Hybrid Robot Control
Contact
Dipl.-Ing. Argtim Tika
Gottlieb-Daimler-Str. 42
67663, Kaiserslautern
Phone: +49 (0)631/205-5093
Fax: +49 (0)631/205-4201
argtim.tika(at)mv.uni-kl.de
Funding
Bundesministerium fur
Wirtschaft und Energie
Mixed-integer online task and trajectory planning
- Monolithic integration of both planning layers into a single planning layer
- Iterative inherent robot task and trajectory planning
- Mixed-integer optimization
Multistep Hybrid Controller
- Mixed-integer nonlinear programming (MINLP) problem for inherent task and trajectory planning
- Convex relaxation using auxiliary variables
- Mixed-integer quadratically constrained programming (MIQCP) problem suitable for online applications
Experimental Results
Two Use Cases to demonstrate the performance of the proposed control structures:
- Hierarchic control structure
- Minimum-distance scheduling
- Minimum-time scheduling
- Minimum-time trajectory planning
- Hybrid control Structure
- Minimum-time task and trajectory planning
Use Case 1:
- Pick-and-place tasks involving six objects and six slots
- All objects and slots have the same orientation as the initial gripper
orientation
Use Case 2:
- Pick-and-place tasks involving six objects and six slots
- Objects o1, o3, o5, and slots s2, s4, s6 have a picking, respective filling
orientation rotated by 90° compared to the initial gripper orientation.
Dynamic Parameter Estimation
- With the dynamic, friction and inertia parameters the dynamic model of a robot manipulator can be written as
- Eliminating the parameters with no effect on the dynamic model and regrouping some parameters yields the regression equation
- The regression equation is solved using optimized trajectories and global optimization techniques
Robot following the persistent exitation optimal trajectory used for identification.