環境エンリッチメント
Smart Food Dispenser
Sensor-triggered feeding that keeps animals moving and foraging.
課題
Food that arrives at the same place and time every day leaves animals with little to do.
Smart Food Dispenser
Natural foraging means searching, moving and working for food. The Smart Food Dispenser detects a nearby animal and releases a small portion, and several units can work together at different times and places to encourage animals to explore and stay active.
動物福祉・栄養
活用シーン
Zoo enrichment
Food that appears in different places to encourage natural foraging.
Animal shelters
Activity and engagement for animals spending long hours in kennels and runs.
Research & care settings
Rule-based feeding for behaviour and enrichment studies.
できること
Detects the animal
Sensors notice when an animal comes close.
Dispenses by rules
Food is released according to the rules you set.
Works as a group
Several units can dispense at varied times and places.
Portion control
Portion limits and cooldowns manage how often food is given.
仕組み
Place
Put one or more dispensers around the enclosure or run.
Set rules
Choose portions, cooldowns and when each unit may dispense.
Enrich
Animals find food as they move and explore.
チームに選ばれる理由
More natural behaviour
Searching and moving for food, as in the wild.
Less routine work
Rules handle the small, frequent feeds.
Controlled portions
Limits and cooldowns stop overfeeding.
Spread through the habitat
Several units turn the whole space into a foraging area.
主な仕様
- Form
- Cylindrical body with an angled feed chute
- Trigger
- Animal presence detected by sensors
- Dispensing
- Small portions by configurable rules
- Control
- Portion limits and cooldowns
- Multi-unit
- Several units can dispense at varied times or locations
こんな現場に
- Animal shelters
- Enrichment areas
- Zoos
- Animal-care and research environments
Planning a pilot?
Tell us about the animals and spaces you want to enrich, and we will plan a dispenser layout.
動物福祉・栄養のその他のデバイス
すべてのデバイス
アンツ・エッジの実際の動きをご覧ください
ご案内をご予約ください。実際のフィールド研究、リアルタイムの音声アノテーション、そしてエッジ上でローカルに動くコンピュータービジョンモデルをご紹介します。モデルは、飼育員や生物学者が実際のカメラ映像を使い、行動ひとつひとつを教えて学習させたものです。
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