Abstract
An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.
Highlights
Path-planning for mobile robots involves identifying an efficient path from a start position to a goal position on a given environmental map using online sensor data
To overcome the slow convergence rate and reduce the overall processing time by quickly obtaining an initial solution, we previously developed a skeletonization-informed rapidly exploring random tree (RRT)* (SIRRT*) for mobile robots moving in 2D environments [21]
As the convergence rate of the IRRT* algorithm depends on the quality of the initial solution computed by random sampling of the RRT* over the whole space, the IRRT*’s performance is unstable even when the start and goal positions do not change within the same environment
Summary
Path-planning for mobile robots involves identifying an efficient path from a start position to a goal position on a given environmental map using online sensor data. PRM planners first construct a graph (a roadmap) that covers the entire environment and includes collision-free paths They compute the shortest path from a start position to a goal along the roadmap. To overcome the slow convergence rate and reduce the overall processing time by quickly obtaining an initial solution, we previously developed a skeletonization-informed RRT* (SIRRT*) for mobile robots moving in 2D environments [21]. As the convergence rate of the IRRT* algorithm depends on the quality of the initial solution computed by random sampling of the RRT* over the whole space, the IRRT*’s performance is unstable even when the start and goal positions do not change within the same environment. Earlier global planners have sought to guarantee optimal solutions and reduce computational times; few studies have explored hierarchical path-planning.
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