Computing multiple guiding paths for samplingbased motion planning Path planning of 3D solid objects leads to search in a sixdimensional configuration space, which can be solved by samplingbased motion planning. The wellknown issue of samplingbased planners is the narrow passage problem, which is caused by the presence of small regions of the configuration space that are difficult to cover by random samples. Guidedbased planners cope with this issue by increasing the probability of sampling along an estimated solution (a guiding path). In the case of sixdimensional configuration space, the guiding path needs to be computed in the configuration space rather than in the workspace. Fast computation of guiding paths can be achieved by solving a similar, yet simpler problem, e.g., by reducing the size of the robot. This results in an approximate solution (path) that is assumed to be located near the solution of the original problem. The guided sampling along this approximate solution may, however, fail if the approximate solution is too far from the desired solution. In this paper, we cope with this problem by sampling the configuration space along multiple approximate solutions. The approximate solutions are computed using a proposed iterative process: after a path (solution) is found, it forms a region where the subsequent search is inhibited, which boosts the search of new solutions. The performance of the proposed approach is verified in scenarios with multiple narrow passages and compared with the stateoftheart planners.

Samplingbased motion planning of 3D solid objects guided by multiple approximate solutions Samplingbased motion planners are often used to solve motion planning problems for robots with many degrees of freedom. These planners explore the related configuration space by random sampling. The wellknown issue of the samplingbased planners is the narrow passage problem. Narrow passages are small collisionfree regions in the configuration space that are, due to their volume, difficult to cover by the random samples. The volume of the narrow passages can be artificially increased by reducing the size of the robot, e.g., by scalingdown its geometry, which increases the probability of placing the random samples into the narrow passages. This allows us to find an approximate solution (trajectory) and use it as a guide to find the solution for a larger robot. Guiding along an approximate solution may, however, fail if this solution leads through such parts of the configuration space that are not reachable or traversable by a larger robot. To improve this guiding process, we propose to compute several approximate solutions leading through different parts of the configuration space, and use all of them to guide the search for a larger robot. We introduce the concept of disabled regions that are prohibited from the exploration using the sampling process. The disabled regions are defined using trajectories already found in the space being searched. The proposed method can solve planning problems with narrow passages with higher success rate than other stateoftheart planners.