Finding Proper Configurations for Modular Robots by Using Genetic Algorithm on Different Terrains

Klidbary, Sajad Haghzad; Shouraki, Saeed Bagheri; Faraji, Salman

doi:10.7763/ijmmm.2013.v1.78

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…Optimal selection of MC paths and relays minimizes energy consumption [62], [63]. In addition, reducing the loss of diffused molecules plays an essential role in increasing system efficiency.…”

Section: Proposed Genetic Algorithmmentioning

confidence: 99%

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Multi-Hop Genetic-Algorithm-Optimized Routing Technique in Diffusion-Based Molecular Communication

Ansari

Alnajjar

2023

IEEE Access

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Molecular communication (MC) is a modern communication paradigm inspired by biological mechanisms and systems. Due to the short range of molecular diffusion, MC systems necessitate a multihop diffusion-based network to transmit information. Finding the optimal routing path is one of the most critical challenges in MC. The main goal is to transfer information through the diffusion of molecules within an optimal state by detecting the shortest route and the proper relays. In this paper, finding the optimal routing path using a genetic algorithm (GA) is investigated in order to find the shortest and the most energyefficient path. Our model intelligently plans the optimum trajectory between the transmitter (TX) and the receiver (RX) by identifying the appropriate relays both locally and globally. Our GA implementation uses a variable-length chromosome encoding to obtain the optimal path by selecting an appropriate fitness function. We also examine and compare the performance of the proposed algorithm with Dijkstra's algorithm (DA), which is one of the deterministic algorithms. Finally, various simulations for different sizes of MC networks are performed to verify the accuracy of the proposed method. Our simulation results demonstrate that the presented GA offers an accurate routing path within an excellent time, even in large-sized environments.

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Section: Proposed Genetic Algorithmmentioning

confidence: 99%

“…4 depicts an example of a 10 × 10 sequentially numbered environment. We assume a two-dimensional environment consisting of cells which can be considered as a grid/cellular network [62], [63]. Fig.…”

Section: A Environment Representationmentioning

confidence: 99%

Multi-Hop Genetic-Algorithm-Optimized Routing Technique in Diffusion-Based Molecular Communication

Ansari

Alnajjar

2023

IEEE Access

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“…Over the past decade, many different optimization algorithms have been proposed for path planning, including genetic algorithm [16,17], neural networks [18], particle swarming algorithm [15,25], ant colony optimization [19,20], A* algorithm [21,22], Dijkstra's algorithm [23,24]. In [15], a method for planning the path of modular robots based on a genetic algorithm was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], the authors used a neural network to analyze the features of cartographic data and predict the difficulty of finding a route on a map. In article [17], the problem of optimizing the trajectory of a modular robot in the event of a module failure based on a particle swarm algorithm was studied. In this case, the optimization problem is reduced to the selection of the coefficients of the objective function.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Method in the Formation of the Configuration of a Multifunctional Modular Robot Using a Swarm Control Strategy

Petrenko¹,

Тебуева²,

Pavlov³

et al. 2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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Multifunctional modular robots consist of a set of modules that can form a kinematic structure in accordance with the current task. When operating in a non-deterministic environment, the adaptive kinematic structure of the robot allows you to change the configuration and adapt to changing conditions and the limitations of the environment. However, the formation of the required configuration can take a lot of time, which is a problem when it is needed to perform the objective function in real time. Based on this, the purpose of this work is to reduce the time required for the formation of the modular robot configuration. The article proposes a method for path planning of moving modules when forming the configuration of a multifunctional modular robot using a swarm control strategy. This approach allows real-time information exchange between robot modules, and path planning accomplishes decentralized for each module, regardless of their number. The use of analytical geometry methods allows reducing the computational complexity of the method and avoiding the energy consumption of the onboard energy resources of the robot. The results of modeling the developed method for a robot consisting of 5-50 modules are presented.When functioning in a non-deterministic environment, the adaptive kinematic structure of the MSRR allows changing the configuration and adapting to changing conditions and environmental constraints.

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Finding Proper Configurations for Modular Robots by Using Genetic Algorithm on Different Terrains

Cited by 2 publications

References 16 publications

Multi-Hop Genetic-Algorithm-Optimized Routing Technique in Diffusion-Based Molecular Communication

Multi-Hop Genetic-Algorithm-Optimized Routing Technique in Diffusion-Based Molecular Communication

Path Planning Method in the Formation of the Configuration of a Multifunctional Modular Robot Using a Swarm Control Strategy

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