Decentralized Adaptive Control for Collaborative Manipulation

Preston Culbertson1, Mac Schwager1

  • 1Stanford University
Nominated for an ICRA 2018 Best Paper award in Robot Manipulation.
The oral presentation is on Tuesday, 22 May 2018 in Great Hall at 15:00-15:15



Awards Session


10:30 - 13:00 | Tue 22 May | podF | TuA@F

Control 1

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This paper presents a design for a decentralized adaptive controller that allows a team of agents to manipulate a common payload in $mathbb{R}^2$ or $mathbb{R}^3$. The controller requires no communication between agents and requires no textit{a priori} knowledge of agent positions or payload properties. The agents can control the payload to track a reference trajectory in linear and angular velocity with center-of-mass measurements, in angular velocity using only local measurements and a common frame, and can stabilize its rotation with only local measurements. The controller is designed via a Lyapunov-style analysis and has proven stability and convergence. The controller is validated in simulation and experimentally with four robots manipulating an object in the plane.

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Ground robots manipulating unknown object

  • Presents decentralized adaptive controller for robotic team to manipulate 2D or 3D objects.
  • Control strategy is decentralized, and requires no communication between agents or a priori payload knowledge.
  • Presents proof of stability and convergence of decentralized control strategy.
  • Control method verified in simulation and experimentally with ground robots.