Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-23T12:44:40.868Z Has data issue: false hasContentIssue false
  • English
  • Français

Path Planning and scheduling for a fleet of autonomous vehicles

Published online by Cambridge University Press:  29 April 2015

Elias Xidias ,
Paraskevi Zacharia  and
Andreas Nearchou
Elias Xidias
Affiliation:
Department of Product and Systems Design Engineering 84100, University of the Aegean, Hermoupolis, Syros, Greece
Paraskevi Zacharia*
Affiliation:
Department of Business Administration, University of Patras, 26 500, Rio, Patras, Greece
Andreas Nearchou
Affiliation:
Department of Business Administration, University of Patras, 26 500, Rio, Patras, Greece
*
*Corresponding author. E-mail: zacharia@mech.upatras.gr
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a new solution approach for managing the motion of a fleet of autonomous vehicles (AVs) in indoor factory environments. AVs are requested to serve a number of workstations (WS) (following a specified desired production plan for materials requirements) while taking into account the safe movement (collisions avoidance) in the shop floor as well as time duration and energy resources. The proposed approach is based on the Bump-Surface concept to represent the 2D environment through a single mathematical entity. The solution of the combined problem of path planning and task scheduling is searched on a higher-dimension B-surface (in our case 3D) in such a way that its inverse image into the robot environment satisfies the given objectives and constraints. Then, a modified Genetic Algorithm (GA) is used to search for a near-optimum solution. The objective of the fleet coordination consists of determining the best feasible paths for the AVs so that all the WS are served at the lowest possible cost. The efficiency of the developed method is investigated and discussed through characteristic simulated experiments concerning a variety of operating environments.

Keywords

Conflicts-freePath planningMotion planningLogisticsService robots
Type
Articles
Information
Robotica , Volume 34 , Issue 10 , October 2016 , pp. 2257 - 2273
Copyright
Copyright © Cambridge University Press 2015 

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. Azariadis, P. N. and Aspragathos, N. A., “Obstacle representation by Bump-surfaces for optimal motion-planning,” Robot. Auton. Syst. 51 (2–3), 129–150 2005.CrossRefGoogle Scholar
2. Berman, S., Schechtman, E. and Edan, Y., “Evaluation of automatic guided vehicle systems,” Robot. Comput.-Integr. Manuf. 25 (3), 123–126 2008.Google Scholar
3. Chen, J.-H. and Ho, S.-Y., “A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm,” Int. J. Mach. Tools Manuf. 45 (7–8), 949–957 2005.CrossRefGoogle Scholar
4. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning (1st Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1989, ISBN:0201157675).Google Scholar
5. Herrero-Pérez, D. and Martínez-Barberá, H., “Modeling distributed transportation systems composed of flexible automated guided vehicles in flexible manufacturing systems,” IEEE Trans. Ind. Inform. 6 (2), 166–180 2010.CrossRefGoogle Scholar
6. Hwang, Y. K. and Ahuja, N., “Gross motion planning – a survey,” ACM Comput. Surv. 24 (3), 219–291 1992.CrossRefGoogle Scholar
7. LaValle, M. S., Planning Algorithms (Cambridge University Press New York, NY, USA, 2006, ISBN:0521862051).CrossRefGoogle Scholar
8. Nearchou, A. C., “The effect of various operators on the genetic search for large scheduling problems,” Int. J. Prod. Econ. 88 (2), 191–203 2004.CrossRefGoogle Scholar
9. Nishi, T. and Maeno, R., “Petri net decomposition approach to optimization of route planning problems for AGV systems,” IEEE Trans. Autom. Sci. Eng. 7 (3), 523–537 2010.CrossRefGoogle Scholar
10. Parker, L. E., “Path Planning and Motion Coordination in Multiple Mobile Robot Teams,” In: Encyclopedia of Complexity and System Science (Meyers, R., ed.) (Springer, 2009).Google Scholar
11. Piegl, L. and Tiller, W., The NURBS Book (Springer-Verlag, Berlin, Heidelberg, 1997).CrossRefGoogle Scholar
12. Ravi Raju, K. and Krishnaiah Chetty, O. V., “Addressing design and control issues of AGV-based FMSs with Petri net aided simulation,” Comput. Integr. Manuf. Syst. 6 (2), 125–134 1993.CrossRefGoogle Scholar
13. Solomon, M., “Algorithms for the vehicle routing and scheduling problem with time windows constraints,” Oper. Res. 35, 254–265 1987.CrossRefGoogle Scholar
14. Tsitsiklis, J. N., “Special cases of travelling salesman and repairman problems with time windows,” Networks 22, 263–282 1992.CrossRefGoogle Scholar
15. Xiao, J. and Michalewicz, Z., “An Evolutionary Computation Approach to Robot Planning and Navigation,” In: Soft Computing in Mechatronics (Hirota, K. and Fukuda, T., eds.) (Springer-Verlag, Heidelberg, Germany, 2000) pp. 117–128.Google Scholar
16. Xidias, E. K. and Azariadis, P. N., “Mission design for a group of autonomous guided vehicles,” Robot. Auton. Syst. 59 (1), 34–43 2011.CrossRefGoogle Scholar
17. Xidias, E. K., Nearchou, A. C. and Aspragathos, N. A., “Vehicle scheduling in 2D shop floor environments,” Industrial Robot 36 (2), 176–183.CrossRefGoogle Scholar
18. Jin, X. and Ray, A., “Navigation of autonomous vehicles for oil spill cleaning in dynamic and uncertain environments,” Int. J. Control 87 (4), 787–801 2014.CrossRefGoogle Scholar