2023/11/17 by Seung-Keol Ryu, Michael Moncton, Ryu, Seung-Keol +5
Computer Science · Engineering · Mathematics · #Aerospace Engineering and Energy Systems #Artificial intelligence #Computer science #Computer vision #Control (management) #Control theory (sociology) #Energy (signal processing) #Mathematical optimization #Mathematics #Motion (physics) #Motion planning #Path (computing) #Planner #Real-time simulation and control systems #Robot #Robotic Path Planning Algorithms #Sampling (signal processing) #Set (abstract data type) #Statistics #Tree (set theory)
paper · pdf · doi:10.48550/arxiv.2311.10915
openalex publication_date 2023/11/17 · openalex created_date 2023/11/22 · openalex updated_date 2026/07/28
In this work, a set of motion primitives is defined for use in an energy-aware motion planning problem. The motion primitives are defined as sequences of control inputs to a simplified four-DOF dynamics model and are used to replace the traditional continuous control space used in many sampling-based motion planners. The primitives are implemented in a Stable Sparse Rapidly Exploring Random Tree (SST) motion planner and compared to an identical planner using a continuous control space. The planner using primitives was found to run 11.0% faster but yielded solution paths that were on average worse with higher variance. Also, the solution path travel time is improved by about 50%. Using motion primitives for sampling spaces in SST can effectively reduce the run time of the algorithm, although at the cost of solution quality.