Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod G.1
Authors: Knobloch, Adrian; Vahrenkamp, Nikolaus; Waechter, Mirko; Asfour, Tamim
Title: 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, which is 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 robots depth camera system. The algorithm makes use of a voxel distance grid which is updated based on perceptual information acquired from the robots perception system. The distance information is considered as a cost factor during the roadmap search and it is considered in a post-processing 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 Rapidly-exploring Random Tree (RRT) 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.
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod G.1
Authors: Knobloch, Adrian; Vahrenkamp, Nikolaus; Waechter, Mirko; Asfour, Tamim
Title: 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, which is 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 robots depth camera system. The algorithm makes use of a voxel distance grid which is updated based on perceptual information acquired from the robots perception system. The distance information is considered as a cost factor during the roadmap search and it is considered in a post-processing 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 Rapidly-exploring Random Tree (RRT) 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.
