CaltechAUTHORS
  A Caltech Library Service

Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming

Morgan, Daniel and Subramanian, Giri P. and Chung, Soon-Jo and Hadaegh, Fred Y. (2016) Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming. International Journal of Robotics Research, 35 (10). pp. 1261-1285. ISSN 0278-3649. http://resolver.caltech.edu/CaltechAUTHORS:20161221-120751793

[img] PDF - Accepted Version
See Usage Policy.

4Mb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20161221-120751793

Abstract

This paper presents a distributed, guidance and control algorithm for reconfiguring swarms composed of hundreds to thousands of agents with limited communication and computation capabilities. This algorithm solves both the optimal assignment and collision-free trajectory generation for robotic swarms, in an integrated manner, when given the desired shape of the swarm (without pre-assigned terminal positions). The optimal assignment problem is solved using a distributed auction assignment that can vary the number of target positions in the assignment, and the collision-free trajectories are generated using sequential convex programming. Finally, model predictive control is used to solve the assignment and trajectory generation in real time using a receding horizon. The model predictive control formulation uses current state measurements to resolve for the optimal assignment and trajectory. The implementation of the distributed auction algorithm and sequential convex programming using model predictive control produces the swarm assignment and trajectory optimization (SATO) algorithm that transfers a swarm of robots or vehicles to a desired shape in a distributed fashion. Once the desired shape is uploaded to the swarm, the algorithm determines where each robot goes and how it should get there in a fuel-efficient, collision-free manner. Results of flight experiments using multiple quadcopters show the effectiveness of the proposed SATO algorithm.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1177/0278364916632065DOIArticle
http://journals.sagepub.com/doi/full/10.1177/0278364916632065PublisherArticle
https://www.youtube.com/watch?v=CFWagY_jhH4Related ItemSupplementary Video
ORCID:
AuthorORCID
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2016 The Author(s). First Published February 23, 2016. Thanks to Saptarshi Bandyopadhyay for stimulating discussions and constructive comments. This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. © 2016 California Institute of Technology. This work was supported by a NASA Office of the Chief Technologist Space Technology Research Fellowship and an Air Force Office of Scientific Research (AFOSR grant number FA95501210193). Government sponsorship acknowledged.
Group:GALCIT
Funders:
Funding AgencyGrant Number
NASA/JPL/CaltechUNSPECIFIED
NASA Space Technology Research FellowshipUNSPECIFIED
Air Force Office of Scientific Research (AFOSR)FA95501210193
Record Number:CaltechAUTHORS:20161221-120751793
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20161221-120751793
Official Citation:Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming Daniel Morgan, Giri P Subramanian, Soon-Jo Chung, Fred Y Hadaegh The International Journal of Robotics Research Vol 35, Issue 10, pp. 1261 - 1285 First published date: February-23-2016 10.1177/0278364916632065
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73087
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:21 Dec 2016 20:26
Last Modified:06 Jul 2018 20:08

Repository Staff Only: item control page