Mobile Manipulation Based on Generic Object Knowledge

Bley, Florian; Schmirgel, Volker; Kraiss, Karl-Friedrich

doi:10.1109/roman.2006.314363

Cited by 17 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their experiment a tabletop scenario with separated objects is used. Bley et al [23] use a configuration of separated objects in the study of using a generic category description for grasping purposes.…”

Section: Related Workmentioning

confidence: 99%

Support relation analysis and decision making for safe robotic manipulation tasks

Mojtahedzadeh

Bouguerra

Schaffernicht

et al. 2015

Robotics and Autonomous Systems

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Support relation analysis and decision making for safe robotic manipulation tasks

Mojtahedzadeh

Bouguerra

Schaffernicht

et al. 2015

Robotics and Autonomous Systems

View full text Add to dashboard Cite

“…The grasping is performed by a compliant lightweight robot arm with parallel gripper. Bley et al [10] propose another approach of grasp selection by fitting learned generic object models to point cloud data. In contrast to our approach they manipulate separated objects.…”

Section: Related Workmentioning

confidence: 99%

Active Recognition and Manipulation for Mobile Robot Bin Picking

Holz

Nieuwenhuisen

Droeschel

et al. 2014

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

Abstract. Grasping individual objects from an unordered pile in a box has been investigated in stationary scenarios so far. In this work, we present a complete system including active object perception and grasp planning for bin picking with a mobile robot. At the core of our approach is an efficient representation of objects as compounds of simple shape and contour primitives. This representation is used for both robust object perception and efficient grasp planning. For being able to manipulate previously unknown objects, we learn object models from single scans in an offline phase. During operation, objects are detected in the scene using a particularly robust probabilistic graph matching. To cope with severe occlusions we employ active perception considering not only previously unseen volume but also outcomes of primitive and object detection. The combination of shape and contour primitives makes our object perception approach particularly robust even in the presence of noise, occlusions, and missing information. For grasp planning, we efficiently pre-compute possible grasps directly on the learned object models. During operation, grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.

show abstract

“…The grasping is performed by a compliant lightweight robot arm with parallel gripper. Bley et al [8] propose another approach of grasp selection by fitting learned generic object models to point cloud data. In contrast to our approach they manipulate separated objects.…”

Section: Related Workmentioning

confidence: 99%

Mobile bin picking with an anthropomorphic service robot

Nieuwenhuisen

Droeschel

Holz

et al. 2013

2013 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-Grasping individual objects from an unordered pile in a box has been investigated in static scenarios so far. In this paper, we demonstrate bin picking with an anthropomorphic mobile robot. To this end, we extend global navigation techniques by precise local alignment with a transport box. Objects are detected in range images using a shape primitivebased approach. Our approach learns object models from single scans and employs active perception to cope with severe occlusions. Grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.

show abstract

Mobile Manipulation Based on Generic Object Knowledge

Cited by 17 publications

References 15 publications

Support relation analysis and decision making for safe robotic manipulation tasks

Support relation analysis and decision making for safe robotic manipulation tasks

Active Recognition and Manipulation for Mobile Robot Bin Picking

Mobile bin picking with an anthropomorphic service robot

Contact Info

Product

Resources

About