In the architectural field a „DREAM architecture enabling to intuitively tune operational parameters of mobile manipulation applications“ (TRL2->5) has been developed. The developed architecture allows the employment of algorithms in a running application in similar way as during simulation (DREAM-Phase) and it also supports the collection of data.
For optimization, existing optimization schemes (Bayesian Optimization) have been made available to the project with the required interfaces (pySMAC). Further investigations have been carried out to deal with large amounts of sparse data sets, these led to methods for „Reducing dimensions of a data vector using background knowledge“ from TRL1->5.
Autonomous Navigation for mobile robots has been one of the key technologies interfaced and further developed in RobDREAM. Significant technological achievements have been made in self-localization „ Localization based on CAD floor plans“ (TRL0->2), which supports also the setup of applications and in self-positioning („Positioning using laser-scan matching“ TRL3->4) of the robots. Further highly successful developments were contributing to a better matching of user intentions and later robot behavior: „ Adaptive Teach-and-Replay” (TRL3->4) and „ Experience-based Navigation according to User Preferences“ (TRL3->5).
For manipulation and grasping numerous devlopments were made for grasp and motion planning. These developments led to significant advances, such as in „ Hierarchical Fingertip Space Grasp Planner“ (TRL3->4) and „ Integrated Grasp Planner“ (TRL1->4) allowing the robot to grasp objects with high flexibility. The motion planning efforts could even be advanced into a state, where commercial exploitation will be possible: „ Reactive planner for bin-picking and visual pick and place“ (TRL4->7) and „ Less programming and planning-Planning with Re-Use“ (TRL2->7).
As perception of the environment is the key for interaction with objects in the surroundings of the robot, also a whole set of technologies has been developed in this field: „ Combined 2D/3D object detection and localisation“ (TRL2->6) allows reliable identification and localization of parts, while the methods for „ Autonomous and assisted object modelling“ (TRL4->7) provide a ground truth in perception optimization („Automatic optimization of pose estimation“, TRL3->6).
Development and advancement of the technology readiness levels of the technologies have been made possible by integrating the developed systems on state of the art mobile manipulation hardware and their evaluation in the realistic manufacturing environment (RME), which re-creates a real use case scenario.
With the achievements made in RobDREAM, the European Robotics sector receives a significant contribution to an increase in market-share. Especially SMEs can benefit and the deployment of robotics to new areas of application is supported. Finally RobDREAM has an impact on the cross-fertilisation between industry and academia as the frameworks for integration and evaluation can be further used in future joint efforts.
