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Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier | IEEE Journals & Magazine | IEEE Xplore

Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier


Abstract:

Wepropose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimiza...Show More

Abstract:

Wepropose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized parameter updates. Starting from a collision-free initial trajectory, our method inherits the theoretical properties of primal interior point method (P-IPM), i.e., guaranteed collision avoidance and homotopy preservation throughout optimization, while being orders of magnitude faster. We have analyzed the convergence and evaluated our method for time-optimal multi-UAV trajectory optimizations and simultaneous goal-reaching of multiple robot arms, where we take into consider kinematics-, dynamics-limits, and homotopy-preserving collision constraints. Our method highlights an order of magnitude’s speedup, while generating trajectories of comparable qualities as state-of-the-art P-IPM solver.
Published in: IEEE Robotics and Automation Letters ( Volume: 7, Issue: 3, July 2022)
Page(s): 5950 - 5957
Date of Publication: 16 March 2022

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