EVDodge: Embodied AI for High-Speed Dodging on a quadrotor using event cameras
Project Page: http://prg.cs.umd.edu/EVDodge arXiv Pre-print: https://arxiv.org/abs/1906.02919 After re-imagining the navigation stack of a micro air vehicle as a series of hierarchical competences, we develop a purposive artificial intelligence based formulation for the problem of general navigation. We call this AI framework "Embodied AI'" - AI design based on the knowledge of the agent’s hardware limitations and timing/computation constraints. Following this design philosophy, we develop a complete AI navigation stack for dodging multiple dynamic obstacles on a quadrotor with a monocular event camera and computation. We also present an approach to directly transfer the shallow neural networks trained in simulation to the real world by subsuming pre-processing using a neural network into the pipeline.