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All Journal Journal of Robotics and Control (JRC) Journal of Fuzzy Systems and Control (JFSC)
Nguyen, Thanh-Trung
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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Multiple Targets Path Planning for Document Delivering Mobile Robot in Dynamic Environments Vu, Van-Phong; Nguyen, Dinh-Hieu; Nguyen, Thanh-Trung; Tran, Minh-Duc
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26660

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.
Co-Authors Dang, Thai-Bao Huynh, Nguyen-Cuong Huynh, Thi-My-Linh Lai, Nguyen-Ngoc-Truc Le, Thi-Thanh-Hoang Le, Tuong-Vy Luong, Nguyen-Phat Ngo, Kha-Vy Ngo, Truong-Doan-Hy Nguyen, Anh-Tuan Nguyen, Chi-Hung Nguyen, Dinh-Hieu Nguyen, Duc-Huy Nguyen, Minh-Tuan Nguyen, Thi-My Nguyen, Thi-My-Hang Nguyen, Xuan-Hoang Pham, Khanh-Hung Tran, Minh-Duc Tran, Thanh-Danh Tran, Van-Si Truong, Quang-Bao Vo, Thi-Kieu-Tien Vu, Van-Phong