUBLICATIONS BY COUNTRIES |
EUROPE | |
MIDDLE EAST | |
ASIA | |
AFRICA | |
............................. | |
United States of America | |
United Kingdom | |
Canada | |
Australia | |
Italy | |
France | |
Brazil | |
Germany | |
Malaysia | |
Turkey | |
China | |
Taiwan | |
Japan | |
Saudi Arabia | |
Jordan | |
Egypt | |
United Arab Emirates | |
India | |
Nigeria |
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuration Eigenspaces
Shyba Zaheer, Tauseef Gulrez
Pages - 14 - 28 | Revised - 31-03-2015 | Published - 30-04-2015
MORE INFORMATION
KEYWORDS
Free-configuration Space, Eigenvector, Motion Planning, Trajectory Planning.
ABSTRACT
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
1 | JANIS, A., & BADE, A. Path planning algorithm in complex environment: A survey. |
A. Al-Odienat and T. Gulrez, “Inverse covariance principal component analysis for power system stability studies,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 22, no. 1, pp. 57–65, 2014. | |
A. Chakravarthy and D. Ghose, “Obstacle avoidance in a dynamic environment:A collision cone approach,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 28, no. 5, pp. 562–574, 1998. | |
“Player stage.” http://playerstage.sourceforge.net/. | |
D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine, vol. 4, no. 1, pp. 23–33, 1997. | |
D. Gallardo, O. Colomina, F. Fl´orez, and R. Rizo, “A genetic algorithm for robust motion planning,” in Tasks and Methods in Applied Artificial Intelligence, pp. 115–121, Springer, 1998. | |
E. Plaku, K. E. Bekris, B. Y. Chen, A. M. Ladd, and E. Kavraki, “Sampling-based roadmap of trees for parallel motion planning,” IEEE Transactions on Robotics, vol. 21, no. 4, pp. 597–608, 2005. | |
H. M. Choset, Principles of robot motion: theory, algorithms, and implementation. MIT press, 2005. | |
I. Jolliffe, Principal component analysis. Wiley Online Library, 2005. | |
J. Borenstein and Y. Koren, “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 278–288, 1991. | |
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 566–580, 1996. | |
M. Tipping and C. Bishop, “Probabilistic principal component analysis, “Journal of the Royal Statistical Society”, Series B, vol. 61, pp. 611–622, 1999. | |
O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” The international journal of robotics research, vol. 5, no. 1,pp. 90–98, 1986. | |
P. Raja and S. Pugazhenthi, Review Optimal path planning of mobile robots: International Journal of Physical Sciences Vol. 7(9), 23, pp. 1314 – 1320, February, 2012 | |
P. Vadakkepat, K. C. Tan, and W. Ming-Liang, “Evolutionary artificial potential fields and their application in real time robot path planning,”in Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, vol. 1, pp. 256–263, IEEE, 2000. | |
P. Vadakkepat, T. H. Lee, and L. Xin, “Application of evolutionary artificial potential field in robot soccer system,” in IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, pp. 2781–2785, IEEE, 2001. | |
R. Al-Hmouz, T. Gulrez, and A. Al-Jumaily, “Probabilistic road maps with obstacle avoidance in cluttered dynamic environment,” in Intelligent Sensors, Sensor Networks and Information Processing Conference, pp. 241–245, IEEE, 2004. | |
R. Kala, K. Warwick Multi-Vehicle Planning using RRT-Connect. Paladyn Journal of Behavioural Robotics, 2(3): 134-144, 2011 | |
S. Tang, W. Khaksar, N. Ismail, and M. Ariffin, “A review on robot motion planning approaches,” Pertanika Journal of Science and Technology, vol. 20, no. 1, pp. 15–29, 2012. | |
S. Zaheer, T. Gulrez, “Beta-eigenspaces for autonomous mobile robotic trajectory outlier detection,” in 2011 IEEE Conference on Technologies for Practical Robot Applications, TePRA, pp. 31–34, 2011. | |
T. Chaudhry, T. Gulrez, A. Zia, and S. Zaheer, “Bezier curve based dynamic obstacle avoidance and trajectory learning for autonomous mobile robots,” in Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, ISDA’10, 2010, pp. 1059–1065. | |
T. Gulrez and A. Tognetti, “A sensorized garment controlled virtual robotic wheelchair,” Journal of Intelligent & Robotic Systems, vol. 74, no. 3-4, pp. 847–868, 2014. | |
T. Gulrez, A. Tognetti, and D. De Rossi, “Sensorized garment augmented 3d pervasive virtual reality system,” in Pervasive Computing, pp. 97– 115, Springer, 2010. | |
T. Gulrez, S. Zaheer, and Y. Abdallah, “Autonomous trajectory learning using free configuration-eigenspaces,” in IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 424– 429, IEEE, 2009. | |
Mr. Shyba Zaheer
T.K.M College of Engineering - India
s.shyba@gmail.com
Dr. Tauseef Gulrez
Virtual and Simulations of Reality (ViSOR) Lab, Department of Computing, Macqaurie University 2109 NSW, Sydney, Australia. - Australia
|
|
|
|
View all special issues >> | |
|
|