|SUBJECT:||M.S. Thesis Presentation|
|TIME:||Friday, October 9, 2020, 10:00 a.m.|
|TITLE:||Enhancing Payload Capacity with Dual-Arm Manipulation and Adaptable Mechanical Intelligence|
|COMMITTEE:||Dr. Anirban Mazumdar, Co-Chair (ME)
Dr. Stephen Balakirsky, Co-Chair (GTRI)
Dr. Aaron Young (ME)
Dr. Kok-Meng Lee (ME)
Individual manipulators are limited by their total load capacity. This places a fundamental limit on the weight of loads that a single manipulator can move. Cooperative manipulation with two arms has the potential to increase the net load capacity of the system. However, it is critical that proper load sharing takes place between the two arms. If this is not maintained, the load limits of one of the arms can be exceeded and lead to catastrophic failure. Ensuring load sharing can be a challenging controls and coordination problem. In this work, we outline a method that utilizes mechanical intelligence in the form of a whiffletree. This system enables load sharing that is robust to position deviations between the two arms. The whiffletree utilizes pneumatic tool-changers which enable autonomous attachment/detachment. We outline the overall design of a whiffletree for dual arm manipulation. We also illustrate how this type of mechanical intelligence can greatly simplify cooperative control. Lastly, we use physical experiments to illustrate enhanced load capacity. Specifically, we show how two UR5 manipulators can re-position a 7kg load. This load would exceed the capacity of a single arm, and we show that the average forces on each arm remains below this level and are relatively evenly distributed.