Intelligent Mobile Olfaction of Swarm Robots

Nurmaini, Siti; Tutuko, Bambang; Thoharsin, Aulia Rahman

doi:10.11591/ijra.v2i4.5085

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…Let random(a, b) denote a random integer between a and b (a and b are included). The number of skills possessed by r i ∈ R was random (1,5), where the skill possessed by r i ∈ R was random(1, l). T SN k = random(1, 4), 1 ≤ k ≤ m, which skill was needed by t k ∈ T was random (1, l).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To get the optimal crossover probability and mutation probability of GGA, a data set was generated with the following random method, the size of which was: n = 30, l = 15, m = 30. The number of skills possessed by r i (r i ∈ R) was random (1,5), with the skill possessed by r i (r i ∈ R) being random (1, l). T SN k = random(1, 4), 1 ≤ k ≤ m, and which skill was needed by t k (t k ∈ T ) was random (1, l).…”

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Coalitional Skill Games for Self-Interested Robots With Svo

Fu¹,

Wang²,

Fang³

2018

International Journal of Robotics and Automation

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Robots in a multi-robot system cannot communicate with each other due to various reasons. The robots can only maximize their own individual performance without regard for the overall system performance. To guarantee high system revenue even when the robots are self-interested, an algorithm called the intermediary recruitment algorithm (IRA) is proposed. It imitates the operating mechanism of an intermediary recruitment market to allocate the tasks to the robots. The simulation results analyse the influence of the robots' social value orientation (SVO) on the system revenue,where SVO is used to measure the degree of self-interest, and the effectiveness of the IRA is also verified.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Coalitional Skill Games for Self-Interested Robots With Svo

Fu¹,

Wang²,

Fang³

2018

International Journal of Robotics and Automation

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show abstract

“…This method explores the ability of animals looking for food sources. Each individual in PSO will be considered as a particle [10,11] in the case of a swarm of robot, robot that represents the particles and the target position represents the available food sources. PSO based multi-robot target search approach is presented in [6,10,12].…”

Section: Related Workmentioning

confidence: 99%

“…Communication between the data base system and the client nodes are conducted by using Internet platform. The mobile sensor node is perhaps the low cost of mobile robot [11].…”

Section: A Mobile Agent Architecturementioning

confidence: 99%

Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET)

Tutuko¹,

Nurmaini²

2014

EECSI

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This paper describes the swarm robots communication and control base Mobile ad-hoc network (MANET). MANET is a source of codes which migrate the network, collects and exchanges information of network nodes. In this work, the communication networks, which do not rely on fixed, preinstalled communication devices like base stations or predefine communication cells. Communications standards are considered in this work use the ad-hoc network such as Wireless LAN, X-Bee/Zig-Bee and Internet platform. All standards are integrated on swarm robots for real experiments. For finding the target, Particle swarm optimization (PSO) algorithm is proposed to control the real swarm robots communication in unknown experiment. As a results swarm robots-base MANET use PSO algorithm produce past response to find the target and swarm robots can move in the group without collision.

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“…In this situation, it needs the modification of the fuzzy-PSO algorithm for swarm robots with target position [12,13]. The process of PSO algorithm is initialized with a group of random particles (solutions), N. The i th particle is represented by its position as a point in an S-dimensional space, where S is the number of variables.…”

Section: A Fuzzy-pso Agorithmmentioning

confidence: 99%

Swarm Robots Control System based Fuzzy-PSO

Nurmaini¹,

Hashim²,

Triadi³

2014

EECSI

Self Cite

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In this paper describes swarm robots control design using combination Fuzzy logic and Particle swarm optimization algorithm. They can communicate with each other to achieve the target. Fuzzy Logic technique is used for navigating swarm robots in unknown environment and Particle Swarm Optimization (PSO) is used for searching and finding the best position of target. In this experiment utilize three identical robots with different color. Every robot has three infrared sensors, two gas sensors, 1 compass sensor and one X-Bee. A camera in the roof of robot arena is utilized to determine the position of each robot with color detection methods. Swarm robots and camera are connected to a computer which serves as an information center. From the experimental results the Fuzzy-PSO algorithm is able to control swarm robots, achieves the best target position in short time and produce smooth trajectory

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Intelligent Mobile Olfaction of Swarm Robots

Cited by 4 publications

References 23 publications

Coalitional Skill Games for Self-Interested Robots With Svo

Coalitional Skill Games for Self-Interested Robots With Svo

Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET)

Swarm Robots Control System based Fuzzy-PSO

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