6 accepted papers
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 envi
We present a data-driven, bottom-up, deep learning approach to robotic grasping of unknown objects using Deep Convolutional Neural Networks (DCNNs). The approach uses depth images of the scene as its sole input for synthesis of a single-grasp solution during execution, adequately portraying the robo…
In this letter, we present a grasp planner that integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identif
Robotic coverage path planning describes the problem of determining a configuration space trajectory for successively covering a specified workspace target area with the robot's end-effector. Performing coverage path planning for mobile robots further requires solving the problem of robot placement,…
Object shape information is essential for robot manipulation tasks, in particular for grasp planning and collision-free motion planning. But in general a complete object model is not available, in particular when dealing with unknown objects. We propose a method for completing shapes that are only p…
We address the question of how resource-aware concepts can be utilized in motion planning algorithms. Resource-awareness facilitate better resource allocation on global system level, e.g. when a humanoid robot needs to distribute and schedule a wide variety of concurrent algorithms. We present a mot…