Development of a whisker sensor system and simulation of active whisking for agent navigation

Yokoi, Hiroshi; Fend, Miriam; Pfeifer, Rolf

doi:10.1109/iros.2004.1389419

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…However, M. Lungarella [89] and H. Yokoi [175] introduced an artificial whisker sensor with a real mouse whisker attached, hair-like, to a capacitor microphone. In the works of M. Fend [47], [48], the whisker sensors were applied for an obstacle avoidance task and texture discrimination while the use of the sensors for sound detection was not mentioned.…”

Section: An Artificial Auditory-tactile Sensormentioning

confidence: 99%

Neural Preprocessing and Control of Reactive Walking Machines

Manoonpong¹

2007

Cognitive Technologies

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Research in the domain of biologically inspired walking machines has focused for the most part on the mechanical designs and locomotion control. Although some of this research has been concentrated on the generation of a reactive behavior of walking machines, it has been restricted only to a few of such reactive behaviors. However, from this research, there are only few examples where different behaviors have been implemented in one machine at the same time. In general, these walking machines were solely designed for pure locomotion, i.e. without sensing environmental stimuli.Therefore, in this thesis, biologically inspired walking machines with different reactive behaviors are presented. Inspired by obstacle avoidance and escape behavior of scorpions and cockroaches, such behavior is implemented in the walking machines as a negative tropism. On the other hand, a sound induced behavior called "sound tropism", in analogy to the prey capture behavior of spiders, is employed as a model of a positive tropism. The biological sensing systems which those animals use to trigger the described behaviors are investigated so that they can be reproduced in the abstract form with respect to their principle functionalities. In addition, the morphologies of a salamander and a cockroach which are designed for efficient locomotion are also taken into account for the leg and trunk designs of the four-and six-legged walking machines, respectively. Different behavior controls for generating the biologically inspired reactive behaviors are developed on the basis of a modular neural structure. Each behavior control consists of a neural preprocessing module and a neural control module. Preprocessing is for sensory signals while the neural control generates basic locomotion and changes the appropriate motions, e.g. turning left, right or walking backward, with respect to sensory signals. Neural preprocessing and control are formed by realizing discrete-time dynamical properties of recurrent neural networks. Parts of the networks are generated iii iv Abstract and optimized by using an evolutionary algorithm. Utilizing the modular neural structure, the coupling of the neural control module with different neural preprocessing modules leads to the desired behavior controllers, e.g. obstacle avoidance and sound tropism. Furthermore, these behavior controllers are then fused by using a sensor fusion technique consisting of lookup table and time scheduling methods to obtain an effective behavior fusion controller, whereby different neural preprocessing modules have to cooperate.Eventually, all of these reactive behavior controllers together with the physical sensor systems are implemented on the physical walking machines to be tested in a real world environment. The fully equipped walking machines can be seen as artificial perception-action systems. As a result, the walking machine(s) is able to respond to environmental stimuli, e.g. wandering around, sound tropism (positive tropism), avoiding obstacles and even escaping from corners a...

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Section: An Artificial Auditory-tactile Sensormentioning

confidence: 99%

Neural Preprocessing and Control of Reactive Walking Machines

Manoonpong¹

2007

Cognitive Technologies

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“…After measuring the local deformation of the substrate surface, the sensor further calculates the bending deformation of the cantilever and then infers the type, direction, position and frequency of the external pressure [13,14]. Several types of sensors have been used, including resistance [15], Hall effect magnetic [16][17][18], capacitor microphone [19,20], and piezoelectric bimorph [21,22] sensors. This particular type of artificial whisker has a simple structure, can be designed and manufactured using existing semiconductor technology, and has been deeply researched and extensively applied.…”

Section: Introductionmentioning

confidence: 99%

Design and fabrication of an E-whisker using a PVDF ring

Jiang

Wang

et al. 2021

Bioinspir. Biomim.

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Mammalian whiskers can perceive obstacles and airflows. In this study, an electronic whisker (E-whisker) sensor was designed and fabricated by setting a PVDF ring with symmetrical electrodes on the root of a fiber beam. Vibration displacements with different waveforms were applied at the free end of the E-whisker beam to study the relationship between the vibration displacements and the output signals. The E-whisker protrusion sensing ability was investigated by driving it to sweep through the surface of a base platform. A static E-whisker beam and a swinging E-whisker were then separately placed in a wind tunnel to detect the airflow perception of the sensor. The experimental results suggested that the E-whisker could sense the frequencies and amplitudes of displacements at its free end, the height and width of a platform or the heights of other irregular protrusions; the static E-whisker could sense the magnitude or direction of an impact airflow, while the swinging E-whisker could sense the magnitude of a constant airflow. Thus, this kind of E-whisker could perceive the environment and airflow through touch sensation and could be used as a physical model to study the principles and abilities of animal whiskers to perceive obstacles and airflows.

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Development of an MEMS based biomimetic whisker sensor for tactile sensing

Wei

Shi

et al. 2019

2019 IEEE International Conference on Cyborg and Bionic Systems (CBS)

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Development of a whisker sensor system and simulation of active whisking for agent navigation

Cited by 3 publications

References 10 publications

Neural Preprocessing and Control of Reactive Walking Machines

Neural Preprocessing and Control of Reactive Walking Machines

Design and fabrication of an E-whisker using a PVDF ring

Development of an MEMS based biomimetic whisker sensor for tactile sensing

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