Accelerating Model Predictive Control for Legged Robots through Distributed Optimization
arxiv(2024)
摘要
This paper presents a novel approach to enhance Model Predictive Control
(MPC) for legged robots through Distributed Optimization. Our method focuses on
decomposing the robot dynamics into smaller, parallelizable subsystems, and
utilizing the Alternating Direction Method of Multipliers (ADMM) to ensure
consensus among them. Each subsystem is managed by its own ocp, with ADMM
facilitating consistency between their optimizations. This approach not only
decreases the computational time but also allows for effective scaling with
more complex robot configurations, facilitating the integration of additional
subsystems such as articulated arms on a quadruped robot. We demonstrate,
through numerical evaluations, the convergence of our approach on two systems
with increasing complexity. In addition, we showcase that our approach
converges towards the same solution when compared to a state-of-the-art
centralized whole-body MPC implementation. Moreover, we quantitatively compare
the computational efficiency of our method to the centralized approach,
revealing up to a 75% reduction in computational time. Overall, our approach
offers a promising avenue for accelerating MPC solutions for legged robots,
paving the way for more effective utilization of the computational performance
of modern hardware.
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