Integration of RRT* and D* Lite with Path Smoothing for Robust Path Planning in a Dynamic Robotic Arm Environment
DOI:
https://doi.org/10.18196/jrc.v6i5.27038Keywords:
Hybrid Path Planning, Sampling-Based Algorithms, Dynamic EnvironmentsAbstract
This paper presents a hybrid trajectory planning framework that integrates the strengths of rapidly-exploring random tree star (RRT), D* Lite (DL) algorithms, and a Gaussian filter to enable efficient and smooth navigation of a two-link robotic arm in dynamic environments. RRT* is employed to generate a globally optimal path from the initial to the goal configurations by exploring the Cartesian workspace while considering kinematic and dynamic constraints, including static obstacles. To handle environmental changes, DL is incorporated for local re-planning, allowing the trajectory to adapt in real-time when obstacles move or new ones appear, thus ensuring continuous path feasibility. The initial path produced by RRT* is incrementally optimized, and any necessary local adjustments are efficiently handled by DL without re-planning the entire path. To further enhance the quality of motion, the Shortcut smoothing + Gaussian filter is applied for path smoothing, resulting in improved trajectory continuity, computational efficiency, and robustness in the presence of dynamic obstacles. This hybrid approach offers the optimality of RRT*, the adaptability of D* Lite, and the smoothness required for practical robotic applications.
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