AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Abstract

This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

Keywords

• human-machine collaboration
• safety
• monocular depth estimation
• obstacle detection
• mobile robots
• midas