Distributed information gathering policies under temporal logic constraints

Kevin Leahy, Austin Jones, Mac Schwager, Calin Belta

2015 54th IEEE Conference on Decision and Control (CDC), 2015

Abstract

In this work, we present an algorithm for synthesizing distributed control policies for networks of mobile robots such that they gather the maximum amount of information about some a priori unknown feature of the environment, e.g. hydration levels of crops or a lost person adrift at sea. Natural motion and communication constraints such as “Avoid obstacles and periodically communicate with all other agents”, are formulated as temporal logic formulae, a richer set of constraints than has been previously considered for this application. The mission constraints are distributed automatically among sub-groups of the agents. Each sub-group independently executes a receding horizon planner that locally optimizes information-gathering and is guaranteed to satisfy the assigned mission speciﬁcation. This approach allows the agents to disperse beyond inter-agent communication ranges while ensuring global team constraints are met. We evaluate our novel paradigm via simulation.

BibTeX

@inproceedings{leahy_distributed_2015,
  address = {Osaka},
  title = {Distributed information gathering policies under temporal logic constraints},
  isbn = {978-1-4799-7886-1},
  url = {http://ieeexplore.ieee.org/document/7403291/},
  abstract = {In this work, we present an algorithm for synthesizing distributed control policies for networks of mobile robots such that they gather the maximum amount of information about some a priori unknown feature of the environment, e.g. hydration levels of crops or a lost person adrift at sea. Natural motion and communication constraints such as “Avoid obstacles and periodically communicate with all other agents”, are formulated as temporal logic formulae, a richer set of constraints than has been previously considered for this application. The mission constraints are distributed automatically among sub-groups of the agents. Each sub-group independently executes a receding horizon planner that locally optimizes information-gathering and is guaranteed to satisfy the assigned mission speciﬁcation. This approach allows the agents to disperse beyond inter-agent communication ranges while ensuring global team constraints are met. We evaluate our novel paradigm via simulation.},
  language = {en},
  urldate = {2020-09-15},
  booktitle = {2015 54th {IEEE} {Conference} on {Decision} and {Control} ({CDC})},
  publisher = {IEEE},
  author = {Leahy, Kevin and Jones, Austin and Schwager, Mac and Belta, Calin},
  month = dec,
  year = {2015},
  keywords = {multi-robot, planning},
  pages = {6803--6808},
  month_numeric = {12}
}