An Inexpensive Flying Robot Design for Embodied Robotics Research

Flying insects are capable of a wide-range of flight and cognitive behaviors which are not currently understood. The replication of these capabilities is of interest to miniaturized robotics, because they share similar size, weight, and energy constraints. Currently, embodiment of insect behavior is primarily done on ground robots which utilize simplistic sensors and have different constraints to flying insects. This limits how much progress can be made on understanding how biological systems fundamentally work. To address this gap, we have developed an inexpensive robotic solution in the form of a quadcopter aptly named BeeBot. Our work shows that BeeBot can support the necessary payload to replicate the sensing capabilities which are vital to bees' flight navigation, including chemical sensing and a wide visual field-of-view. BeeBot is controlled wirelessly in order to process this sensor data off-board; for example, in neural networks. Our results demonstrate the suitability of the proposed approach for further study of the development of navigation algorithms and of embodiment of insect cognition

A lightweight, inexpensive robotic system for insect vision

Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i.e. small size, low weight, and low energy consumption). Research in this area aims to enable robots with similarly efficient flight and cognitive abilities. Visual processing is important to flying insects' impressive flight capabilities, but currently, embodiment of insect-like visual systems is limited by the hardware systems available. Suitable hardware is either prohibitively expensive, difficult to reproduce, cannot accurately simulate insect vision characteristics, and/or is too heavy for small robotic platforms. These limitations hamper the development of platforms for embodiment which in turn hampers the progress on understanding of how biological systems fundamentally works. To address this gap, this paper proposes an inexpensive, lightweight robotic system for modelling insect vision. The system is mounted and tested on a robotic platform for mobile applications, and then the camera and insect vision models are evaluated. We analyse the potential of the system for use in embodiment of higher-level visual processes (i.e. motion detection) and also for development of navigation based on vision for robotics in general. Optic flow from sample camera data is calculated and compared to a perfect, simulated bee world showing an excellent resemblance