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Trajectory and temporal planning of a wheeled mobile robot on an uneven surface

Published online by Cambridge University Press:  01 July 2009

Imran Waheed  and
Reza Fotouhi
Imran Waheed
Affiliation:
Mechanical Engineering Department, University of Saskatchewan, Canada
Reza Fotouhi*
Affiliation:
Mechanical Engineering Department, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, CanadaS7N 5A9
*
*Corresponding author. E-mail: reza.fotouhi@usask.ca
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Summary

Computing a realistic velocity profile for a mobile robot is a challenging task due to the large number of kinematic and dynamic constraints involved. In order for a mobile robot to complete its task it must be able to plan and follow a trajectory. It may also be necessary to follow a given velocity profile, depending on the environment. Temporal planning, or following a given velocity profile, can be used to minimize time of motion and to avoid moving obstacles. For example, assuming the mobile robot is a smart wheelchair, it must follow a prescribed path while following a strict speed limit. This paper presents a temporal planning algorithm that is implemented on a wheeled mobile robot to be used in an indoor setting, such as a hospital ward. The path planning stage is accomplished by using cubic spline functions. A trajectory is created by assigning an arbitrary time of 1 s to each segment of the path. This trajectory is made feasible by applying a number of constraints and using a linear scaling technique. When a velocity profile is given, a non-linear time scaling technique is used to fit the mobile robot's linear velocity to the given velocity profile. A method for avoiding moving obstacles is also implemented. Simulation and experimental results showed good agreement with each other. The main contribution of this paper is in developing a temporal planning algorithm, which is capable of moving on an uneven surface (graded non-flat), and its implementation on the mobile robot at the robotics lab in the University of Saskatchewan. This algorithm is computationally very efficient as it requires low computation cost and does not involve major iterations.

Keywords

Wheeled mobile robotsTrajectory planningTemporal planning
Type
Article
Information
Robotica , Volume 27 , Issue 4 , July 2009 , pp. 481 - 498
Copyright
Copyright © Cambridge University Press 2008

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References

1.Waheed, I. and Fotouhi, R., “Trajectory/Temporal Planning of an Intelligent Healthcare Wheeled Mobile Robot,” UVS Canada Conference, Montebello, QC (Nov. 7–10, 2006).Google Scholar
2.Hatase, T., Wakamatsu, Y., Nishimura, H. and Yamamoto, H., “Intelligent wheelchair robot,” Fujitsu 57 (3), 263–268 (2006).Google Scholar
3.Bley, F., Rous, M., Canzler, U. and Kraiss, K.-F., “Supervised Navigation and Manipulation for Impaired Wheelchair Users,” Proceedings of IEEE Conference of Systems, Man and Cybernetics, The Hague, Netherlands, Vol. 3 (Oct. 10–13, 2004) pp. 2790–2796.Google Scholar
4.Pacnik, G., “Voice Operated Intelligent Wheelchair – VOIC,” IEEE International Symposium on Industrial Electronics, Dubrovnik, Croatia (Jun. 20–23, 2005) pp. 1221–1226.Google Scholar
5.Gurtler, C., “Trajectory Planning for Mobile Robots Based on Dynamical Models,” Proceedings of IEEE International Conference on Intelligent Engineering Systems, Budapest, Hungary, (1997) pp. 171–174.Google Scholar
6.Aydin, S. and Temeltas, H., “Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path,” Adv. Rob. 18 (7), 725–748 (2004).CrossRefGoogle Scholar
7.Stentz, A., “Optimal and efficient path planning for unknown and dynamic environments,” Int. J. Rob. Automat. 10 (3), 89–100 (1995).Google Scholar
8.Louste, C. and Liegeois, A., “Path planning for nonholonomic vehicles: A potential viscous fluid field method,” Robotica 20, 291–298 (2002).Google Scholar
9.Yahja, A., Singh, S. and Stentz, A., “An efficient on-line path planner for outdoor mobile robots,” Rob. Autonom. Syst. 32, 129–143 (2000).Google Scholar
10.Cherif, M., “Motion planning for all-terrain vehicles: A physical modeling approach for coping with dynamic and contact interaction constraints,” IEEE Trans. Rob. Automat. 15 (2), 202–218 (1999).Google Scholar
11.Paolo, G., “Technique to analytically formulate and to solve the 2-dimensional constrained trajectory planning problem for a mobile robot,” J. Intell. Rob. Syst. 27 (3), 237–262 (2000).Google Scholar
12.Munoz, V., Ollero, A., Prado, M. and Simon, A., “Mobile Robot Trajectory Planning with Dynamic and Kinematic Constraints” Proceedings of IEEE International Conference on Robotics and Automation, San Diego, CA, Vol. 4 (1994) pp. 2802–2807.Google Scholar
13.Lamiraux, F., Sekhavat, S. and Laumond, J. P., “Motion planning and control for Hilare pulling a trailer,” IEEE Trans. Rob. Automat. 15 (4), 640–652 (1999).Google Scholar
14.Choi, J. S. and Kim, B. K., “Near-time-optimal trajectory planning for wheeled mobile robots with translational and rotational sections,” IEEE Trans. Rob. Automat. 17 (1), 85–90 (2001).Google Scholar
15.Prado, M., Simon, A., Perez, A. and Ezquerro, F., “Effects of terrain irregularities on wheeled mobile robots,” Robotica 21, 143–152 (2003).Google Scholar
16.Kim, K. and Cho, H., “An obstacle avoidance method for mobile robots based on fuzzy decision making,” Robotica 24, 567–578 (2006).Google Scholar
17.Abo-Shanab, R. F. and Sepehri, N., “Dynamic modeling of tip-over stability of mobile manipulators considering the friction effects,” Robotica 23, 189–196 (2005).CrossRefGoogle Scholar
18.Fotouhi, R., Szyszkowski, W. and Nikiforuk, P., “Trajectory planning and speed control for a two-link rigid manipulator,” J. Mech. Des. Trans. ASME 124 (3), 585–589 (2002).Google Scholar
19.Munoz, V. F., Garcia-Cerezo, A. and Cruz, A., “A mobile robots trajectory planning approach under motion restrictions,” Integrat. Comput.-Aided Eng. 6 (4), 331–347 (1999).CrossRefGoogle Scholar
20.Prado, M., Simon, A. and Ezquerro, F., “Velocity, acceleration and deceleration bounds for a time-optimal planner of a wheeled mobile robot,” Robotica 20, 181–193 (2002).CrossRefGoogle Scholar
21.Prado, M., Simon, A., Carabias, E., Perez, A. and Ezquerro, F., “Optimal velocity planning of wheeled mobile robots on specific paths in static and dynamic environments,” J. Rob. Syst. 20 (12), 737–754 (2003).Google Scholar
22.Strang, G., Introduction to Applied Mathematics (Wellesley College, Wellesly, Mass, 1986).Google Scholar
23. ActivMedia Robotics, PowerBot™ AGV Operations Manual (version 5, March 2005).Google Scholar
24.Waheed, I., Experimental Study of a Trajectory/Temporal Planning Algorithm of a Wheeled Mobile Robot M. Sc. Thesis (University of Saskatchewan, Saskatoon, Canada, 2006). http://library2.usask.ca/theses/available/etd-01032007-132908/.Google Scholar
25.Parking, R. E., Applied Robotic Analysis (Prentice Hall, New Jersey, 1991).Google Scholar
26.Waheed, I. and Fotouhi, R., “Trajectory/Temporal Planning of Mobile Robot Manipulators,” Canadian Space Agency Workshop, Montreal, QC (Oct. 17–18, 2006)Google Scholar
27.Gillespie, T. D., Fundamentals of Vehicle Dynamics, Chapter 3 (Society of Automotive Engineers, Warrendale, Pennsylvania, 1992, TL243.G548).Google Scholar
28.Wong, J. Y., Theory of Ground Vehicles, Chapter 1 (John Wiley and Sons, New York; Toronto, 1978).Google Scholar