scholarly journals Stick Insect Antennae

Scholarpedia ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. 6829 ◽  
Author(s):  
Volker Dürr
Keyword(s):  
Stick Insect ◽  
Insect Antennae

scholarly journals Improving the Detection of the Contact Point in Active Sensing Antennae by Processing Combined Static and Dynamic Information

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1808
Author(s):  
Luis Mérida-Calvo ◽  
Daniel Feliu-Talegón ◽  
Vicente Feliu-Batlle
Keyword(s):  
Contact Point ◽  
Load Cell ◽  
Robotic Systems ◽  
Accurate Estimation ◽  
Flexible Beam ◽  
Dynamic Information ◽  
New Developments ◽  
Static Information ◽  
Insect Antennae ◽  
Flexible Antenna

The design and application of sensing antenna devices that mimic insect antennae or mammal whiskers is an active field of research. However, these devices still require new developments if they are to become efficient and reliable components of robotic systems. We, therefore, develop and build a prototype composed of a flexible beam, two servomotors that drive the beam and a load cell sensor that measures the forces and torques at the base of the flexible beam. This work reports new results in the area of the signal processing of these devices. These results will make it possible to estimate the point at which the flexible antenna comes into contact with an object (or obstacle) more accurately than has occurred with previous algorithms. Previous research reported that the estimation of the fundamental natural frequency of vibration of the antenna using dynamic information is not sufficient as regards determining the contact point and that the estimation of the contact point using static information provided by the forces and torques measured by the load cell sensor is not very accurate. We consequently propose an algorithm based on the fusion of the information provided by the two aforementioned strategies that enhances the separate benefits of each one. We demonstrate that the adequate combination of these two pieces of information yields an accurate estimation of the contacted point of the antenna link. This will enhance the precision of the estimation of points on the surface of the object that is being recognized by the antenna. Thorough experimentation is carried out in order to show the features of the proposed algorithm and establish its range of application.

Outbreak of the stick insect, Ramulus mikado (Phasmatodea, Phasmatidae), in the Akashina area of Japan (Azumino City, Nagano Prefecture)

2021 ◽  
Vol 24 (2) ◽  
pp. 196-200
Author(s):  
Koki Yano ◽  
Takahisa Ozaki ◽  
Tomoya Suzuki ◽  
Haruka Yamazaki ◽  
Masayoshi Nasuno ◽  
...  
Keyword(s):  
Stick Insect ◽  
Nagano Prefecture

Space constancy in form perception by the stick insect

1996 ◽  
Vol 83 (7) ◽  
pp. 323-324 ◽  
Author(s):  
L. Frantsevich ◽  
L. Frantsevich
Keyword(s):  
Stick Insect ◽  
Form Perception ◽  
Space Constancy

scholarly journals Simplified Phasmatodea population evolution algorithm for optimization

2021 ◽  
Author(s):  
Pei-Cheng Song ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
Hongmei Yang
Keyword(s):  
Population Growth ◽  
Path Dependence ◽  
Optimization Problems ◽  
Stick Insect ◽  
Insect Population ◽  
Engineering Optimization ◽  
Population Evolution ◽  
Evolution Path ◽  
Evolution Characteristics ◽  
Evolution Algorithm

AbstractThis work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

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