Distributed Trajectory Optimization

Distributed Trajectory Optimization

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.

The code is available on the “ADMM” branch of TrajectoryOptimization.jl.

dist quad

Related Papers

2020
May
PDF Scalable Cooperative Transport of Cable-Suspended Loads with UAV's using Distributed Trajectory Optimization
Brian Jackson, Taylor Howell, Kunal Shah, Mac Schwager, and Zac Manchester
International Conference on Robotics and Automation (ICRA). Paris, France.

People

Zac Manchester
Assistant Professor
Brian Jackson
Real-time motion planning
Taylor Howell
Robust feedback motion planning and optimization through contact
Last updated: 2019-08-15