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Lee S, Milner E, Hauert S. A Data-Driven Method for Metric Extraction to Detect Faults in Robot Swarms. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3189789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Suet Lee
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | - Emma Milner
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | - Sabine Hauert
- Department of Engineering Mathematics, University of Bristol, United Kingdom
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52
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Towards the Achievement of Path Planning with Multi-robot Systems in Dynamic Environments. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01555-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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53
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An inchworm-inspired motion strategy for robotic swarms. ROBOTICA 2021. [DOI: 10.1017/s0263574721000321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractEffective motion planning and localization are necessary tasks for swarm robotic systems to maintain a desired formation while maneuvering. Herein, we present an inchworm-inspired strategy that addresses both these tasks concurrently using anchor robots. The proposed strategy is novel as, by dynamically and optimally selecting the anchor robots, it allows the swarm to maximize its localization performance while also considering secondary objectives, such as the swarm’s speed. A complementary novel method for swarm localization, that fuses inter-robot proximity measurements and motion commands, is also presented. Numerous simulated and physical experiments are included to illustrate our contributions.
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54
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Sende M, Schranz M, Prato G, Brosse E, Morando O, Umlauft M. Engineering Swarms of Cyber-Physical Systems with the CPSwarm Workbench. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01430-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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55
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Murphy RR. Swarm robots in science fiction. Sci Robot 2021; 6:6/56/eabk0451. [PMID: 34321348 DOI: 10.1126/scirobotics.abk0451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 06/30/2021] [Indexed: 11/02/2022]
Abstract
In both science and science fiction, robot swarms are out of control.
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56
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Ozkan-Aydin Y, Goldman DI. Self-reconfigurable multilegged robot swarms collectively accomplish challenging terradynamic tasks. Sci Robot 2021; 6:6/56/eabf1628. [PMID: 34321347 DOI: 10.1126/scirobotics.abf1628] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 07/05/2021] [Indexed: 11/02/2022]
Abstract
Swarms of ground-based robots are presently limited to relatively simple environments, which we attribute in part to the lack of locomotor capabilities needed to traverse complex terrain. To advance the field of terradynamically capable swarming robotics, inspired by the capabilities of multilegged organisms, we hypothesize that legged robots consisting of reversibly chainable modular units with appropriate passive perturbation management mechanisms can perform diverse tasks in variable terrain without complex control and sensing. Here, we report a reconfigurable swarm of identical low-cost quadruped robots (with directionally flexible legs and tail) that can be linked on demand and autonomously. When tasks become terradynamically challenging for individuals to perform alone, the individuals suffer performance degradation. A systematic study of performance of linked units leads to new discoveries of the emergent obstacle navigation capabilities of multilegged robots. We also demonstrate the swarm capabilities through multirobot object transport. In summary, we argue that improvement capabilities of terrestrial swarms of robots can be achieved via the judicious interaction of relatively simple units.
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Affiliation(s)
- Yasemin Ozkan-Aydin
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. .,School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Daniel I Goldman
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
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57
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Soliman YA, Abdulkader SN, Mohamed TM. Constructing a cohesive pattern for collective navigation based on a swarm of robotics. PeerJ Comput Sci 2021; 7:e626. [PMID: 34395863 PMCID: PMC8323727 DOI: 10.7717/peerj-cs.626] [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: 12/16/2020] [Accepted: 06/16/2021] [Indexed: 06/13/2023]
Abstract
Swarm robotics carries out complex tasks beyond the power of simple individual robots. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Many difficulties are facing the aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, and only local information interaction among robots. This paper proposed a new aggregation algorithm. This algorithm combined with the wave algorithm to achieve collective navigation and the recruitment strategy. In this work, the aggregation algorithm consists of two main phases: the searching phase, and the surrounding phase. The execution time of the proposed algorithm was analyzed. The experimental results showed that the aggregation time in the proposed algorithm was significantly reduced by 41% compared to other algorithms in the literature. Moreover, we analyzed our results using a one-way analysis of variance. Also, our results showed that the increasing swarm size significantly improved the performance of the group.
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Affiliation(s)
- Yehia A. Soliman
- Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
| | - Sarah N. Abdulkader
- Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
- Faculty of Computer Studies, Arab Open University, Cairo, Egypt
| | - Taha M. Mohamed
- Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
- Faculty of Business, University of Jeddah, Kingdom of Saudi Arabia (KSA)
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58
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Autonomous foraging with a pack of robots based on repulsion, attraction and influence. Auton Robots 2021. [DOI: 10.1007/s10514-021-09994-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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59
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Abstract
AbstractDecentralised autonomous systems rely on distributed learning to make decisions and to collaborate in pursuit of a shared objective. For example, in swarm robotics the best-of-n problem is a well-known collective decision-making problem in which agents attempt to learn the best option out of n possible alternatives based on local feedback from the environment. This typically involves gathering information about all n alternatives while then systematically discarding information about all but the best option. However, for applications such as search and rescue in which learning the ranking of options is useful or crucial, best-of-n decision-making can be wasteful and costly. Instead, we investigate a more general distributed learning process in which agents learn a preference ordering over all of the n options. More specifically, we introduce a distributed rank learning algorithm based on three-valued logic. We then use agent-based simulation experiments to demonstrate the effectiveness of this model. In this context, we show that a population of agents are able to learn a total ordering over the n options and furthermore the learning process is robust to evidential noise. To demonstrate the practicality of our model, we restrict the communication bandwidth between the agents and show that this model is also robust to limited communications whilst outperforming a comparable probabilistic model under the same communication conditions.
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60
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Franci A, Bizyaeva A, Park S, Leonard NE. Analysis and control of agreement and disagreement opinion cascades. SWARM INTELLIGENCE 2021. [DOI: 10.1007/s11721-021-00190-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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61
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Liu W, Ran W, Nantogma S, Xu Y. Adaptive Information Sharing with Ontological Relevance Computation for Decentralized Self-Organization Systems. ENTROPY 2021; 23:e23030342. [PMID: 33799388 PMCID: PMC8002109 DOI: 10.3390/e23030342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 02/28/2021] [Accepted: 03/08/2021] [Indexed: 11/16/2022]
Abstract
Decentralization is a peculiar characteristic of self-organizing systems such as swarm intelligence systems, which function as complex collective responsive systems without central control and operates based on contextual local coordination among relatively simple individual systems. The decentralized particularity of self-organizing systems lies in their capacity to spontaneously respond to accommodate environmental changes in a cooperative manner without external control. However, if members cannot obtain observations of the state of the whole team and environment, they have to share their knowledge and policies with each other through communication in order to adapt to the environment appropriately. In this paper, we propose an information sharing mechanism as an independent decision phase to improve individual members' joint adaption to the world to fulfill an optimal self-organization in general. We design the information sharing decision analogous to human information sharing mechanisms. In this case, information can be shared among individual members by evaluating the semantic relationship of information based on ontology graph and their local knowledge. That is, if individual member collects more relevant information, the information will be used to update its local knowledge and improve sharing relevant information by measuring the ontological relevance. This will enable more related information to be acquired so that their models will be reinforced for more precise information sharing. Our simulations and experimental results show that this design can share information efficiently to achieve optimal adaptive self-organizing systems.
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62
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Murakami H, Feliciani C, Nishiyama Y, Nishinari K. Mutual anticipation can contribute to self-organization in human crowds. SCIENCE ADVANCES 2021; 7:7/12/eabe7758. [PMID: 33731351 PMCID: PMC7968841 DOI: 10.1126/sciadv.abe7758] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/01/2021] [Indexed: 06/10/2023]
Abstract
Human crowds provide paradigmatic examples of collective behavior emerging through self-organization. Understanding their dynamics is crucial to help manage mass events and daily pedestrian transportation. Although recent findings emphasized that pedestrians' interactions are fundamentally anticipatory in nature, whether and how individual anticipation functionally benefits the group is not well understood. Here, we show the link between individual anticipation and emergent pattern formation through our experiments of lane formation, where unidirectional lanes are spontaneously formed in bidirectional pedestrian flows. Manipulating the anticipatory abilities of some of the pedestrians by distracting them visually delayed the collective pattern formation. Moreover, both the distracted pedestrians and the nondistracted ones had difficulties avoiding collisions while navigating. These results imply that avoidance maneuvers are normally a cooperative process and that mutual anticipation between pedestrians facilitates efficient pattern formation. Our findings may influence various fields, including traffic management, decision-making research, and swarm dynamics.
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Affiliation(s)
- Hisashi Murakami
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan.
| | - Claudio Feliciani
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Yuta Nishiyama
- Information and Management Systems Engineering, Nagaoka University of Technology, Niigata 940-2188, Japan
| | - Katsuhiro Nishinari
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
- Department of Aeronautics and Astronautics, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
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63
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Hoang DNM, Tran DM, Tran TS, Pham HA. An adaptive weighting mechanism for Reynolds rules-based flocking control scheme. PeerJ Comput Sci 2021; 7:e388. [PMID: 33817034 PMCID: PMC7959594 DOI: 10.7717/peerj-cs.388] [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: 10/28/2020] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Cooperative navigation for fleets of robots conventionally adopts algorithms based on Reynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the overall performance could not be guaranteed due to unexpected conditions not covered in experiments. This paper proposes a novel control scheme for a swarm of Unmanned Aerial Vehicles (UAVs) that also employs the original Reynolds rules but adopts an adaptive weight allocation mechanism based on the current context than being fixed at the beginning. The simulation results show that our proposed scheme has better performance than the conventional Reynolds-based ones in terms of the flock compactness and the reduction in the number of crashed swarm members due to collisions. The analytical results of behavioral rules' impact also validate the proposed weighting mechanism's effectiveness leading to improved performance.
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Affiliation(s)
- Duc N. M. Hoang
- Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh, Vietnam
- Vietnam National University of Ho Chi Minh City (VNU-HCM), Ho Chi Minh, Vietnam
| | - Duc M. Tran
- Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh, Vietnam
- Vietnam National University of Ho Chi Minh City (VNU-HCM), Ho Chi Minh, Vietnam
| | - Thanh-Sang Tran
- Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh, Vietnam
- Vietnam National University of Ho Chi Minh City (VNU-HCM), Ho Chi Minh, Vietnam
| | - Hoang-Anh Pham
- Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh, Vietnam
- Vietnam National University of Ho Chi Minh City (VNU-HCM), Ho Chi Minh, Vietnam
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64
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Jdeed M, Schranz M, Elmenreich W. A study using the low-cost swarm robotics platform spiderino in education. COMPUTERS AND EDUCATION OPEN 2020. [DOI: 10.1016/j.caeo.2020.100017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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65
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Vermesan O, Bahr R, Ottella M, Serrano M, Karlsen T, Wahlstrøm T, Sand HE, Ashwathnarayan M, Gamba MT. Internet of Robotic Things Intelligent Connectivity and Platforms. Front Robot AI 2020; 7:104. [PMID: 33501271 PMCID: PMC7805974 DOI: 10.3389/frobt.2020.00104] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 07/02/2020] [Indexed: 11/27/2022] Open
Abstract
The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and "things" have evolved significantly. "Things" now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of "intelligent things" (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.
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Affiliation(s)
| | | | | | - Martin Serrano
- Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland
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67
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Tarapore D, Groß R, Zauner KP. Sparse Robot Swarms: Moving Swarms to Real-World Applications. Front Robot AI 2020; 7:83. [PMID: 33501250 PMCID: PMC7805967 DOI: 10.3389/frobt.2020.00083] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 05/19/2020] [Indexed: 11/30/2022] Open
Abstract
Robot swarms are groups of robots that each act autonomously based on only local perception and coordination with neighboring robots. While current swarm implementations can be large in size (e.g., 1,000 robots), they are typically constrained to working in highly controlled indoor environments. Moreover, a common property of swarms is the underlying assumption that the robots act in close proximity of each other (e.g., 10 body lengths apart), and typically employ uninterrupted, situated, close-range communication for coordination. Many real world applications, including environmental monitoring and precision agriculture, however, require scalable groups of robots to act jointly over large distances (e.g., 1,000 body lengths), rendering the use of dense swarms impractical. Using a dense swarm for such applications would be invasive to the environment and unrealistic in terms of mission deployment, maintenance and post-mission recovery. To address this problem, we propose the sparse swarm concept, and illustrate its use in the context of four application scenarios. For one scenario, which requires a group of rovers to traverse, and monitor, a forest environment, we identify the challenges involved at all levels in developing a sparse swarm-from the hardware platform to communication-constrained coordination algorithms-and discuss potential solutions. We outline open questions of theoretical and practical nature, which we hope will bring the concept of sparse swarms to fruition.
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Affiliation(s)
- Danesh Tarapore
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Roderich Groß
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Klaus-Peter Zauner
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
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