Distance-Aware Dynamically Weighted Roadmaps for Motion Planning in Unknown Environments

Abstract

The paper presents and evaluates a distance-aware dynamic roadmap (DA-DRM) algorithm as an extension of the dynamic roadmap (DRM) approach. In contrast to previous work, the algorithm is capable of planning collision-free trajectories while considering the distance to obstacles, even in unknown environments, which are perceived by the robot's depth camera system. The algorithm makes use of a voxel distance grid, which is updated based on perceptual information acquired from the robot's perception system. The distance information is considered as a cost factor during the roadmap search and it is considered in a postprocessing step that is used for trajectory smoothing. We evaluate the DA-DRM algorithm in simulation and in a real-world experiments with the humanoid robot ARMAR-III. In addition, we compare our algorithm against the DRM and the RRT-Connect algorithm. The results demonstrate the performance of our algorithm in terms of keeping a safety distance to obstacles, trajectory smoothness as well as the ability to generate solutions in narrow free space.