Scalable Collaborative Manipulation with Distributed Trajectory Planning

@inproceedings{shorinwa_scalable_2020,
address = {Las Vegas, NV, USA},
title = {Scalable {Collaborative} {Manipulation} with {Distributed} {Trajectory} {Planning}},
isbn = {978-1-72816-212-6},
url = {https://ieeexplore.ieee.org/document/9340957/},
abstract = {We present a distributed algorithm to enable a group of robots to collaboratively manipulate an object to a desired conﬁguration while avoiding obstacles. Each robot solves a local optimization problem iteratively and communicates with its local neighbors, ultimately converging to the optimal trajectory of the object over a receding horizon. The algorithm scales efﬁciently to large groups, with a convergence rate constant in the number of robots, and can enforce constraints that are only known to a subset of the robots, such as for collision avoidance using local online sensing. We show that the algorithm converges many orders of magnitude faster, and results in a tracking error two orders of magnitude lower, than competing distributed collaborative manipulation algorithms based on Consensus alternating direction method of multipliers (ADMM).},
language = {en},
urldate = {2021-03-16},
booktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
publisher = {IEEE},
author = {Shorinwa, Ola and Schwager, Mac},
month = oct,
year = {2020},
pages = {9108--9115},
month_numeric = {10}
}