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Integrated Analysis of Mapping, Path Planning, and Advanced Motion Control for Autonomous Robotic Navigation
Author(s)
Date Issued
October 1, 2025
Type
Article
Abstract
Autonomous robotic navigation is essential in modern systems for revolutionising various industries that operate in both static and dynamic environments. To achieve this autonomous navigation, various conventional techniques that handle environment mapping, path planning, and motion control as individual modules often face challenges in addressing the complexities of autonomous navigation. Therefore, this paper presents an integrated technique that combines three essential components, such as environment mapping, path planning, and motion control, to enhance autonomous navigation performance. The first component, i.e., the mapping, utilises both binary and probabilistic occupancy maps to represent the environment. The second component is path planning, which incorporates various graph- and sampling-based algorithms such as PRM, A*, Hybrid A*, RRT, RRT*, and BiRRT, which are evaluated in terms of path length, computational time, and safety margin on various maps. The final component, i.e., motion control, utilises both conventional and advanced controller strategies such as PID, FOPID, SFC, and MPC, for better sinusoidal trajectory tracking. The four case studies for path planning and one case study on trajectory tracking on various occupancy maps demonstrated that the A* algorithm and MPC outperformed all the compared techniques in terms of optimal path length, computational time, safety margin, and trajectory tracking error. Thus, the proposed integrated approach reveals that the interplay between mapping fidelity, planning efficiency, and control robustness is vital for reliable autonomous navigation.
Citation
Fractal and Fractional, 9(10), 2025