{"ID":2899822,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01198","arxiv_id":"2507.01198","title":"Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives","abstract":"This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space (C-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free C-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.","short_abstract":"This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space (C-space) as adaptive motion primitives within the graph search algorithm. Due to their...","url_abs":"https://arxiv.org/abs/2507.01198","url_pdf":"https://arxiv.org/pdf/2507.01198v1","authors":"[\"Benjamin Kraljusic\",\"Zlatan Ajanovic\",\"Nermin Covic\",\"Bakir Lacevic\"]","published":"2025-07-01T21:33:33Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CG\"]","methods":"[\"LoRA\"]","has_code":false}