Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-27T14:28:53.717Z Has data issue: false hasContentIssue false

An Efficient Minimum-Time Cooperative Task Planning Algorithm for Serving Robots and Operators

Published online by Cambridge University Press:  22 November 2019

Yong-Hwi Kim
Affiliation:
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. E-mail: [email protected]
Byung Kook Kim*
Affiliation:
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

We establish an efficient minimum-time cooperative task planning algorithm for robots and operators to serve clients. This problem is an extension of the multiple traveling salesman problem and the vehicle routing problem with synchronization constraints, but it is more difficult due to the cooperative tasks of the robots and operators. To find the exact minimum-time task plan with a small tree size, we propose an efficient branch-and-bound with a good initial tree and keen complementary heuristics. Our algorithm is also effective as an approximate algorithm for the many clients problem within the capacity of the computer used. The efficiency of our algorithm is revealed via case studies: telemedical service robots and planetary exploration robots.

Type
Articles
Copyright
Copyright © Cambridge University Press 2019

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

Fikes, R. E. and Nilsson, N. J., “STRIPS: A new approach to the application of theorem proving,” J. Artif. Intell. 2(3), 189208 (1971).CrossRefGoogle Scholar
Fox, M. and Long, D., “PDDL2.1: An extension to PDDL for expressing temporal planning domains,” J. Artif. Intell. Res. 20, 61124 (2003).CrossRefGoogle Scholar
Younes, H. L. S. and Simmons, R. G., “VHPOP: Versatile heuristic partial order planner,” J. Artif. Intell. Res. 20, 405430 (2003).CrossRefGoogle Scholar
Galindo, C., Fernandez, J. A. and Gonzalez, J., “Improving efficiency in mobile robot task planning through world abstraction,” IEEE Trans. Robot. 20(4), 677690 (2004).CrossRefGoogle Scholar
Galindo, C., Fernandez, J. A. and Gonzalez, J., “Multihierarchical interactive task planning: Application to mobile robotics,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 38(3), 785798 (2008).CrossRefGoogle ScholarPubMed
Kvarnstrom, J. and Doherty, P., “TALplanner: A temporal logic based forward chaining planner,” Ann. Math. Artif. Intell. 30, 119169 (2000).CrossRefGoogle Scholar
Garrido, A. and Onaindia, E., “Domain-independent temporal planning in a planning-graph-based approach,” AI Commun. 19(4), 341367 (2006).Google Scholar
Lian, F.-L. and Murray, R., “Cooperative task planning of multi-robot systems with temporal constraints,” In: Proceedings of the 2003 IEEE International Conference Robotics & Automation (ICRA), Taipei, Taiwan (2003) pp. 25042509.Google Scholar
Geng, L., Zhang, Y. F., Wang, J. J., Fuh, J. Y. H. and Teo, S. H., “Cooperative Task Planning for Multiple Autonomous UAVs with Graph Representation and Genetic Algorithm,” In: Proceedings of the 10th IEEE International Conference Control & Automation (ICCA), Hangzhou, China (2013) pp. 394399.Google Scholar
Fortelle, A. L., “Analysis of Reservation Algorithms for Cooperative Planning at Intersections,” In: Proceedings of the 13th IEEE Annual Conference Intelligent Transportation Systems (2010) pp. 445449.Google Scholar
Maini, P. and Sujit, P. B., “On Cooperation Between a Fuel Constrained UAV and a Refueling UGV for Large Scale Mapping Applications,” In: Proceedings of the International Conference Unmanned Aircraft Systems (ICUAS), Denver, Colorado (2015) pp. 13701377.Google Scholar
Baldacci, R. and Mingozzi, A., “A unified exact method for solving different classes of vehicle routing problems,” Math. Program. 120(2), 347380 (2009).CrossRefGoogle Scholar
Sundar, K. and Rathinam, S., “An Exact Algorithm for a Heterogeneous, Multiple Depot, Multiple Traveling Salesman Problem,” In: Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS), Denver, Colorado (2015) pp. 366371.Google Scholar
Singh, A. and Baghel, A. S., “A new grouping genetic algorithm approach to the multiple traveling salesperson problem,” Soft Comput. 13(1), 95101 (2009).CrossRefGoogle Scholar
Li, J., Zhou, M., Sun, Q., Dai, X. and Yu, X., “Colored traveling salesman problem,” IEEE Trans. Cybernetics 45(11), 23902401 (2015).CrossRefGoogle ScholarPubMed
Carter, A. E. and Ragsdale, C. T., “A new approach to solving the multiple traveling salesperson problem using genetic algorithms,” Eur. J. Oper. Res. 175(1), 246257 (2006).CrossRefGoogle Scholar
Valero-Gomez, A., Valero-Gomez, J., Castro-Gonzalez, A. and Moreno, L., “Use of Genetic Algorithms for Target Distribution and Sequencing in Multiple Robot Operation,” In: Proceedings of the 2011 IEEE International Conference Robotics and Biomimetics, Phuket, Thailand (2011) pp. 27182724.Google Scholar
Venkatesh, P. and Singh, A., “Two metaheuristic approaches for the multiple traveling salesperson problem,” Appl. Soft Comput. 26, 7489 (2015).CrossRefGoogle Scholar
Lanzarone, E., Matta, A. and Sahin, E., “Operation management applied to home care services: The problem of assigning human resources to patients,” IEEE Trans. Systems Man Cybern. Part A: Syst. Hum. 42(6), 13461363 (2012).CrossRefGoogle Scholar
Faigl, J., Kulich, M. and Preucil, L., “Goal Assignment using Distance Cost in Multi-Robot Exploration,” In: Proceedings of the 2012 IEEE/RSJ International Conference Intelligent Robots and Systems, Vilamoura, Algarve (2012) pp. 37413746.Google Scholar
Sarin, S., Sherali, H., Judd, J. and Tsai, P., “Multiple asymmetric traveling salesmen problem with and without precedence constraints: Performance comparison of alternative formulations,” Oper Res. 51, 6489 (2014).Google Scholar
Levin, A. and Yovel, U., “Local search algorithm for multiple-depot vehicle routing and for multiple traveling salesman problems with proved performance guarantees,” J. Comp Optim. 28, 726747 (2014).CrossRefGoogle Scholar
Pei, Y. and Mutka, M. W., “STARS: Static Relays for Remote Sensing in multirobot real-time search and monitoring,” IEEE Trans. Parallel Distrib. Syst. 24(10), 20792089 (2013).CrossRefGoogle Scholar
Rouseau, L-M., Gendreau, M. and Pesant, G., “The Synchronized Vehicle Dispatching Problem,” In: Proceedings of Odysseus, Palermo, Italy (2003).Google Scholar
Freling, R., Huisman, D. and Wagelmans, A. P. M., “Models and algorithms for integration of vehicle and crew scheduling,” J. Scheduling 6(1), 6385 (2003).CrossRefGoogle Scholar
Ioachim, J., Desrosiers, J., Sournis, F. and Belanger, N., “Fleet assignment and routing with schedule synchronization constraints,” Eur. J. Oper. Res. 199(1), 7590 (1999).CrossRefGoogle Scholar
Hachemi, N. E., Gendreau, M. and Rousseau, L.-M., “A heuristic to solve the synchronized log-truck scheduling problem,” Comput. Oper. Res. 40(3), 666673 (2011).CrossRefGoogle Scholar
IBM: ILOG CPLEX Optimization Studio 12.7.1: CP Optimizer Online Documentation (2017), Available at http://ibm.biz/COS1271DocumentationGoogle Scholar
Bredstrom, D. and Ronnqvist, M., “Combined vehicle routing and scheduling with temporal precedence and synchronization constraints,” Eur. J. Oper. Res. 191(1), 1929 (2008).CrossRefGoogle Scholar
Afifi, S., Dang, D.-C. and Moukrim, A., “Heuristic solutions for the vehicle routing problem with time windows and synchronized visits,” Optim. Lett. 10(3), 511525 (2016).Google Scholar
Parragh, S. N. and Doerner, K. F., “Solving routing problems with pairwise synchronization constraints,” CEJOR 26(2), 443464 (2018).Google ScholarPubMed
Balas, E. and Toth, P., “ Branch and Bound Methods in the Traveling Salesman Problem,” (John Wiley & Sons, Chichester, UK, 1985).Google Scholar
Valle, S. M. L., Planning Algorithms, (Cambridge University Press, Cambridge, UK, 2014).Google Scholar
Pearl, J., Heuristics: Intelligent Search Strategies for Computer Problem Solving, (Addison-Wesley, Reading, MA, USA, 1984).Google Scholar
Uthus, D., Riddle, P. and Guesgen, H., “DFS* and The Traveling Tournament Problem,” In: Proceedings of the CPAIOR’ 09, LNCS, vol. 5547. (Springer, Berlin, Germany, 2009) pp. 279293.Google Scholar
Uthus, D., Riddle, P. and Guesgen, H., “Solving the traveling tournament problem with iterative-deepening A*. J. Scheduling 15, 601614 (2012).Google Scholar
Korf, R., “Depth-first iterative deepening: an optimal admissible tree search,” Artif. Intell. 27(1), 97109 (1985).Google Scholar
Ritt, M., “A Branch-and-Bound Algorithm with Cyclic Best-First Search for the Permutation Flow Shop Scheduling Problem,” In: Proceedings of IEEE Conference Automation Science and Engineering (CASE), Fort Worth, Texas, USA (2016) pp. 872877.Google Scholar
Lin, Y., Chiu, T. and Su, Y. T., “Optimal and near-optimal resource allocation algorithms for OFSMA networks,” IEEE Trans. Wireless Commun. 8(8), 40664077 (2009).Google Scholar
Saad, S., Jaafar, W. and Jamil, S., “Solving Standard Traveling Salesman Problem and Multiple Traveling Salesman Problem by Using Branch-and-Bound,” In: Proceedings of the 20th National Symposium on Mathematical Science in Putrajaya, Malaysia, AIP Conference Proceedings, vol. 1522 (2013) pp. 14061411.Google Scholar
Tozkapan, A., Kirca, O. and Chung, C., “A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem,” Comput. Oper. Res. 30(2), 309320 (2003).Google Scholar
Yuan, Y., “A Fast Parallel Branch and Bound Algorithm for Treewidth,” In: Proceedings of the 23rd IEEE International Conference Tools with Artificial Intelligence, Boca Raton, Florida, USA (2011) pp. 472479.Google Scholar
Bukchin, Y. and Rabinowitch, I., “A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs,” Eur. J. Oper. Res. 174(1), 492508 (2006).CrossRefGoogle Scholar
Zhang, W., “Depth-First Branch-and-Bound Versus Local Search: A Case Study,” In: Proceedings of AAAI-00, Austin, Texas, USA (2000).Google Scholar
Chong, E. K. P. and Zak, S. H., “ An Introduction to Optimization,” 4th edn., (John Wiley & Sons, New York, USA, 2013).Google Scholar
Kiraly, A. and Abonyi, J., “A novel approach to solve multiple traveling salesmen problem by genetic algorithm,” Computat. Intell. Eng. 313, 141151 (2010).CrossRefGoogle Scholar
Liu, W., Li, S., Zhao, F. and Zheng, A., “An Ant Colony Optimization Algorithm for the Multiple Traveling Salesmen Problem,” In: Proceedings of ICIEA 2009, Xi’an, China (2009) pp. 15331537.Google Scholar
Goodrich, M. T., Tamassia, R. and Mount, D., “ Data Structures & Algorithms,” (John Wiley & Sons, New York, USA, 2011).Google Scholar
Milesi-Ferretti, G. M., Perotti, R. and Rostagno, M., “Electoral systems and public spending,” Quarterly J. Econ. 117(2), 609657 (2002).CrossRefGoogle Scholar
Zhang, W. and Korf, R. E., “Performance of linear-space search algorithms,” Artificial Intelligence 79(2), 241292 (1995).CrossRefGoogle Scholar