1
|
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.
Collapse
|
2
|
On Using Simulation to Predict the Performance of Robot Swarms. Sci Data 2022; 9:788. [PMID: 36581617 PMCID: PMC9800372 DOI: 10.1038/s41597-022-01895-1] [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: 07/14/2022] [Accepted: 12/12/2022] [Indexed: 12/31/2022] Open
Abstract
The discrepancy between simulation and reality-known as the reality gap-is one of the main challenges associated with using simulations to design control software for robot swarms. Currently, the reality-gap problem necessitates expensive and time consuming tests on physical robots to reliably assess control software. Predicting real-world performance accurately without recurring to physical experiments would be particularly valuable. In this paper, we compare various simulation-based predictors of the performance of robot swarms that have been proposed in the literature but never evaluated empirically. We consider (1) the classical approach adopted to estimate real-world performance, which relies on the evaluation of control software on the simulation model used in the design process, and (2) some so-called pseudo-reality predictors, which rely on simulation models other than the one used in the design process. To evaluate these predictors, we reuse 1021 instances of control software and their real-world performance gathered from seven previous studies. Results show that the pseudo-reality predictors considered yield more accurate estimates of the real-world performance than the classical approach.
Collapse
|
3
|
Hiraga M, Katada Y, Ohkura K. Echo state networks for embodied evolution in robotic swarms. ARTIFICIAL LIFE AND ROBOTICS 2022. [DOI: 10.1007/s10015-022-00828-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
4
|
Hasselmann K, Ligot A, Ruddick J, Birattari M. Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms. Nat Commun 2021; 12:4345. [PMID: 34272382 PMCID: PMC8285396 DOI: 10.1038/s41467-021-24642-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/23/2021] [Indexed: 11/23/2022] Open
Abstract
Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms.
Collapse
Affiliation(s)
- Ken Hasselmann
- IRIDIA, Université libre de Bruxelles, Brussels, Belgium
| | - Antoine Ligot
- IRIDIA, Université libre de Bruxelles, Brussels, Belgium
| | - Julian Ruddick
- IRIDIA, Université libre de Bruxelles, Brussels, Belgium
| | | |
Collapse
|
5
|
Wankelmut: A Simple Benchmark for the Evolvability of Behavioral Complexity. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11051994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In evolutionary robotics, an encoding of the control software that maps sensor data (input) to motor control values (output) is shaped by stochastic optimization methods to complete a predefined task. This approach is assumed to be beneficial compared to standard methods of controller design in those cases where no a priori model is available that could help to optimize performance. For robots that have to operate in unpredictable environments as well, an evolutionary robotics approach is favorable. We present here a simple-to-implement, but hard-to-pass benchmark to allow for quantifying the “evolvability” of such evolving robot control software towards increasing behavioral complexity. We demonstrate that such a model-free approach is not a free lunch, as already simple tasks can be unsolvable barriers for fully open-ended uninformed evolutionary computation techniques. We propose the “Wankelmut” task as an objective for an evolutionary approach that starts from scratch without pre-shaped controller software or any other informed approach that would force the behavior to be evolved in a desired way. Our main claim is that “Wankelmut” represents the simplest set of problems that makes plain-vanilla evolutionary computation fail. We demonstrate this by a series of simple standard evolutionary approaches using different fitness functions and standard artificial neural networks, as well as continuous-time recurrent neural networks. All our tested approaches failed. From our observations, we conclude that other evolutionary approaches will also fail if they do not per se favor or enforce the modularity of the evolved structures and if they do not freeze or protect already evolved functionalities from being destroyed again in the later evolutionary process. However, such a protection would require a priori knowledge of the solution of the task and contradict the “no a priori model” approach that is often claimed in evolutionary computation. Thus, we propose a hard-to-pass benchmark in order to make a strong statement for self-complexifying and generative approaches in evolutionary computation in general and in evolutionary robotics specifically. We anticipate that defining such a benchmark by seeking the simplest task that causes the evolutionary process to fail can be a valuable benchmark for promoting future development in the fields of artificial intelligence, evolutionary robotics, and artificial life.
Collapse
|
6
|
Kuckling J, Stützle T, Birattari M. Iterative improvement in the automatic modular design of robot swarms. PeerJ Comput Sci 2020; 6:e322. [PMID: 33816972 PMCID: PMC7924708 DOI: 10.7717/peerj-cs.322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 11/06/2020] [Indexed: 05/26/2023]
Abstract
Iterative improvement is an optimization technique that finds frequent application in heuristic optimization, but, to the best of our knowledge, has not yet been adopted in the automatic design of control software for robots. In this work, we investigate iterative improvement in the context of the automatic modular design of control software for robot swarms. In particular, we investigate the optimization of two control architectures: finite-state machines and behavior trees. Finite state machines are a common choice for the control architecture in swarm robotics whereas behavior trees have received less attention so far. We compare three different optimization techniques: iterative improvement, Iterated F-race, and a hybridization of Iterated F-race and iterative improvement. For reference, we include in our study also (i) a design method in which behavior trees are optimized via genetic programming and (ii) EvoStick, a yardstick implementation of the neuro-evolutionary swarm robotics approach. The results indicate that iterative improvement is a viable optimization algorithm in the automatic modular design of control software for robot swarms.
Collapse
Affiliation(s)
- Jonas Kuckling
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
| | - Thomas Stützle
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
| | | |
Collapse
|
7
|
Ligot A, Kuckling J, Bozhinoski D, Birattari M. Automatic modular design of robot swarms using behavior trees as a control architecture. PeerJ Comput Sci 2020; 6:e314. [PMID: 33816965 PMCID: PMC7924474 DOI: 10.7717/peerj-cs.314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 10/16/2020] [Indexed: 05/26/2023]
Abstract
We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules-low-level behaviors and conditions-into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple's ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple's performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.
Collapse
Affiliation(s)
- Antoine Ligot
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
| | - Jonas Kuckling
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
| | - Darko Bozhinoski
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
- Cognitive Robotics, Delft University of Technology, Delft, Netherlands
| | | |
Collapse
|
8
|
Birattari M, Ligot A, Hasselmann K. Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-0215-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
9
|
Hiraga M, Wei Y, Ohkura K. Evolving collective cognition for object identification in foraging robotic swarms. ARTIFICIAL LIFE AND ROBOTICS 2020. [DOI: 10.1007/s10015-020-00628-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
10
|
Coppola M, McGuire KN, De Wagter C, de Croon GCHE. A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints. Front Robot AI 2020; 7:18. [PMID: 33501187 PMCID: PMC7806031 DOI: 10.3389/frobt.2020.00018] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/04/2020] [Indexed: 11/30/2022] Open
Abstract
This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.
Collapse
Affiliation(s)
- Mario Coppola
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
- Department of Space Systems Engineering, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Kimberly N. McGuire
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Christophe De Wagter
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Guido C. H. E. de Croon
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| |
Collapse
|
11
|
Nogueira YLB, de Brito CEF, Vidal CA, Cavalcante-Neto JB. Towards intrinsic autonomy through evolutionary computation. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09798-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
12
|
Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms. SWARM INTELLIGENCE 2019. [DOI: 10.1007/s11721-019-00175-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
13
|
Coppola M, Guo J, Gill E, de Croon GCHE. The PageRank algorithm as a method to optimize swarm behavior through local analysis. SWARM INTELLIGENCE 2019. [DOI: 10.1007/s11721-019-00172-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
14
|
Barresi J. On building a person: benchmarks for robotic personhood. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1653386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- John Barresi
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, Canada
| |
Collapse
|
15
|
Birattari M, Ligot A, Bozhinoski D, Brambilla M, Francesca G, Garattoni L, Garzón Ramos D, Hasselmann K, Kegeleirs M, Kuckling J, Pagnozzi F, Roli A, Salman M, Stützle T. Automatic Off-Line Design of Robot Swarms: A Manifesto. Front Robot AI 2019; 6:59. [PMID: 33501074 PMCID: PMC7806002 DOI: 10.3389/frobt.2019.00059] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 07/03/2019] [Indexed: 11/13/2022] Open
Abstract
Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms. We define the off-line design problem and illustrate it via a possible practical realization, highlight the core research questions, raise a number of issues regarding the existing literature that is relevant to the automatic off-line design, and provide guidelines that we deem necessary for a healthy development of the domain and for ensuring its relevance to potential real-world applications.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Andrea Roli
- Alma Mater Studiorum, Università di Bologna, Bologna, Italy
| | | | | |
Collapse
|
16
|
Howard D, Eiben AE, Kennedy DF, Mouret JB, Valencia P, Winkler D. Evolving embodied intelligence from materials to machines. NAT MACH INTELL 2019. [DOI: 10.1038/s42256-018-0009-9] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
|
17
|
Persiani M, Franchi AM, Gini G. A working memory model improves cognitive control in agents and robots. COGN SYST RES 2018. [DOI: 10.1016/j.cogsys.2018.04.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
18
|
Wei Y, Hiraga M, Ohkura K, Car Z. Autonomous task allocation by artificial evolution for robotic swarms in complex tasks. ARTIFICIAL LIFE AND ROBOTICS 2018. [DOI: 10.1007/s10015-018-0466-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
19
|
Abstract
SUMMARYThe evolutionary-aided design process is a method to find solutions to design and optimisation problems. Evolutionary algorithms (EAs) are applied to search for optimal solutions from a solution space that evolves over several generations. EAs have found applications in many areas of robotics. This paper covers the efforts to determine body morphology of robots through evolution and body morphology with the controller of robots or similar creatures through co-evolution. The works are reviewed from the perspective of how different algorithms are applied and includes a brief explanation of how they are implemented.
Collapse
|
20
|
|
21
|
Bredeche N, Haasdijk E, Prieto A. Embodied Evolution in Collective Robotics: A Review. Front Robot AI 2018; 5:12. [PMID: 33500899 PMCID: PMC7806005 DOI: 10.3389/frobt.2018.00012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 01/29/2018] [Indexed: 11/13/2022] Open
Abstract
This article provides an overview of evolutionary robotics techniques applied to online distributed evolution for robot collectives, namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. This article also presents a comprehensive summary of research published in the field since its inception around the year 2000, providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an online distributed learning method for designing collective behaviors in swarm-like collectives. This article concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.
Collapse
Affiliation(s)
- Nicolas Bredeche
- Sorbonne Université, CNRS, Institute of Intelligent Systems and Robotics, ISIR, Paris, France
| | - Evert Haasdijk
- Computational Intelligence Group, Department of Computer Science, Vrije Universiteit, Amsterdam, Netherlands
| | - Abraham Prieto
- Integrated Group for Engineering Research, Universidade da Coruna, Ferrol, Spain
| |
Collapse
|
22
|
Hasselmann K, Robert F, Birattari M. Automatic Design of Communication-Based Behaviors for Robot Swarms. LECTURE NOTES IN COMPUTER SCIENCE 2018. [DOI: 10.1007/978-3-030-00533-7_2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|
23
|
Silva F, Correia L, Christensen AL. Evolutionary online behaviour learning and adaptation in real robots. ROYAL SOCIETY OPEN SCIENCE 2017; 4:160938. [PMID: 28791130 PMCID: PMC5541525 DOI: 10.1098/rsos.160938] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Accepted: 06/28/2017] [Indexed: 05/26/2023]
Abstract
Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.
Collapse
Affiliation(s)
- Fernando Silva
- Bio-inspired Computation and Intelligent Machines Lab, 1649-026 Lisboa, Portugal
- BioISI, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
| | - Luís Correia
- BioISI, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Anders Lyhne Christensen
- Bio-inspired Computation and Intelligent Machines Lab, 1649-026 Lisboa, Portugal
- Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisboa, Portugal
| |
Collapse
|
24
|
Montanier JM, Carrignon S, Bredeche N. Behavioral Specialization in Embodied Evolutionary Robotics: Why So Difficult? Front Robot AI 2016. [DOI: 10.3389/frobt.2016.00038] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
25
|
Francesca G, Birattari M. Automatic Design of Robot Swarms: Achievements and Challenges. Front Robot AI 2016. [DOI: 10.3389/frobt.2016.00029] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
26
|
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.
Collapse
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
| |
Collapse
|