Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization

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Abstract
Most approaches to multi-robot control either rely on local decentralized control policies that scale well in the number of agents, or on centralized methods that can handle constraints and produce rich system-level behavior, but are typically computationally expensive and scale poorly in the number of agents, relegating them to offline planning. This work presents a scalable approach that uses distributed trajectory optimization to parallelize computation over a group of computationally-limited agents while handling general nonlinear dynamics and non-convex constraints. The approach, including near-real-time onboard trajectory generation, is demonstrated in hardware on a cable-suspended load problem with a team of quadrotors automatically reconfiguring to transport a heavy load through a doorway.
Funding Information
  • NASA Early Career Faculty (80NSSC18K1503)
  • Stanford-Ford Alliance
  • National Science Foundation (DGE-1656518)

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