Graph-Sparse Distributed Predictive Control for Multi-Robot Motion Planning via Edge-Consensus ADMM
Overview
This paper presents a distributed model predictive control (MPC) architecture for collision-avoiding motion planning of planar robots with double-integrator dynamics.
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Environment: simulation
“distributed candidate does not satisfy the direct-execution conditions. Evaluated in C++ simulations involving 8, 16, and 32 robots, the proposed architecture achieves collision-free execution and substantially improves computational scalability while maintaining task-level performance comparable to a centralized complete-graph baseline.”
Exact source · revision 1Source owner not reported
Reported quantity · Robot count: 32 robots · Basis not reported
“distributed candidate does not satisfy the direct-execution conditions. Evaluated in C++ simulations involving 8, 16, and 32 robots, the proposed architecture achieves collision-free execution and substantially improves computational scalability while maintaining task-level performance comparable to a centralized complete-graph baseline.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGraph-Sparse Distributed Predictive Control for Multi-Robot Motion Planning via Edge-Consensus ADMM
This paper presents a distributed model predictive control (MPC) architecture for collision-avoiding motion planning of planar robots with double-integrator dynamics.
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