Abstract
Online coverage path planning is a canonical multi-robot task, where the objective is to minimize the time it takes for robots to visit every point in an unknown area. Two general major approaches have been explored in the literature: a stigmergic approach, inspired by ant behavior, relies on active marking of the environment. In contrast, the collaborative approach relies instead on localization, memory of positions, and global communications. In this paper, we report on a new approach, inspired by territorial bird chirping, which borrows from both previous approaches: it relies on localization and memory, but not on global communications. We provide a detailed analytic and empirical evaluation of this model.
Original language | English |
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Title of host publication | Springer Proceedings in Advanced Robotics |
Publisher | Springer Science and Business Media B.V. |
Pages | 31-43 |
Number of pages | 13 |
DOIs | |
State | Published - 2018 |
Publication series
Name | Springer Proceedings in Advanced Robotics |
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Volume | 6 |
ISSN (Print) | 2511-1256 |
ISSN (Electronic) | 2511-1264 |
Bibliographical note
Publisher Copyright:© 2018, Springer International Publishing AG.
Funding
LG thanks the support of the Bar Ilan Robotics Consortium (BIRC) and the office of the vice president during his stay at Bar Ilan University and acknowledges discussions with Adham Sabra and Alan Winfield. The research was supported in part by ISF grant #1511/12 and EPSRC grant EP/I013717/1. As always, thanks to K. Ushi. Data Access Statement The Java code to run the stochastic simulations is openly available in the data.bris University of Bristol repository under DOI: https://doi.org/10.5523/bris.i1rl4lk2boj410h 6ui4cpblfh. Acknowledgements LG thanks the support of the Bar Ilan Robotics Consortium (BIRC) and the office of the vice president during his stay at Bar Ilan University and acknowledges discussions with Adham Sabra and Alan Winfield. The research was supported in part by ISF grant #1511/12 and EPSRC grant EP/I013717/1. As always, thanks to K. Ushi. Data Access Statement The Java code to run the stochastic simulations is openly available in the data.bris University of Bristol repository under DOI: https://doi.org/10.5523/bris.i1rl4lk2boj410h 6ui4cpblfh.
Funders | Funder number |
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Bar Ilan Robotics Consortium | |
data.bris University of Bristol | |
Engineering and Physical Sciences Research Council | EP/I013717/1 |
Israel Science Foundation | 1511/12 |