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Hong J, Chen D, Li W, Fan Z. Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:7982. [PMID: 37766037 PMCID: PMC10535329 DOI: 10.3390/s23187982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/12/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
Quadrotor unmanned aerial vehicles (UAVs) often encounter intricate environmental and dynamic limitations in real-world applications, underscoring the significance of proficient trajectory planning for ensuring both safety and efficiency during flights. To tackle this challenge, we introduce an innovative approach that harmonizes sophisticated environmental insights with the dynamic state of a UAV within a potential field framework. Our proposition entails a quadrotor trajectory planner grounded in a kinodynamic gene regulation network potential field. The pivotal contribution of this study lies in the amalgamation of environmental perceptions and kinodynamic constraints within a newly devised gene regulation network (GRN) potential field. By enhancing the gene regulation network model, the potential field becomes adaptable to the UAV's dynamic conditions and its surroundings, thereby extending the GRN into a kinodynamic GRN (K-GRN). The trajectory planner excels at charting courses that guide the quadrotor UAV through intricate environments while taking dynamic constraints into account. The amalgamation of environmental insights and kinodynamic constraints within the potential field framework bolsters the adaptability and stability of the generated trajectories. Empirical results substantiate the efficacy of our proposed methodology.
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Affiliation(s)
- Juncao Hong
- College of Engineering, Shantou University, Shantou 515063, China
| | - Diquan Chen
- College of Engineering, Shantou University, Shantou 515063, China
| | - Wenji Li
- College of Engineering, Shantou University, Shantou 515063, China
- Key Laboratory of Intelligent Manufacturing Technology, Shantou University, Ministry of Education, Shantou 515063, China
- International Cooperation Base of Evolutionary Intelligence and Robotics, Shantou University, Shantou 515063, China
| | - Zhun Fan
- College of Engineering, Shantou University, Shantou 515063, China
- Key Laboratory of Intelligent Manufacturing Technology, Shantou University, Ministry of Education, Shantou 515063, China
- International Cooperation Base of Evolutionary Intelligence and Robotics, Shantou University, Shantou 515063, China
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Lei X, Zhang S, Xiang Y, Duan M. Self-organized multi-target trapping of swarm robots with density-based interaction. COMPLEX INTELL SYST 2023. [DOI: 10.1007/s40747-023-01014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023]
Abstract
AbstractThe task of multi-target trapping in swarm robots can often be solved by global shape planning and target assignment, but it still remains a challenge to achieve fully self-organized multi-target trapping behavior based on local information. In this paper, inspired by the concept of spatial density in physics and biology, we proposed a novel density-based method to enable the swarm robots to entrap multiple targets with either single-ring, multi-ring or multi-subgroup formation in a distributed and self-organized way while neither communication among robots nor encirclement function is required. Each robot’s local spatial density is considered as the main clue for the individual’s motion decision-making and the enclosed configurations emerge from such individual-level interactions rather than being explicitly designed. Numerical simulations and real robotic experiments are conducted to validate the effectiveness of the proposed method. The results show that the proposed self-organized trapping method allows a swarm of robots to entrap multiple moving targets in a stable, flexible, noise-tolerate and size-scalable fashion.
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Petersen KH, Napp N, Stuart-Smith R, Rus D, Kovac M. A review of collective robotic construction. Sci Robot 2019; 4:4/28/eaau8479. [PMID: 33137745 DOI: 10.1126/scirobotics.aau8479] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 01/25/2019] [Indexed: 11/02/2022]
Abstract
The increasing need for safe, inexpensive, and sustainable construction, combined with novel technological enablers, has made large-scale construction by robot teams an active research area. Collective robotic construction (CRC) specifically concerns embodied, autonomous, multirobot systems that modify a shared environment according to high-level user-specified goals. CRC tightly integrates architectural design, the construction process, mechanisms, and control to achieve scalability and adaptability. This review gives a comprehensive overview of research trends, open questions, and performance metrics.
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Affiliation(s)
- Kirstin H Petersen
- School of Electrical and Computer Engineering, Cornell University, Rhodes Hall 324, Ithaca, NY 14853, USA
| | - Nils Napp
- University at Buffalo, Buffalo, NY 14260, USA.
| | - Robert Stuart-Smith
- University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Daniela Rus
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Mirko Kovac
- Imperial College of London, London SW7 2AZ, UK.,Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
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García-Calvo R, Guisado JL, Diaz-del-Rio F, Córdoba A, Jiménez-Morales F. Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks. Evol Bioinform Online 2018; 14:1176934318767889. [PMID: 29662297 PMCID: PMC5898668 DOI: 10.1177/1176934318767889] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 02/28/2018] [Indexed: 12/12/2022] Open
Abstract
Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).
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Affiliation(s)
- Raúl García-Calvo
- Department of Computer Architecture and Technology, University of Seville, Seville, Spain
| | - JL Guisado
- Department of Computer Architecture and Technology, University of Seville, Seville, Spain
| | - Fernando Diaz-del-Rio
- Department of Computer Architecture and Technology, University of Seville, Seville, Spain
| | - Antonio Córdoba
- Department of Condensed Matter Physics, University of Seville, Seville, Spain
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Byun H. A reaction-diffusion mechanisms for node scheduling design of wireless sensor networking systems. JOURNAL OF HIGH SPEED NETWORKS 2016. [DOI: 10.3233/jhs-160551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Heejung Byun
- Department of Information and Telecommunication Engineering, Suwon University, 445-745 San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, Korea. E-mail:
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Peng X, Zhang S, Lei X. Multi-target trapping in constrained environments using gene regulatory network-based pattern formation. INT J ADV ROBOT SYST 2016. [DOI: 10.1177/1729881416670152] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Inspired by the morphogenesis of biological organisms, gene regulatory network-based methods have been used in complex pattern formation of swarm robotic systems. In this article, obstacle information was embedded into the gene regulatory network model to make the robots trap targets with an expected pattern while avoiding obstacles in a distributed manner. Based on the modified gene regulatory network model, an implicit function method was adopted to represent the expected pattern which is easily adjusted by adding extra feature points. Considering environmental constraints (e.g. tunnels or gaps in which robots must adjust their pattern to conduct trapping task), a pattern adaptation strategy was proposed for the pattern modeler to adaptively adjust the expected pattern. Also to trap multiple targets, a splitting pattern adaptation strategy was proposed for diffusively moving targets so that the robots can trap each target separately with split sub-patterns. The proposed model and strategies were verified through a set of simulation with complex environmental constraints and non-consensus movements of targets.
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Affiliation(s)
- Xingguang Peng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China
| | - Shuai Zhang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China
| | - Xiaokang Lei
- School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, China
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Applications and design of cooperative multi-agent ARN-based systems. Soft comput 2015. [DOI: 10.1007/s00500-014-1330-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Cussat-Blanc S, Pollack J. Cracking the egg: virtual embryogenesis of real robots. ARTIFICIAL LIFE 2014; 20:361-383. [PMID: 24730763 DOI: 10.1162/artl_a_00136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
All multicellular living beings are created from a single cell. A developmental process, called embryogenesis, takes this first fertilized cell down a complex path of reproduction, migration, and specialization into a complex organism adapted to its environment. In most cases, the first steps of the embryogenesis take place in a protected environment such as in an egg or in utero. Starting from this observation, we propose a new approach to the generation of real robots, strongly inspired by living systems. Our robots are composed of tens of specialized cells, grown from a single cell using a bio-inspired virtual developmental process. Virtual cells, controlled by gene regulatory networks, divide, migrate, and specialize to produce the robot's body plan (morphology), and then the robot is manually built from this plan. Because the robot is as easy to assemble as Lego, the building process could be easily automated.
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Yao Y, Marchal K, Van de Peer Y. Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments. PLoS One 2014; 9:e90695. [PMID: 24599485 PMCID: PMC3944896 DOI: 10.1371/journal.pone.0090695] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Accepted: 02/03/2014] [Indexed: 11/18/2022] Open
Abstract
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.
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Affiliation(s)
- Yao Yao
- Department of Plant Systems Biology, VIB, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
| | - Kathleen Marchal
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
- Department of Information Technology, iMinds, Ghent University, Ghent, Belgium
| | - Yves Van de Peer
- Department of Plant Systems Biology, VIB, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Department of Genetics, Genomics Research Institute, University of Pretoria, Pretoria, South Africa
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Higuera C, Villaverde AF, Banga JR, Ross J, Morán F. Multi-criteria optimization of regulation in metabolic networks. PLoS One 2012; 7:e41122. [PMID: 22848435 PMCID: PMC3406099 DOI: 10.1371/journal.pone.0041122] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Accepted: 06/21/2012] [Indexed: 02/01/2023] Open
Abstract
Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different "environments" (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks.
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Affiliation(s)
- Clara Higuera
- Department of Biochemistry and Molecular Biology, Complutense University, Madrid, Spain
| | | | - Julio R. Banga
- Bio Process Engineering Group IIM-CSIC (Spanish National Research Council), Vigo, Spain
| | - John Ross
- Department of Chemistry, Stanford University, Stanford, California, United States of America
| | - Federico Morán
- Department of Biochemistry and Molecular Biology, Complutense University, Madrid, Spain
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Yaochu Jin, Hongliang Guo, Yan Meng. A Hierarchical Gene Regulatory Network for Adaptive Multirobot Pattern Formation. ACTA ACUST UNITED AC 2012; 42:805-16. [DOI: 10.1109/tsmcb.2011.2178021] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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13
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Distributed Autonomous Morphogenesis in a Self-Assembling Robotic System. MORPHOGENETIC ENGINEERING 2012. [DOI: 10.1007/978-3-642-33902-8_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Jin Y, Meng Y. Morphogenetic Robotics: An Emerging New Field in Developmental Robotics. ACTA ACUST UNITED AC 2011. [DOI: 10.1109/tsmcc.2010.2057424] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Meng Y, Zhang Y, Jin Y. Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model. IEEE COMPUT INTELL M 2011. [DOI: 10.1109/mci.2010.939579] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Meng Y, Jin Y. Distributed Multi-Agent Systems for a Collective Construction Task based on Virtual Swarm Intelligence. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2010. [DOI: 10.4018/jsir.2010040104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, a virtual swarm intelligence (VSI)-based algorithm is proposed to coordinate a distributed multi-robot system for a collective construction task. Three phases are involved in a construction task: search, detect, and carry. Initially, robots are randomly located within a bounded area and start random search for building blocks. Once the building blocks are detected, agents need to share the information with their local neighbors. A distributed virtual pheromone-trail (DVP) based model is proposed for local communication among agents. If multiple building blocks are detected in a local area, agents need to make decisions on which agent(s) should carry which block(s). To this end, a virtual particle swarm optimization (V-PSO)-based model is developed for multi-agent behavior coordination. Furthermore, a quorum sensing (QS)-based model is employed to balance the tradeoff between exploitation and exploration, so that an optimal overall performance can be achieved. Extensive simulation results on a collective construction task have demonstrated the efficiency and robustness of the proposed VSI-based framework.
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Affiliation(s)
- Yan Meng
- Stevens Institute of Technology, USA
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