Hostname: page-component-797576ffbb-tx785 Total loading time: 0 Render date: 2023-12-07T17:00:52.808Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "useRatesEcommerce": true } hasContentIssue false

Speeding Up On-Line Route Scheduling for an Autonomous Robot Through Pre-Built Paths

Published online by Cambridge University Press:  14 July 2020

Raul Alves*
Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil E-mails:,
Josué Silva de Morais
Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil E-mails:,
Keiji Yamanaka
Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil E-mails:,
*Corresponding author. E-mail:


Today, robots can be found helping humans with their daily tasks. Some tasks require the robot to visit a set of locations in the environment efficiently, like in the Traveling Salesman Problem. As indoor environments are maze-like areas, feasible paths connecting locations must be computed beforehand, so they can be combined during the scheduling, which can be impracticable for real-time applications. This work presents an on-line Route Scheduling supported by a Fast Path Planning Method able to adjust pre-built paths. Experiments were carried out with virtual and real robots to evaluate time and quality of tours.

Research Article
© The Author(s), 2020. Published by Cambridge University Press

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.)


Zielinska, T., “History of Service Robots,” Robotics: Concepts, Methodologies, Tools, and Applications (IGI Global, 2014) pp. 114, doi: 10.4018/978-1-4666-4607-0.ch001.Google Scholar
Matai, R., Singh, S. and Mittal, M. L., “Traveling Salesman Problem: An Overview of Applications, Formulations, and Solution Approaches,” In: Traveling Salesman Problem: Theory and Applications. (Davendra, D., ed.) (InTech, 2010) pp. 126.Google Scholar
Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost pathsIEEE Trans. Syst. Sci. Cybern. 4(2), 100107 (1968).CrossRefGoogle Scholar
LaValle, S. M. and Kuffner, J. J., “Rapidly-Exploring Random Trees: Progress and Prospects,” In: Algorithmic and Computational Robotics (Donald, B. R., Lynch, K. M. and Rus, D., eds.) (AK Peters/CRC Press, 2001) pp. 303307.Google Scholar
Macharet, D. G., and Campos, M. F. M., “A survey on routing problems and robotic systems”, Robotica. 36(12), 17811803 (2018).CrossRefGoogle Scholar
Bolourian, N. and Hammad, A., “Path Planning of LiDAR-Equipped UAV for Bridge Inspection Considering Potential Locations of Defects,” In: Advances in Informatics and Computing in Civil and Construction Engineering (Proceedings of the 35th CIB W78 2018 Conference: IT in Design, Construction, and Management) (I. Mutis and T. Hartmann, eds.) (Springer, Cham, 2019) pp. 545552.CrossRefGoogle Scholar
S. MahmoudZadeh, D. W. M. Powers and Zadeh, R. B., “Mission Planning in Terms of Task-Time Management and Routing,” In: Autonomy and Unmanned Vehicles: Augmented Reactive Mission and Motion Planning Architecture (Cognitive Science and Technology) (Springer, Singapore, 2019) pp. 5571.Google Scholar
Arzamendia, M., Gregor, D., Reina, D. G. and Toral, S. L., “A Path Planning Approach of an Autonomous Surface Vehicle for Water Quality Monitoring Using Evolutionary Computation,” In: Technology for Smart Futures (Dastbaz, M., Arabnia, H. and Akhgar, B., eds.) (Springer, Cham, 2018) pp. 5573.CrossRefGoogle Scholar
Noormohammadi-Asl, A. and Taghirad, H. D., “Multi-goal motion planning using traveling salesman problem in belief spaceInf. Sci. 471(1), 164184 (2019)CrossRefGoogle Scholar
Sathiya, V. and Chinnadurai, M., “Evolutionary algorithms-based multi-objective optimal mobile robot trajectory planning”, Robotica. 37(8), 13631382 (2019).CrossRefGoogle Scholar
Abu-Dakka, F. J., Valero, F. J., Suñer, J. L. and Mata, V., “A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments”, Robotica. 33(3), 669683 (2015).CrossRefGoogle Scholar
Azimi, S., Zainal Abidin, M. S. and Mohamed, Z., “Solving an agricultural robot routing problem with binary particle swarm optimization and a genetic algorithm,” Int. J. Mech. Eng. Rob. Res. 7(5), 521527 (2018), doi: 10.18178/ijmerr.7.5.521-527.Google Scholar
Tsilomitrou, O., Evangeliou, N. and Tzes, A., “Mobile Robot Tour Scheduling Acting as Data Mule in a Wireless Sensor Network,” Proceedings of the 5th International Conference on Control, Decision and Information Technologies (IEEE-CoDIT), Thessaloniki, Greece (2018) pp. 327332.Google Scholar
Cheng, J., Miao, Z., Li, B. and Xu, W., “An Improved ACO Algorithm for Mobile Robot Path Planning,” Proceedings of the International Conference on Information and Automation (IEEE-ICIA), Ningbo, China (2016) pp. 963968.Google Scholar
Ghadiry, W., Habibi, J., Aghdam, A. G. and Zhang, Y., “Time-Efficient Trajectory Optimization in Patrolling Problems with Non-Prespecified Depots and Robots,” Proceedings of the 24th Mediterranean Conference on Control and Automation (IEEE-MED), Athens, Greece (2016) pp. 10471052.Google Scholar
Le, C., Pham, H. X. and La, H. M., “A Multi-Robotic System for Environmental Cleaning,” CoRR available at: (2018).Google Scholar
Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition, (Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989).Google Scholar