Introducing autonomous aerial robots in industrial manufacturing

Abstract

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries. It includes modules for accurate pose estimation without using a Global Navigation Satellite System (GNSS), autonomous navigation, radio-based localization, and obstacle avoidance, among others, providing a fully onboard solution capable of autonomously performing complex tasks in dynamic indoor environments in which all necessary sensors, electronics, and processing are on the robot. It was developed to fulfill two use cases relevant in many industries: light object logistics and missing tool search. The presented robotic system, functionalities, and use cases have been extensively validated with Technology Readiness Level 7 (TRL-7) in the Centro Bahía de Cádiz (CBC) Airbus D&S factory in fully working conditions.

Aligning sustainability goals of industrial operations and marketing in Industry 4.0 environment for MSMEs in an emerging economy

“…This feature of drones can help reduce the greenhouse gas emissions leading to improved environmental sustainability and flexibility of the actors of a supply chain. Similarly, autonomous robots seem to have a high impact in the hazardous chemical manufacturing industries, where either human workforce is prone to chemical hazard, or the production process needs to be rid of human touch (Perez-Grau et al , 2021). By classifying the production processes in terms of the risk involved, the human workforce can be appointed to relatively safer departments and robots can take care of the riskier ones.…”

UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

“…A brief description of the size and building characteristics are included in terms of the real-world scene. In terms of vehicle description, the brand, model, and a brief description of those papers with commercial drones (Bachrach et al, 2010;Dang et al, 2020;Fuentes et al, 2014;Konrad et al, 2017;Li et al, 2018;Nakamura et al, 2016Nakamura et al, , 2017Perez-Grau et al, 2021;Tsiourva and Papachristos, 2020;Yu et al, 2017) were included. In general, all articles with real-world tests include the drone sensors' brand and model because many drones were custom built with specific parts for the research.…”

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

“…Finally, local map sharing and merging techniques must be developed in order to enable creating an unique global map resulting from the entire drone fleet. The IoT nodes can be also localized and mapped by the drones using radio range information, see e.g., [16]. The identifiers and locations of these IoT nodes may be used as reference to improve the accuracy of local map merging.…”