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arXiv · 2501.00507

Real-Time Sampling-Based Safe Motion Planning for Robotic Manipulators in Dynamic Environments

Nermin Covic·Bakir Lacevic·Dinko Osmankovic·Tarik Uzunovic
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Abstract

In this paper, we present the main features of Dynamic Rapidly-exploring Generalized Bur Tree (DRGBT) algorithm, a sampling-based planner for dynamic environments. We provide a detailed time analysis and appropriate scheduling to facilitate a real-time operation. To this end, an extensive analysis is conducted to identify the time-critical routines and their dependence on the number of obstacles. Furthermore, information about the distance to obstacles is used to compute a structure called dynamic expanded bubble of free configuration space, which is then utilized to establish sufficient conditions for a guaranteed safe motion of the robot while satisfying all kinematic constraints. An extensive randomized simulation trial is conducted to compare the proposed algorithm to a competing state-of-the-art method. Finally, an experimental study on a real robot is carried out covering a variety of scenarios including those with human presence. The results show the effectiveness and feasibility of real-time execution of the proposed motion planning algorithm within a typical sensor-based arrangement, using cheap hardware and sequential architecture, without the necessity for GPUs or heavy parallelization.

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Nermin Covic, Bakir Lacevic, Dinko Osmankovic, Tarik Uzunovic. 2024-12-31. Real-Time Sampling-Based Safe Motion Planning for Robotic Manipulators in Dynamic Environments. https://doi.org/10.1109/tro.2025.3598119

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