abstract

On Site Fidelity and the Price of Ignorance in Swarm Robotic Central Place Foraging Algorithms

Authors:

Abhinav Aggarwal,

G. Matthew Fricke,

Melanie E. MosesAuthors Info & Claims

PODC '19: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing

Pages 63 - 65

https://doi.org/10.1145/3293611.3331572

Published: 16 July 2019 Publication History

Abstract

A key factor limiting the performance of central place foraging algorithms is the awareness of the agent(s) about the location of food items around the nest. We study the ratio of how much time an ignorant agent takes relative to an omniscient forager for complete collection of food items in the arena. This effectively quantifies the penalty each algorithm pays for not knowing (or choosing to ignore information gained about) where the resources are located. We model the effect of depletion of food items from the arena on the foraging efficiency over time and analytically verify that returning to the location of the last food item found strongly helps in counteracting this effect. To the best of our knowledge, these results have only been empirically argued so far.

References

[1]

A Aggarwal, D Gupta, W F Vining, G M Fricke, and M E Moses. 2019. Ignorance is Not Bliss: An Analysis of Central-Place Foraging Algorithms. Manuscript Under Submission. Available athttps://bit.ly/2QcPaX8 (2019).

[2]

A Aggarwal and J Saia. 2019. Robustly Finding a Cluster Using the Golden Ratio. Manuscript Under Submission. (2019).

[3]

A Aggarwal, W F Vining, D Gupta, J Saia, and M E Moses. 2019. A Most Irrational Foraging Algorithm. Manuscript Under Submission. (2019).

[4]

Ricardo Baeza-Yates and René Schott. 1995. Parallel searching in the plane. Computational Geometry5, 3 (1995), 143--154.

Digital Library

[5]

Allan Borodin and Ran El-Yaniv. 2005. Online computation and competitive analysis. Cambridge University Press (2005).

Digital Library

[6]

Ofer Feinerman and Amos Korman. 2017. The ANTS problem. Distributed Computing 30, 3 (2017), 149--168.

Digital Library

[7]

G Matthew Fricke, Joshua P Hecker, Antonio D Griego, Linh T Tran, and Melanie EMoses. 2016. A distributed deterministic spiral search algorithm for swarms. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 4430--4436.

[8]

Deborah M Gordon and Alan W Kulig. 1996. Founding, foraging, and fighting: colony size and the spatial distribution of harvester ant nests. Ecology 77, 8 (1996), 2393--2409.

[9]

Joshua P Hecker and Melanie E Moses. 2015. Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms. Swarm Intelligence 9, 1(2015), 43--70.

[10]

HC Holland. 1957. The archimedes spiral. Nature179, 4556 (1957), 432.

[11]

Valerie King and Jared Saia. 2004. Choosing a random peer. In Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing. ACM, 125--130.

Digital Library

[12]

Malika Meghjani, Sandeep Manjanna, and Gregory Dudek. 2016. Multi-targetrendezvous search. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2596--2603.

[13]

Danielle P Mersch, Alessandro Crespi, and Laurent Keller. 2013. Tracking indi-viduals shows spatial fidelity is a key regulator of ant social organization. Science 340, 6136 (2013), 1090--1093.

[14]

Cameron Musco, Hsin-Hao Su, and Nancy Lynch. 2016. Ant-inspired density estimation via random walks. In Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing. ACM, 469--478.

Digital Library

[15]

Lu Qi, Antonio D. Griego, G. Matthew Fricke, and Melanie E Moses. 2019. Comparing Physical and Simulated Performance of a Deterministic and a Bio-inspired Stochastic Foraging Strategy for Robot Swarms. In Proceedings of the International Conference on Robotics and Automation (ICRA). IEEE.

[16]

Luc Steels. 1990. Cooperation between distributed agents through self-organisation. In Intelligent Robots and Systems' 90. 'Towards a New Frontier of Applications', Proceedings. IROS'90. IEEE International Workshop on. IEEE, 8--14.

[17]

Andrew Chi-Chin Yao. 1977. Probabilistic computations: Toward a unified measure of complexity. In 18th Annual Symposium on Foundations of Computer Science(sfcs 1977). IEEE, 222--227.

Digital Library

Cited By

Lu QFricke GEricksen JMoses M(2020)Swarm Foraging Review: Closing the Gap Between Proof and PracticeCurrent Robotics Reports10.1007/s43154-020-00018-1Online publication date: 28-Aug-2020
https://doi.org/10.1007/s43154-020-00018-1
Ben-David NChang YDory MLeitersdorf D(2019)PODC 2019 ReviewACM SIGACT News10.1145/3374857.337486650:4(33-45)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3374857.3374866
Alistarh D(2019)Distributed Computing Column 76ACM SIGACT News10.1145/3374857.337486550:4(31-32)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3374857.3374865

Index Terms

On Site Fidelity and the Price of Ignorance in Swarm Robotic Central Place Foraging Algorithms
1. Computing methodologies

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cover image ACM Conferences

PODC '19: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing

July 2019

563 pages

ISBN:9781450362177

DOI:10.1145/3293611

General Chair:
Peter Robinson
City University of Hong Kong
,
Program Chair:
Faith Ellen
University of Toronto, Canada

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 July 2019

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James S. McDonnell Foundation, Complex Systems Scholar Award

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PODC '19

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PODC '19: ACM Symposium on Principles of Distributed Computing

July 29 - August 2, 2019

Toronto ON, Canada

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PODC '19 Paper Acceptance Rate 48 of 173 submissions, 28%;

Overall Acceptance Rate 740 of 2,477 submissions, 30%

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Cited By

Lu QFricke GEricksen JMoses M(2020)Swarm Foraging Review: Closing the Gap Between Proof and PracticeCurrent Robotics Reports10.1007/s43154-020-00018-1Online publication date: 28-Aug-2020
https://doi.org/10.1007/s43154-020-00018-1
Ben-David NChang YDory MLeitersdorf D(2019)PODC 2019 ReviewACM SIGACT News10.1145/3374857.337486650:4(33-45)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3374857.3374866
Alistarh D(2019)Distributed Computing Column 76ACM SIGACT News10.1145/3374857.337486550:4(31-32)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3374857.3374865

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