International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 6 , Issue 2 , PP: 73-82, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Hatip 1 * , Karla Zayood 2 , Rabah Scharif 3
Doi: https://doi.org/10.54216/IJAACI.060207
A capability that is indispensable in robotic navigation when it comes to planning paths through dynamic and uncertain environments efficiently and accurately. This work aims at a hybrid stochastic-deterministic path planning by combining the best of both worlds in order to improve robotic navigation. This hybrid model uses stochastic techniques to employ the robustness of uncertainly models, but offers efficient execution with deterministic algorithms for our optimum path solution. The method combines a highly exploratory stochastic sampling-based planner for environmental search with a deterministic optimization component that refines paths generated by the former, enforcing constraints such as minimal traversal distance (energy efficiency), while avoiding obstacles. The integration of those methods targets to override the disadvantages that each purely stochastic or solely deterministic model required, giving a more flexible and robust solution for autonomous vehicle guidance. We use simulation analysis and real-life experimental data to validate the algorithm in comparison with traditional algorithms. The approach performs significantly better, up to an order of magnitude in terms of accuracy and efficiency on navigation as well as robustness against cluttered or dynamic disturbances. These results indicate that the proposed hybrid stochastic-deterministic path-planning algorithm has strong potential to contribute to improving autonomy of robotic navigation systems, especially in highly dynamic and precise applications. The post provides a new framework to improve autonomous navigation of robots for complex environments that can support more efficient, reliable and high-level robotic systems in industrial, household or exploratory settings.
Hybrid Path Planning , Stochastic-Deterministic Navigation , Robotic Navigation , Autonomous Systems , Path Optimization , Obstacle Avoidance , Dynamic Environments , Stochastic Sampling , Deterministic Optimization , Robotic Systems , Computational Efficiency , Navigation Accuracy
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