Decentralized Admittance Control for a Multi�manipulator System: Theory and Experiments
Graziano Carriero, Monica Sileo, Sebastiano Fregnan, Francesco Pierri, Fabrizio Caccavale, Yiannis Karayiannidis
AI summary
Problem
Centralized control architectures for multi-robot cooperative manipulation suffer from scalability issues, communication bottlenecks, and vulnerability to single-point failures, while managing internal and external interaction wrenches remains challenging in fully distributed settings.
Approach
Each robot uses a consensus-based observer to estimate teammates' wrenches over a directed communication graph, feeding two local admittance filters that independently regulate the object's reference trajectory (external wrenches) and each robot's end-effector motion (internal wrenches).
Key results
- Consensus observers estimate inter-robot wrenches with bounded errors (<5%) despite delays and collisions
- External admittance filter distributes environmental contact forces across the team
- Internal admittance filter minimizes internal stresses and individual robot effort
- Experimental validation on three Franka Panda arms confirms robust wrench reduction and trajectory tracking
Why it matters
Enables scalable, fault-tolerant multi-robot manipulation for heavy or large object handling in environments where centralized control is impractical or unreliable.
Abstract
This paper presents a decentralized control frame- work for cooperative object transportation with multiple robotic manipulators. In particular two admittance schemes are designed in order to regulate external contact wrenches and internal interaction wrenches without a central unit or all- to-all communication. Each manipulator estimates the wrenches exerted by its teammates through a bank of consensus-based observers that exploits a strongly connected communication graph. These estimates feed two local admittance filters: an external filter, computing the reference object trajectory while limiting environmental wrenches, and an internal filter, gen- erating the end-effector trajectory to minimize each robot’s contribution to internal wrenches. Experiments carried out with three 7-DOF Franka Emika Panda arms show a marked re- duction of both external and internal wrenches, demonstrating the effectiveness and robustness of the proposed approach.