ANALYSIS OF EXISTING APPROACHES TO LOCAL NAVIGATION AND CONTROL OF LOGISTICS MOBILE ROBOTS IN DYNAMIC ENVIRONMENTS
https://doi.org/10.5281/zenodo.20569665
Keywords:
logistics mobile robot, dynamic environment, SLAM, local navigation, DWA, TEB, MPC, adaptive control, localization uncertainty, motion safetyAbstract
This study provides a critical analysis of contemporary approaches to local navigation and motion control for logistics mobile robots operating in dynamically changing environments. Logistics mobile robots are increasingly deployed in manufacturing facilities, warehouse systems, service infrastructures, healthcare institutions, and other indoor technological domains to support automated material handling, object delivery, and service-oriented operations. However, the coexistence of mobile robots with humans, moving carts, other robotic platforms, and temporarily appearing obstacles substantially increases the complexity of ensuring safe, stable, and smooth motion. Under such conditions, conventional navigation and control strategies are often limited in maintaining reliable obstacle avoidance, motion continuity, and real-time adaptability to environmental uncertainties. Accordingly, the systematic investigation of SLAM-based localization and mapping, local navigation algorithms, and adaptive motion control methods is of considerable scientific and practical relevance for enhancing the autonomy, robustness, and operational reliability of logistics mobile robots.
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Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics & Automation Magazine, 13(3), 108–117. https://doi.org/10.1109/MRA.2006.1678144
Borenstein, J., & Koren, Y. (1991). The vector field histogram—Fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation, 7(3), 278–288. https://doi.org/10.1109/70.88137
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. https://doi.org/10.1109/TRO.2016.2624754
Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine, 13(2), 99–110. https://doi.org/10.1109/MRA.2006.1638022
Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4(1), 23–33. https://doi.org/10.1109/100.580977
Grisetti, G., Kümmerle, R., Stachniss, C., & Burgard, W. (2010). A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2(4), 31–43. https://doi.org/10.1109/MITS.2010.939925
Labbé, M., & Michaud, F. (2019). RTAB-Map as an open-source LiDAR and visual simultaneous localization and mapping library for large-scale and long-term online operation. Journal of Field Robotics, 36(2), 416–446. https://doi.org/10.1002/rob.21831
Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022). Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, 7(66), eabm6074. https://doi.org/10.1126/scirobotics.abm6074
Macenski, S., & Jambrecic, I. (2021). SLAM Toolbox: SLAM for the dynamic world. Journal of Open Source Software, 6(61), 2783. https://doi.org/10.21105/joss.02783
Macenski, S., Martin, F., White, R., & Ginés Clavero, J. (2020). The Marathon 2: A navigation system. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Macenski, S., Singh, S., Martin, F., & Ginés, J. (2023). Regulated Pure Pursuit for robot path tracking. Autonomous Robots, 47, 685–694. https://doi.org/10.1007/s10514-023-10097-6
Rösmann, C., Hoffmann, F., & Bertram, T. (2017a). Integrated online trajectory planning and optimization in distinctive topologies. Robotics and Autonomous Systems, 88, 142–153. https://doi.org/10.1016/j.robot.2016.11.007
Rösmann, C., Hoffmann, F., & Bertram, T. (2017b). Online trajectory planning in ROS under kinodynamic constraints with timed elastic bands. In A. Koubaa (Ed.), Robot Operating System (ROS): The complete reference (Vol. 2). Springer. https://doi.org/10.1007/978-3-319-54927-9_3
Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT Press.
Yoo, H., Kim, J., & Lee, S. (2025). Improved model predictive control for dynamic obstacle avoidance. Mathematics, 13(22), 3624. https://doi.org/10.3390/math13223624
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