A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
This addresses the need for autonomous support systems for older adults in domestic environments, though it is incremental as it builds on existing deep learning and edge computing techniques.
The paper tackles the problem of enabling assistive unmanned vehicles to detect and follow a person indoors for older adults, resulting in a cost-effective and power-efficient modular system that integrates with existing navigation stacks.
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.