Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency

Safa Jameel Al-Kamil () and Róbert Szabolcsi
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Safa Jameel Al-Kamil: Doctoral School on Safety and Security Sciences, Óbuda University, 1034 Budapest, Hungary
Róbert Szabolcsi: Institute of Mechatronics and Vehicle Engineering, Óbuda University, 1081 Budapest, Hungary

Mathematics, 2024, vol. 12, issue 12, 1-21

Abstract: Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.

Keywords: mobile robotics; path planning; machine learning motion capture; communication; complex environment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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