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Buchanan E, Alden K, Pomfret A, Timmis J, Tyrrell AM. A study of error diversity in robotic swarms for task partitioning in foraging tasks. Front Robot AI 2023; 9:904341. [PMID: 36686209 PMCID: PMC9845931 DOI: 10.3389/frobt.2022.904341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 12/08/2022] [Indexed: 01/05/2023] Open
Abstract
Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and will have a similar (if not the same) error model. However, in reality, this is not the case, and this lack of uniformity in the error model, and other operations, can lead to various emergent behaviors. This paper considers the impact of the error model and compares robots in a swarm that operate using the same error model (uniform error) against each robot in the swarm having a different error model (thus introducing error diversity). Experiments are presented in the context of a foraging task. Simulation and physical experimental results show the importance of the error model and diversity in achieving the expected swarm behavior.
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Affiliation(s)
- Edgar Buchanan
- School of Physics, Engineering and Technology, University of York, York, United Kingdom,*Correspondence: Edgar Buchanan, ; Andy M. Tyrrell,
| | - Kieran Alden
- School of Physics, Engineering and Technology, University of York, York, United Kingdom
| | - Andrew Pomfret
- School of Physics, Engineering and Technology, University of York, York, United Kingdom
| | - Jon Timmis
- School of Computer Science, University of Sunderland, Sunderland, United Kingdom
| | - Andy M. Tyrrell
- School of Physics, Engineering and Technology, University of York, York, United Kingdom,*Correspondence: Edgar Buchanan, ; Andy M. Tyrrell,
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Kuckling J. Recent trends in robot learning and evolution for swarm robotics. Front Robot AI 2023; 10:1134841. [PMID: 37168882 PMCID: PMC10166233 DOI: 10.3389/frobt.2023.1134841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/21/2023] [Indexed: 05/13/2023] Open
Abstract
Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.
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Fedele G, D'Alfonso L. A Kinematic Model for Swarm Finite-Time Trajectory Tracking. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:3806-3815. [PMID: 30106703 DOI: 10.1109/tcyb.2018.2856269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper focuses on the trajectory tracking problem for a swarm of mobile agents. A kinematic model describing the interactions and evolutions of the swarm members is proposed and its main properties are analyzed emphasizing that the agents centroid is ensured to track in finite-time a given reference trajectory and that the agents reach an aggregation in finite-time in a hyper-ball moving around the centroid path. One of the main characteristics of the model is the presence of an interaction matrix, between agents coordinates, which allows to define some properties of the swarm allowing the creation of different forms of agents aggregations, i.e., spheres, ellipsoids, straight lines, etc. Indeed swarm properties related to the agents configuration around the performed path along with agents interactions and absence of collisions are analyzed depending on the chosen interaction matrix.
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Jones S, Studley M, Hauert S, Winfield AFT. A Two Teraflop Swarm. Front Robot AI 2018; 5:11. [PMID: 33500898 PMCID: PMC7805610 DOI: 10.3389/frobt.2018.00011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/25/2018] [Indexed: 11/13/2022] Open
Abstract
We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks.
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Affiliation(s)
- Simon Jones
- University of Bristol, Bristol, United Kingdom.,University of the West of England, Bristol, United Kingdom.,Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom
| | - Matthew Studley
- University of the West of England, Bristol, United Kingdom.,Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom
| | - Sabine Hauert
- University of Bristol, Bristol, United Kingdom.,Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom
| | - Alan Frank Thomas Winfield
- University of the West of England, Bristol, United Kingdom.,Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom
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Valentini G, Ferrante E, Dorigo M. The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives. Front Robot AI 2017. [DOI: 10.3389/frobt.2017.00009] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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do Nascimento NM, de Lucena CJP. FIoT: An agent-based framework for self-adaptive and self-organizing applications based on the Internet of Things. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.10.031] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Duarte M, Costa V, Gomes J, Rodrigues T, Silva F, Oliveira SM, Christensen AL. Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots. PLoS One 2016; 11:e0151834. [PMID: 26999614 PMCID: PMC4801206 DOI: 10.1371/journal.pone.0151834] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 03/05/2016] [Indexed: 11/19/2022] Open
Abstract
Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.
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Affiliation(s)
- Miguel Duarte
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Vasco Costa
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Jorge Gomes
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- BioISI, Faculdade de Ciências, Lisbon, Portugal
| | - Tiago Rodrigues
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Fernando Silva
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- BioISI, Faculdade de Ciências, Lisbon, Portugal
| | - Sancho Moura Oliveira
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Anders Lyhne Christensen
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
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On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario. SWARM INTELLIGENCE 2015. [DOI: 10.1007/s11721-015-0113-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Trianni V, López-Ibáñez M. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. PLoS One 2015; 10:e0136406. [PMID: 26295151 PMCID: PMC4546428 DOI: 10.1371/journal.pone.0136406] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 08/04/2015] [Indexed: 11/19/2022] Open
Abstract
The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.
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Affiliation(s)
- Vito Trianni
- Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy
- * E-mail:
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Ferrante E, Turgut AE, Duéñez-Guzmán E, Dorigo M, Wenseleers T. Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comput Biol 2015; 11:e1004273. [PMID: 26247819 PMCID: PMC4527708 DOI: 10.1371/journal.pcbi.1004273] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 04/08/2015] [Indexed: 01/27/2023] Open
Abstract
Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as "task partitioning", whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.
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Affiliation(s)
- Eliseo Ferrante
- Laboratory of Socio-Ecology and Social Evolution, Zoological Institute, KU Leuven, Leuven, Belgium
| | - Ali Emre Turgut
- Mechanical Engineering Department, Middle East Technical University, Ankara, Turkey
| | - Edgar Duéñez-Guzmán
- Laboratory of Socio-Ecology and Social Evolution, Zoological Institute, KU Leuven, Leuven, Belgium
| | - Marco Dorigo
- IRIDIA–CoDE, Université Libre de Bruxelles, Brussels, Belgium
| | - Tom Wenseleers
- Laboratory of Socio-Ecology and Social Evolution, Zoological Institute, KU Leuven, Leuven, Belgium
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Francesca G, Brambilla M, Brutschy A, Garattoni L, Miletitch R, Podevijn G, Reina A, Soleymani T, Salvaro M, Pinciroli C, Mascia F, Trianni V, Birattari M. AutoMoDe-Chocolate: automatic design of control software for robot swarms. SWARM INTELLIGENCE 2015. [DOI: 10.1007/s11721-015-0107-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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14
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Tuci E, Trianni V. On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1594-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Francesca G, Brambilla M, Brutschy A, Trianni V, Birattari M. AutoMoDe: A novel approach to the automatic design of control software for robot swarms. SWARM INTELLIGENCE 2014. [DOI: 10.1007/s11721-014-0092-4] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Burbidge R, Wilson MS. Vector-valued function estimation by grammatical evolution for autonomous robot control. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2013.09.044] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Formica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again. LECTURE NOTES IN COMPUTER SCIENCE 2012. [DOI: 10.1007/978-3-642-32650-9_25] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Sperati V, Trianni V, Nolfi S. Self-organised path formation in a swarm of robots. SWARM INTELLIGENCE 2011. [DOI: 10.1007/s11721-011-0055-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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