Multiple Targets Path Planning for Document Delivering Mobile Robot in Dynamic Environments

Authors

  • Van-Phong Vu Ho Chi Minh City University of Technology and Education
  • Dinh-Hieu Nguyen Ho Chi Minh City University of Technology and Education
  • Thanh-Trung Nguyen Ho Chi Minh City University of Technology and Education
  • Minh-Duc Tran Ho Chi Minh City University of Technology and Education

DOI:

https://doi.org/10.18196/jrc.v6i4.26660

Keywords:

Mobile Robot, Inner Traveling Salesman (ITSP), Ant Colony Algorithm (ACO), Path Planning, SLAM, Dijkstra, DWA, AMCL

Abstract

This paper proposes a method to control and make path planning for a delivering mobile robot that operates in a dynamic environment. The working environment is an office building that will appear both static obstacles and dynamic obstacles (such as such as people or other mobile robots). The mobile robot is designed to carry documents to multiple targets that are determined by users. Users can call the mobile robot and input the information of the documents and targets that need to be delivered via the website. The working environment map will be established by using LiDAR and SLAM technology. The path plaining is executed in two steps. Firstly, the ant colony algorithm (ACO) is employed to solve the indoor traveling salesman problem (ITSP), the TSP for indoor application, for determining the globally optimal moving schedule to multiple targets in this paper. Then, the shortest moving path between point to points for the delivering mobile robot is determined by using the Dijkstra algorithm. The shortest moving path for the delivering mobile robot is determined by using the Dijkstra algorithm. The ant colony algorithm (ACO) is employed to solve the inner traveling salesman problem (ITSP) to determine the optimal moving schedule to multiple targets in this paper. The dynamic window approach (DWA) methodology is applied to assist mobile robots in avoiding static and dynamic obstacles. In addition, the adaptive monte Carlo localization (AMCL) is used for positioning the mobile robot on the map. Finally, the simulation in MATLAB and Gazebo environment as well as the experiments, are presented to prove the superior success of the delivering mobile robot.

References

R. R. Shamshiri and I. A. Hameed, Mobile Robots for Digital Farming. CRC Press, 2024, doi: 10.1201/9781003306283.

P. Asgharian, A. M. Panchea, and F. Ferland, “A Review on the Use of Mobile Service Robots in Elderly Care,” Robotics, vol. 11, no. 6, pp. 1–27, 2022, doi: 10.3390/robotics11060127.

R. Hercik, R. Byrtus, R. Jaros, and J. Koziorek, “Implementation of Autonomous Mobile Robot in SmartFactory,” Applied Sciences (Switzerland), vol. 12, no. 17, p. 8912, 2022, doi: 10.3390/app12178912.

J. Zhang, Q. Gong, Y. Zhang, and J. Wang, “Finite-time global trajectory tracking control for uncertain wheeled mobile robots,” IEEE Access, vol. 8, pp. 187808–187813, 2020, doi: 10.1109/ACCESS.2020.3030633.

E. Slawinski, D. Santiago, and V. Mut, “Dual Coordination for Bilateral Teleoperation of a Mobile Robot with Time Varying Delay,” IEEE Latin America Transactions, vol. 18, no. 10, pp. 1777–1784, 2020, doi: 10.1109/TLA.2020.9387669.

H. Yang, S. Wang, Z. Zuo, and P. Li, “Trajectory tracking for a wheeled mobile robot with an omnidirectional wheel on uneven ground,” IET Control Theory and Applications, vol. 14, no. 7, pp. 921–929, 2020, doi: 10.1049/iet-cta.2019.1074.

Y. T. Liu, R. Z. Sun, T. Y. Zhang, X. N. Zhang, L. Li, and G. Q. Shi, “Warehouse-Oriented Optimal Path Planning for Autonomous Mobile Fire-Fighting Robots,” Security and Communication Networks, vol. 2020, p. 13, 2020, doi: 10.1155/2020/6371814.

X. Zhang, J. Lai, D. Xu, H. Li, and M. Fu, “2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots,” Journal of Advanced Transportation, vol. 2020, p. 14, 2020, doi: 10.1155/2020/8867937.

K. Zhu and T. Zhang, “Deep reinforcement learning based mobile robot navigation: A review,” Tsinghua Science and Technology, vol. 26, no. 5, pp. 674–691, 2021, doi: 10.26599/TST.2021.9010012.

J. Wang, B. Ma, and K. Yan, “Mobile Robot Circumnavigating an Unknown Target Using Only Range Rate Measurement,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 2, pp. 509–513, 2022, doi: 10.1109/TCSII.2021.3082195.

F. Sun, Y. Chen, Y. Wu, L. Li, and X. Ren, “Motion Planning and Cooperative Manipulation for Mobile Robots With Dual Arms,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 6, pp. 1345–1356, 2022, doi: 10.1109/TETCI.2022.3146387.

Z. Wang, P. Li, Q. Li, Z. Wang, and Z. Li, “Motion Planning Method for Car-Like Autonomous Mobile Robots in Dynamic Obstacle Environments,” IEEE Access, vol. 11, pp. 137387–137400, 2023, doi: 10.1109/ACCESS.2023.3339539.

H. Cao et al., “Safe Reinforcement Learning-Based Motion Planning for Functional Mobile Robots Suffering Uncontrollable Mobile Robots,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 5, pp. 4346–4363, 2024, doi: 10.1109/TITS.2023.3330183.

S. Hong and D. Park, “ML-Based Fast and Precise Embedded Rack Detection Software for Docking and Transport of Autonomous Mobile Robots Using 2-D LiDAR,” IEEE Embedded Systems Letters, vol. 16, no. 4, pp. 401–404, 2024, doi: 10.1109/LES.2024.3442927.

G. Grisetti, C. Stachniss, and W. Burgard, “Improved techniques for grid mapping with Rao-Blackwellized particle filters,” IEEE Transactions on Robotics, vol. 23, no. 1, pp. 34–46, 2007, doi: 10.1109/TRO.2006.889486.

J. Qiao, J. Guo, and Y. Li, “Simultaneous localization and mapping (SLAM)-based robot localization and navigation algorithm,” Applied Water Science, vol. 14, no. 7, pp. 1–8, 2024, doi: 10.1007/s13201-024-02183-6.

J. Zhong, D. Kong, Y. Wei, X. Hu, and Y. Yang, “Efficiency-optimized path planning algorithm for car-like mobile robots in bilateral constraint corridor environments,” Robotics and Autonomous Systems, vol. 186, p. 104923, 2025, doi: 10.1016/j.robot.2025.104923.

J. C. Trujillo, R. Munguia, J. C. Albarrán, and M. Arteaga, “Control and monocular visual SLAM of nonholonomic mobile robots,” European Journal of Control, vol. 82, p. 101171, 2025, doi: 10.1016/j.ejcon.2024.101171.

Z. An, C. Li, Y. Han, and M. Niu, “Improved Bidirectional JPS Algorithm for Mobile Robot Path Planning in Complex Environments,” Computers, Materials and Continua, vol. 83, no. 1, pp. 1347–1366, 2025, doi: 10.32604/cmc.2025.059037.

Yujin and G. Xiaoxue, “Optimal Route Planning of Parking Lot Based on Dijkstra Algorithm,” in Proceedings - 2017 International Conference on Robots and Intelligent System, ICRIS 2017, pp. 221–224, 2017, doi: 10.1109/ICRIS.2017.62.

S. Kim, H. Jin, M. Seo, and D. Har, “Optimal Path Planning of Automated Guided Vehicle using Dijkstra Algorithm under Dynamic Conditions,” in 2019 7th International Conference on Robot Intelligence Technology and Applications, RiTA 2019, pp. 231–236, 2019, doi: 10.1109/RITAPP.2019.8932804.

H. Afrisal, A. Syakur, M. A. Riyadi, G. K. Nugraha, A. A. Nanda, and I. Setiawan, “Experimental Analysis on Path Planning Strategy using Dijkstra Algorithm and Follow the Carrot Method for Indoor Mobile Robot Navigation,” in Proceedings - International Conference on Informatics and Computational Sciences, pp. 193–198, 2021, doi: 10.1109/ICICoS53627.2021.9651862.

D. D. Zhu and J. Q. Sun, “A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute,” IEEE Access, vol. 9, pp. 19761–19775, 2021, doi: 10.1109/ACCESS.2021.3053169.

Y. Su, J. Xin, and C. Sun, “Dynamic Path Planning for Mobile Robots Based on Improved RRT∗ and DWA Algorithms,” IEEE Transactions on Industrial Electronics, 2025, doi: 10.1109/TIE.2025.3546349.

Y. Luo, S. Lin, Y. Wang, and K. Liang, “Optimized Path Planning for Autonomous Guided Vehicle Through Fusion of Improved A* and Dynamic Window Approach,” IEEE Access, vol. 13, pp. 68577–68586, 2025, doi: 10.1109/ACCESS.2025.3561005.

D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics and Automation Magazine, vol. 4, no. 1, pp. 23–33, 1997, doi: 10.1109/100.580977.

Y. Li et al., “A Mobile Robot Path Planning Algorithm Based on Improved A∗ Algorithm and Dynamic Window Approach,” IEEE Access, vol. 10, pp. 57736–57747, 2022, doi: 10.1109/ACCESS.2022.3179397.

X. Wu, K. Tu, and J. Jiang, “Study on Improved Dynamic Window Approach for Mobile Robot Under Narrow Channel,” in 2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023, pp. 139–142, 2023, doi: 10.1109/RICAI60863.2023.10489376.

M. Dobrevski and D. Skocaj, “Dynamic Adaptive Dynamic Window Approach,” IEEE Transactions on Robotics, vol. 40, pp. 3068–3081, 2024, doi: 10.1109/TRO.2024.3400932.

S. Thrun, Probabilistic robotics, vol. 45, no. 3. MIT Press, 2002, doi: 10.1145/504729.504754.

W. Xiaoyu, L. Caihong, S. Li, Z. Ning, and F. U. Hao, “On adaptive monte carlo localization algorithm for the mobile robot based on ROS,” in Chinese Control Conference, CCC, vol. 2018, pp. 5207–5212, 2018, doi: 10.23919/ChiCC.2018.8482698.

G. Puga, N. Espinosa, M. Hidalgo, O. García, and I. Paunovic, “Beluga: A Modern Monte Carlo Localization Package for ROS and ROS 2,” in Springer Proceedings in Advanced Robotics, pp. 126–130, 2024, doi: 10.1007/978-3-031-76424-0_23.

H. Zhu and Q. Luo, “Indoor Localization of Mobile Robots Based on the Fusion of an Improved AMCL Algorithm and a Collision Algorithm,” IEEE Access, vol. 12, pp. 67199–67208, 2024, doi: 10.1109/ACCESS.2024.3399192.

M. A. Chung and C. W. Lin, “An Improved Localization of Mobile Robotic System Based on AMCL Algorithm,” IEEE Sensors Journal, vol. 22, no. 1, pp. 900–908, 2022, doi: 10.1109/JSEN.2021.3126605.

L. Yu, M. Li, and G. Pan, “Indoor Localization Based on Fusion of AprilTag and Adaptive Monte Carlo,” in IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021, pp. 464–468, 2021, doi: 10.1109/ITNEC52019.2021.9587205.

Y. Li, L. Jiang, B. Tang, Y. Guo, B. Lei, and H. Liu, “Adaptive Monte Carlo Localization in Unstructured Environment via the Dimension Chain of Semantic Corners,” IEEE Transactions on Industrial Informatics, vol. 20, no. 7, pp. 9714–9724, 2024, doi: 10.1109/TII.2024.3385836.

M. Ibrahim, M. Salem, I. Hossain, A. Sarbabidya, and S. K. Saha, “Autonomous robot navigation with SLAM, AMCL, and real-time user interaction for task management and identity verification,” in 2024 IEEE 3rd International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024 - Proceedings, pp. 252–257, 2024, doi: 10.1109/RAAICON64172.2024.10928347.

W. Huang and J. Yuan, “Improvements based on Adaptive Monte Carlo Localization,” in 2024 5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024, pp. 103–106, 2024, doi: 10.1109/ISPDS62779.2024.10667543.

A. De Mello Gai, S. Bevilacqua, A. R. Cukla, and D. F. T. Gamarra, “Evaluation on IMU and odometry sensor fusion for a Turtlebot robot using AMCL on ROS framework.,” in Proceedings - 2023 Latin American Robotics Symposium, 2023 Brazilian Symposium on Robotics, and 2023 Workshop of Robotics in Education, LARS/SBR/WRE 2023, pp. 637–642, 2023, doi: 10.1109/LARS/SBR/WRE59448.2023.10332977.

L. P. N. Matias, T. C. Santos, D. F. Wolf, and J. R. Souza, “Path Planning and Autonomous Navigation using AMCL and AD,” in Proceedings - 12th LARS Latin American Robotics Symposium and 3rd SBR Brazilian Robotics Symposium, LARS-SBR 2015 - Part of the Robotics Conferences 2015, pp. 320–324, 2016, doi: 10.1109/LARS-SBR.2015.31.

G. Shang, G. Gu, F. Jiang, and X. Hu, “Research on AMCL Algorithm Coupled with DWA Algorithm in Logistics Scenarios,” in 2024 IEEE International Workshop on Radio Frequency and Antenna Technologies, iWRF and AT 2024, pp. 453–458, 2024, doi: 10.1109/iWRFAT61200.2024.10594703.

J. Weber, M. Heimbach, and M. Schmidt, “Experimental Validation of NDT-AMCL: a Precise and Reliable Localizer for Mobile Robots in Human Crowds Using Normal Distribution Transforms,” in Proceedings - 2024 8th IEEE International Conference on Robotic Computing, IRC 2024, pp. 162–169, 2024, doi: 10.1109/IRC63610.2024.00035.

M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, Nov. 2006, doi: 10.1109/CI-M.2006.248054.

M. Breaban, R. Necula, D. Lucanu, and D. Stamate, “Joint Decision Making in Ant Colony Systems for Solving the Multiple Traveling Salesman Problem,” Procedia Computer Science, vol. 225, pp. 3498–3507, 2023, doi: 10.1016/j.procs.2023.10.345.

M. Liu et al., “A Slime Mold-Ant Colony Fusion Algorithm for Solving Traveling Salesman Problem,” IEEE Access, vol. 8, pp. 202508–202521, 2020, doi: 10.1109/ACCESS.2020.3035584.

B. Song, S. Tang, and Y. Li, “A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments,” Mathematical Biosciences and Engineering, vol. 21, no. 2, pp. 2189–2211, 2024, doi: 10.3934/mbe.2024096.

X. Zeng, Q. Song, S. Yao, Z. Tian, and Q. Liu, “Traveling Salesman Problems with Replenishment Arcs and Improved Ant Colony Algorithms,” IEEE Access, vol. 9, pp. 101042–101051, 2021, doi: 10.1109/ACCESS.2021.3093295.

M. Wang, T. Ma, G. Li, X. Zhai, and S. Qiao, “Ant Colony Optimization with an Improved Pheromone Model for Solving MTSP with Capacity and Time Window Constraint,” IEEE Access, vol. 8, pp. 106872–106879, 2020, doi: 10.1109/ACCESS.2020.3000501.

Y. Liu, S. Guo, S. Tang, J. Song, and J. Zhang, “Path Planning for Robots Based on Adaptive Dual-Layer Ant Colony Optimization Algorithm and Adaptive Dynamic Window Approach,” IEEE Sensors Journal, vol. 25, no. 11, pp. 19694–19708, 2025, doi: 10.1109/JSEN.2025.3557437.

G. Li, C. Liu, L. Wu, and W. Xiao, “A mixing algorithm of ACO and ABC for solving path planning of mobile robot,” Applied Soft Computing, vol. 148, p. 110868, 2023, doi: 10.1016/j.asoc.2023.110868.

F. Sui, X. Tang, Z. Dong, X. Gan, P. Luo, and J. Sun, “ACO+PSO+A*: A bi-layer hybrid algorithm for multi-task path planning of an AUV,” Computers and Industrial Engineering, vol. 175, p. 108905, 2023, doi: 10.1016/j.cie.2022.108905.

K. A. Athira, R. Yalavarthia, T. Saisandeepa, K. S. S. Harshitha, A. Shaa, and D. J. Udayan, “ACO-DTSP Algorithm: Optimizing UAV Swarm Routes with Workload Constraints,” in Procedia Computer Science,

vol. 235, pp. 163–172, 2024, doi: 10.1016/j.procs.2024.04.019.

E. De Kuyffer, W. Joseph, L. Martens, and T. De Pessemier, “Travel route and formation optimization for flocks of drones in package delivery by using an ACO based V-Shape algorithm,” Results in Engineering, vol. 24, p. 103627, 2024, doi: 10.1016/j.rineng.2024.103627.

D. Li, L. Wang, J. Cai, K. Ma, and T. Tan, “Research on Terminal Distance Index-Based Multi-Step Ant Colony Optimization for Mobile Robot Path Planning,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 4, pp. 2321–2337, 2023, doi: 10.1109/TASE.2022.3212428.

V. P. Vu, T. G. T. Nguyen, X. S. Nguyen, V. T. Le, and D. H. Pham, “ITSP/IMTSP-Based Path Planning for Multiple-Mobile Robot System,” IEEE Access, vol. 12, pp. 99183–99200, 2024, doi: 10.1109/ACCESS.2024.3427798.

X. Yao, X. Shi, X. Zhang, and X. Mei, “An Ant Colony Optimization Parameter Tuning Method Based on Uniform Design for Path Planning of Mobile Robots,” in 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022,

pp. 1183–1191, 2022, doi: 10.1109/AEECA55500.2022.9918906.

K. Tomitagawa, A. Anuntachai, S. Chotipant, O. Wongwirat, and S. Kuchii, “Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm,” IEEE Access, vol. 10, pp. 117261–117272, 2022, doi: 10.1109/ACCESS.2022.3219416.

H. J. Kaleybar, M. Davoodi, M. Brenna, and D. Zaninelli, “Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review,” IEEE Access, vol. 11, pp. 68972–68993, 2023, doi: 10.1109/ACCESS.2023.3292790.

Downloads

Published

2025-07-20

How to Cite

[1]
V.-P. Vu, D.-H. Nguyen, T.-T. Nguyen, and M.-D. Tran, “Multiple Targets Path Planning for Document Delivering Mobile Robot in Dynamic Environments”, J Robot Control (JRC), vol. 6, no. 4, pp. 1949–1961, Jul. 2025.

Issue

Section

Articles