Foraging Under Fire: A Robotic Flower System Incorporating Multimodal Signaling and Aversive Stimuli

Abstract

Artificial flowers have long been used in pollinator research to understand and manipulate key floral features such as rewards and display. Increased access to 3D printing and Internet of Things (IoT) technologies has expanded the capabilities of artificial flowers, enabling more precise control and real-time data collection. These IoT-enabled artificial flowers, referred to as robotic flowers or robo-flowers, integrate single-board computers, such as the Raspberry Pi series or similar embedded system devices, as well as affordable camera and sensor modules. However, despite their flexibility and modularity, the majority of robotic flowers are designed to investigate how pollinators make foraging decisions based on visual cues linked to floral rewards, with less attention paid to the broader information landscape that pollinators use to decide which flowers to visit. We have developed a robotic flower system that extends this approach to incorporate multimodal signaling capabilities as well as aversive floral stimuli. These stimuli were designed to allow for investigation into the more nuanced information tradeoffs that feature in pollinators foraging decisions, but the designs could be broadly useful for researchers interested in understanding insect nociception, decision-making, and apparent predation in the context of plant-pollinator interactions.