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Hasan M, Saifullah MK, Kamal MAS, Yamada K. Distributed Broadcast Control of Multi-Agent Systems Using Hierarchical Coordination. Biomimetics (Basel) 2024; 9:407. [PMID: 39056848 PMCID: PMC11274499 DOI: 10.3390/biomimetics9070407] [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: 05/11/2024] [Revised: 06/29/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC.
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Affiliation(s)
| | | | - Md Abdus Samad Kamal
- Division of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan; (M.H.); (M.K.S.); (K.Y.)
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Van Havermaet S, Khaluf Y, Simoens P. Reactive shepherding along a dynamic path. Sci Rep 2024; 14:14915. [PMID: 38942794 PMCID: PMC11213918 DOI: 10.1038/s41598-024-65894-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/25/2024] [Indexed: 06/30/2024] Open
Abstract
Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill employed in the herding of animals, crowd control, and evacuation operations. Integrating shepherding capabilities into robots holds promise to perform such tasks with increased efficiency and reduced labor costs. To date, robotic shepherds have only been designed to steer a herd towards a predetermined goal location without constraints on the trajectory. However, the tasks of a sheepdog encompass not only steering the herd but also (i) maintaining the herd within a designated area and (ii) averting dangers, obstacles, or undesirable terrain such as newly sown land. We present a decentralized control algorithm for multi-robot shepherding designed to guide a group of animals along a specified path delineated by two boundaries. The algorithm incorporates the additional objective of preserving the group within these boundaries. Simulation results reveal that, especially in sections of the path with sharp turns and a small distance between the boundaries, the group exhibits a tendency to deviate beyond the prescribed margin. Additionally, our findings emphasize the algorithm's sensitivity to the ratio of robot-group sizes and the magnitude of the group's velocity.
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Affiliation(s)
- Stef Van Havermaet
- IDLab, Department of Information Technology, Ghent University - imec, B-9052, Gent, Belgium.
| | - Yara Khaluf
- IDLab, Department of Information Technology, Ghent University - imec, B-9052, Gent, Belgium
- Department of Social Sciences, Wageningen University and Research, 6706KN, Wageningen, The Netherlands
| | - Pieter Simoens
- IDLab, Department of Information Technology, Ghent University - imec, B-9052, Gent, Belgium
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Van Havermaet S, Simoens P, Landgraf T, Khaluf Y. Steering herds away from dangers in dynamic environments. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230015. [PMID: 37234508 PMCID: PMC10206474 DOI: 10.1098/rsos.230015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023]
Abstract
Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.
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Affiliation(s)
- Stef Van Havermaet
- Department of Information Technology, University of Ghent—imec, Technologiepark 126, 9052 Ghent, Belgium
| | - Pieter Simoens
- Department of Information Technology, University of Ghent—imec, Technologiepark 126, 9052 Ghent, Belgium
| | - Tim Landgraf
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 7, 14195 Berlin, Germany
| | - Yara Khaluf
- Department of Information Technology, University of Ghent—imec, Technologiepark 126, 9052 Ghent, Belgium
- Department of Social Sciences, Wageningen University and Research, Hollandseweg 1, 6706KN Wageningen, The Netherlands
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Arques P, Aznar F, Pujol M, Rizo R. Obtaining emergent behaviors for swarm robotics singling with deep reinforcement learning. Adv Robot 2023. [DOI: 10.1080/01691864.2023.2194952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
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Guidance by multiple sheepdogs including abnormalities. ARTIFICIAL LIFE AND ROBOTICS 2022. [DOI: 10.1007/s10015-022-00807-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Zhang S, Pan J. Collecting a Flock With Multiple Sub-Groups by Using Multi-Robot System. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3178152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Shuai Zhang
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | - Jia Pan
- Department of Computer Science, The University of Hong Kong, Hong Kong
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7
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Auletta F, Fiore D, Richardson MJ, di Bernardo M. Herding stochastic autonomous agents via local control rules and online target selection strategies. Auton Robots 2022. [DOI: 10.1007/s10514-021-10033-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractWe propose a simple yet effective set of local control rules to make a small group of “herder agents” collect and contain in a desired region a large ensemble of non-cooperative, non-flocking stochastic “target agents” in the plane. We investigate the robustness of the proposed strategies to variations of the number of target agents and the strength of the repulsive force they feel when in proximity of the herders. The effectiveness of the proposed approach is confirmed in both simulations in ROS and experiments on real robots.
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Herd guidance by multiple sheepdog agents with repulsive force. ARTIFICIAL LIFE AND ROBOTICS 2022. [DOI: 10.1007/s10015-021-00726-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Abstract
This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.
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Zhi J, Lien JM. Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors Using Deep Reinforcement Learning. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3068955] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Song H, Varava A, Kravchenko O, Kragic D, Wang MY, Pokorny FT, Hang K. Herding by caging: a formation-based motion planning framework for guiding mobile agents. Auton Robots 2021. [DOI: 10.1007/s10514-021-09975-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Hu J, Turgut AE, Krajnik T, Lennox B, Arvin F. Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.3018549] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Abbass HA, Hunjet RA. Smart Shepherding: Towards Transparent Artificial Intelligence Enabled Human-Swarm Teams. UNMANNED SYSTEM TECHNOLOGIES 2021. [DOI: 10.1007/978-3-030-60898-9_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Bassolillo SR, D’Amato E, Notaro I, Blasi L, Mattei M. Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4324. [PMID: 32756360 PMCID: PMC7436082 DOI: 10.3390/s20154324] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/26/2020] [Accepted: 07/28/2020] [Indexed: 11/16/2022]
Abstract
This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.
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Affiliation(s)
- Salvatore Rosario Bassolillo
- Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa (CE), Italy; (S.R.B.); (I.N.); (L.B.); (M.M.)
| | - Egidio D’Amato
- Department of Science and Technology, University of Naples Parthenope, 80143 Naples, Italy
| | - Immacolata Notaro
- Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa (CE), Italy; (S.R.B.); (I.N.); (L.B.); (M.M.)
| | - Luciano Blasi
- Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa (CE), Italy; (S.R.B.); (I.N.); (L.B.); (M.M.)
| | - Massimiliano Mattei
- Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa (CE), Italy; (S.R.B.); (I.N.); (L.B.); (M.M.)
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Long NK, Sammut K, Sgarioto D, Garratt M, Abbass HA. A Comprehensive Review of Shepherding as a Bio-Inspired Swarm-Robotics Guidance Approach. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020. [DOI: 10.1109/tetci.2020.2992778] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Piardi L, Kalempa VC, Limeira M, Schneider de Oliveira A, Leitão P. ARENA-Augmented Reality to Enhanced Experimentation in Smart Warehouses. SENSORS 2019; 19:s19194308. [PMID: 31590295 PMCID: PMC6806094 DOI: 10.3390/s19194308] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 09/26/2019] [Accepted: 09/28/2019] [Indexed: 11/23/2022]
Abstract
The current industrial scenario demands advances that depend on expensive and sophisticated solutions. Augmented Reality (AR) can complement, with virtual elements, the real world. Faced with this features, an AR experience can meet the demand for prototype testing and new solutions, predicting problems and failures that may only exist in real situations. This work presents an environment for experimentation of advanced behaviors in smart factories, allowing experimentation with multi-robot systems (MRS), interconnected, cooperative, and interacting with virtual elements. The concept of ARENA introduces a novel approach to realistic and immersive experimentation in industrial environments, aiming to evaluate new technologies aligned with the Industry 4.0. The proposed method consists of a small-scale warehouse, inspired in a real scenario characterized in this paper, managing by a group of autonomous forklifts, fully interconnected, which are embodied by a swarm of tiny robots developed and prepared to operate in the small scale scenario. The AR is employed to enhance the capabilities of swarm robots, allowing box handling and virtual forklifts. Virtual laser range finders (LRF) are specially designed as segmentation of a global RGB-D camera, to improve robot perception, allowing obstacle avoidance and environment mapping. This infrastructure enables the evaluation of new strategies to improve manufacturing productivity, without compromising the production by automation faults.
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Affiliation(s)
- Luis Piardi
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança (IPB), Campus de Santa Apolónia, 5300-253 Bragança, Portugal.
- Graduate School of Electrical Engineering and Computer Science (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, Paraná, Brazil.
| | - Vivian Cremer Kalempa
- Graduate School of Electrical Engineering and Computer Science (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, Paraná, Brazil.
- Centro de Educação do Planalto Norte (CEPLAN), Universidade do Estado de Santa Catarina (UDESC), Rua Luiz Fernando Hastreiter 180, São Bento do Sul 89283-081, Santa Catarina, Brazil.
| | - Marcelo Limeira
- Graduate School of Electrical Engineering and Computer Science (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, Paraná, Brazil.
| | - André Schneider de Oliveira
- Graduate School of Electrical Engineering and Computer Science (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, Paraná, Brazil.
| | - Paulo Leitão
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança (IPB), Campus de Santa Apolónia, 5300-253 Bragança, Portugal.
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Santos C, Espinosa F, Martinez-Rey M, Gualda D, Losada C. Self-Triggered Formation Control of Nonholonomic Robots. SENSORS 2019; 19:s19122689. [PMID: 31207941 PMCID: PMC6631130 DOI: 10.3390/s19122689] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 06/07/2019] [Accepted: 06/12/2019] [Indexed: 11/16/2022]
Abstract
In this paper, we report the design of an aperiodic remote formation controller applied to nonholonomic robots tracking nonlinear, trajectories using an external positioning sensor network. Our main objective is to reduce wireless communication with external sensors and robots while guaranteeing formation stability. Unlike most previous work in the field of aperiodic control, we design a self-triggered controller that only updates the control signal according to the variation of a Lyapunov function, without taking the measurement error into account. The controller is responsible for scheduling measurement requests to the sensor network and for computing and sending control signals to the robots. We design two triggering mechanisms: centralized, taking into account the formation state and decentralized, considering the individual state of each unit. We present a statistical analysis of simulation results, showing that our control solution significantly reduces the need for communication in comparison with periodic implementations, while preserving the desired tracking performance. To validate the proposal, we also perform experimental tests with robots remotely controlled by a mini PC through an IEEE 802.11g wireless network, in which robots pose is detected by a set of camera sensors connected to the same wireless network.
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Affiliation(s)
- Carlos Santos
- Electronics Department, University of Alcalá, Engineering School, Campus Universitario, 28871 Alcalá de Henares, Spain.
| | - Felipe Espinosa
- Electronics Department, University of Alcalá, Engineering School, Campus Universitario, 28871 Alcalá de Henares, Spain.
| | - Miguel Martinez-Rey
- Electronics Department, University of Alcalá, Engineering School, Campus Universitario, 28871 Alcalá de Henares, Spain.
| | - David Gualda
- Electronics Department, University of Alcalá, Engineering School, Campus Universitario, 28871 Alcalá de Henares, Spain.
| | - Cristina Losada
- Electronics Department, University of Alcalá, Engineering School, Campus Universitario, 28871 Alcalá de Henares, Spain.
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Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles. SENSORS 2018; 18:s18071999. [PMID: 29932130 PMCID: PMC6068971 DOI: 10.3390/s18071999] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 06/20/2018] [Indexed: 11/30/2022]
Abstract
The special issue, “Sensors, Wireless Connectivity and Systems for Autonomous Vehicles and Smart Mobility” on MDPI Sensors presents 12 accepted papers, with authors from North America, Asia, Europe and Australia, related to the emerging trends in sensing and navigation systems (i.e., sensors plus related signal processing and understanding techniques in multi-agent and cooperating scenarios) for autonomous vehicles, including also unmanned aerial and underwater ones.
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