Swarm Foraging Review: Closing the Gap Between Proof and Practice
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GROUP ROBOTICS (M GINI AND F AMIGONI, SECTION EDITORS)
Swarm Foraging Review: Closing the Gap Between Proof and Practice Qi Lu 1 & G. Matthew Fricke 2,3
&
John C. Ericksen 2,4 & Melanie E. Moses 2,5
# Springer Nature Switzerland AG 2020
Abstract Purpose of Review We review recent research on swarm robot foraging and contextualize it with foundational work. Recent work can be divided into two complementary camps: self-organizing algorithms that provide practical gains and analytical research focus on theoretical proofs. Recent Findings Encouragingly, the convergence between theory and practice is evident in analytical work on the scaling of transportation networks and in behavioral grammars that give formal insight into emergent properties of foraging. Augmented reality has enabled virtual pheromones to be used with hardware, blurring the line between physical and simulation experiments. Summary In this review we highlight bio-inspired and self-organizing approaches to swarm foraging and contrast them with approaches that can provide theoretical proofs, but which abstract away important features from foraging in real-world environments. Keywords Swarm robotics . Swarm intelligence . Bio-inspired foraging . Foraging taxonomies . Central place foraging review
Introduction Østergaard et al. [1] defined swarm foraging as “a two-step repetitive process in which (1) robots search a designated region of space for certain objects, and (2) once found these objects are brought to a goal region using some form of navigation.” Winfield [2] wrote that the foraging task is a powerful benchmark for three reasons: social insects provide a proof-of-concept, success requires the coordination of several physical tasks (searching, harvesting, transportation, homing, and deposition at a collection site), and optimality requires cooperation between robots.
One of the most cited applications of swarm foraging is the harvesting of resources on extraterrestrial bodies [2–4]. Efficient resource collection under these remote and harsh conditions requires the use of autonomous robots. The fundamental challenge in swarm foraging is the complex and dynamic interaction between robots, the environment, and targets, given only limited and noisy local information. Several divergent lines of research have been developed to meet this challenge. This review focuses on research published between 2015 and early 2020. We refer the reader to Senanayake et al. [5], Bayindir [6], and Zedadra et al. [7] for reviews of work before
This article is part of the Topical Collection on Group Robotics * G. Matthew Fricke [email protected] Qi Lu [email protected] John C. Ericksen [email protected] Melanie E. Moses [email protected]
1
Department of Computer Science, University of Texas San Antonio, One UTSA Circle, San Antonio, TX 78249, USA
2
Department of Computer Science, University of New Mexico, MSC01 1130, Albuquerque, NM 87131-0001, USA
3
Center for Advanced Research Computing, University of New Mexico, Albuquerque, NM, USA
4
Honeywell Federal Manufactu
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