Abstract
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 work. 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.
Highlights
Recent improvements in sensors, processing, and batteries have made new technologies low-weight, low-power, and low-cost
Flying insects are of interest to the design of small robotic platforms, because they represent a complete working solution which is capable of the behaviours required for performing in challenging environments
The robotics embodiment testing involved taking a series of images and videos of well-defined images along a hallway navigation task
Summary
Recent improvements in sensors, processing, and batteries have made new technologies low-weight, low-power, and low-cost This has allowed robots, sUAVs (small Unmanned Aerial Vehicles), to be more accessible and users to broaden their applications. Flying insects are capable of sophisticated odometry, including estimating flight duration, integrating their course over time to generate a direct home vector (‘path integration’), and regulating their flight speed (Srinivasan et al, 1996, 2015). They can perform smooth landing on unfamiliar targets, and optimise routing around a set of target locations in a few flights (Lihoreau et al, 2012). Current computational research (e.g. Green Brain Project and “Brains on Board” Project) is trying to model and embody these behaviours to show how bee's physiology is able to accomplish these impressive tasks with such efficient coding and processing of information (Cope et al, 2013)
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