Morphological approach for autonomous and adaptive system: The construction of three-dimensional artificial model based on self-reconfigurable modular agents

Yeom, Kiwon

doi:10.1016/j.neucom.2012.12.082

Cited by 5 publications

(4 citation statements)

References 41 publications

(51 reference statements)

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“…For all hardware approaches, the development of controllers to govern the robot swarm remains an open challenge [32]. Control types include, for instance, generalized control to reliably construct any arbitrarily specified artifact (e.g., [33,36]), as well as control to build a specific type of structure (e.g., [13]) or solve a specific task (e.g., [31]). Across types, controllers are advantaged by incorporating stochasticity (cf.…”

Section: Self-organizing Behaviors For Constructionmentioning

confidence: 99%

Using Interactive Evolution to Design Behaviors for Non-deterministic Self-organized Construction

Heinrich

Zahadat

Harding

et al. 2018

Proceedings of the 2018 Symposium on Simulation for Architecture and Urban Design (SimAUD 2018)

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Self-organizing construction is an emerging subdomain for on-site construction robots. This not only presents new challenges for robotics, but due to the stochasticity involved in such systems, impacts the modeling and prediction of resulting built structures. Self-organizing models have been explored by architects for generative design and for optimization, but so far have infrequently been studied in the context of construction. Here we present a strategy for architects to design with non-deterministic self-organizing behaviors, using interactive evolution to incorporate user judgment. We introduce our "Integrated Growth Projection" method, having implemented it into a software pipeline for early phase design. We test the software with an initial user group of architects, to see whether the method and pipeline helps them design a non-deterministic self-organizing behavior. The user group creates several hybrid controllers that reliably solve their chosen design tasks.

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Section: Self-organizing Behaviors For Constructionmentioning

confidence: 99%

Using Interactive Evolution to Design Behaviors for Non-deterministic Self-organized Construction

Heinrich

Zahadat

Harding

et al. 2018

Proceedings of the 2018 Symposium on Simulation for Architecture and Urban Design (SimAUD 2018)

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“…Shape formation was extensively studied for the 2D case, but not as much for 3D shapes. For example, Yeom [16] developed a multi-agent based approach for constructing 3D shapes inspired by biological morphogenesis. Cell processes like differential cell-adhesion, gene-regulation, and inter-cell communication were used as a basis for building the model.…”

Section: Framework Specificationmentioning

confidence: 99%

Taking swarms to the field: A framework for underwater mission planning

Tolba¹,

Ammar²,

Rajasekaran³

2015

2015 IEEE Symposium on Computers and Communication (ISCC)

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Large scale missions in unpredictable and unstable underwater environments are beyond the capabilities of a single intelligent and complex robot. Relying on one robot is far from optimal as loss of the unit means failure of the whole mission. On the contrary, robotic swarms depend on large numbers of simple, cheap, and error-prone robots that exhibit global desirable features like fault-tolerance, scalability, and robustness. The study of underwater robotic swarms from the overall missionplanning perspective has been very limited. Planning specific parts of a mission has mostly been the focus of previous research. We propose a general framework for designing and planning underwater swarm missions. We break a typical mission down into its primary constituents and study the requirements for each submission separately. Initially, the swarm self-organizes to form a shape that maximizes residual energy and minimizes water resistance during transport towards the target. This is followed by path planning and shape maintenance, in which the swarm strives to maintain its collective shape and move in cohesion on a collectively-selected path. Next, another self-organization phase takes place to optimize mission-core accomplishment at the target. Remaining phases, in the back trip, are the counterparts of the first two. Related work in each phase is presented and discussed and future work is also highlighted.

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“…The control tasks of modular robots associated with the formation and reconfiguration of the kinematic structure are given in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The listed works propose a methodological approach to the development of methods and algorithms for reconfiguration the structure of robots with a mechatronicmodular design.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the development of methods and algorithms for reconfiguring modular robots has gained a certain distribution using methods of artificial intelligence (AI), which is reflected in the work of many researchers [14][15][16][17][18][19][20]. In the listed works, the following methods were used: genetic algorithms, artificial neural networks, cellular automata, particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions

Петренко¹,

Тебуева²,

Pavlov³

et al. 2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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Modular robots, consisting of many identical modules, are one of the most difficult areas of robotics. Each newly added element changes the shape and capabilities of the end device, for example, adds functionality or allows the robot to move in new planes. The reconfiguration of the kinematic structure is a sequence of movements of each robot module from the initial position of the initial configuration to the final position of the desired configuration. The paper considers a method for reconfiguration the kinematic structure of a mechatronic-modular robot using reinforcement learning. The proposed method will be built on the basis of a learning algorithm, where the information for training will be the actions taken and the "reward" is a value characterizing the quality of the robot's completion of the target task. The purpose of the training is to build a control algorithm that maximizes the total reward for a certain period of time. The effectiveness of the learning algorithm was tested by computer simulation of a robot, consisting of 5, 10 and 15 modules, in the formation of the target configuration.

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Morphological approach for autonomous and adaptive system: The construction of three-dimensional artificial model based on self-reconfigurable modular agents

Cited by 5 publications

References 41 publications

Using Interactive Evolution to Design Behaviors for Non-deterministic Self-organized Construction

Using Interactive Evolution to Design Behaviors for Non-deterministic Self-organized Construction

Taking swarms to the field: A framework for underwater mission planning

Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions

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