Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives
This is an incremental improvement for robotic manipulators, enhancing efficiency in motion planning for complex environments.
The paper tackles robot motion planning by introducing adaptive motion primitives called burs that expand in free configuration space, reducing path-finding time and expansion counts, with results showing outperformance over fixed primitives in complex scenarios, especially for high-degree-of-freedom manipulators.
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.