Closed-loop Locomotion Analyzer for Investigating Context-dependent Collision Avoidance Systems in Insects

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  • 身体と環境の相互作用に基づく昆虫の衝突回避機構解明のための閉ループ行動実験装置の構築
  • シンタイ ト カンキョウ ノ ソウゴ サヨウ ニ モトズク コンチュウ ノ ショウトツ カイヒ キコウ カイメイ ノ タメ ノ ヘイループ コウドウ ジッケン ソウチ ノ コウチク

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Abstract

Insects are capable of extremely rapid collision avoidance behaviors with a minimum of processing overhead. These features make them interacting for robotics. To implement the strategies of insect in mobile robots or cars, a through evaluation of these systems is necessary. We developed a closed-loop experimental system for analyzing insect collision avoidance behavior in virtual environments. In the current implementation, a tethered female cricket walks on a floating sphere and the rotations of the sphere are translated into movements of a “virtual cricket” in a computer-generated virtual space. Visual information including obstacle and background patterns in the virtual space are then fed back to the tethered cricket as visual stimuli projected onto a screen in front of the cricket. To induce reliably reproducible straight-line walking, we applied the male calling song that induces positive phonotaxis in female crickets. We demonstrated that the tethered female cricket displayed collision avoidance behavior in response to visual stimuli during positive phonotaxis. By employing this system, we investigated a key stimulus that triggered collision avoidance behavior in crickets in different behavioral contexts: a cricket approaching a static object and an object moving towards a quiescent cricket. The results indicate that crickets used a certain threshold of image size (visual angle) of the projected object as a key stimulus. Furthermore, we found that the threshold depended on the behavioral context: quiescent crickets started avoidance farther from the approaching object compared to crickets walking towards a static object. We conclude that behavioral context is an important factor in decision making. With our closed-loop system for behavioral analysis, we can systematically extract the conditions under which optimal behavioral performance is obtained. This will be an important step in the design of sensory processors for robots.

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