The execution of material handling tasks using autonomous guided vehicles (AGVs) has proven a real success during the last decade. Nevertheless, the installation of AGVs is costly as it needs to modify the workshop's configuration by defining dedicated movement zones. Recently, more flexible and collaborative mobile robots known as autonomous intelligent robots (AIV) can be used in manufacturing systems. This new generation of intelligent mobile robots does not need specific zones and can interact with unexpected or mobile obstacles such as human operators. This paper focuses on AIV fleet size definition in a variable and unexpected environment with humans while keeping AIV assigned transportation tasks on time. A simulation that model the complexity of the AIV travel time estimation under the mentioned circumstances and the improvement brought by IoT, Big Data and sensors by using them as the real-time data source is developed.

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The CoRoT project is a collaboration of French and UK partners funded by the EU Interreg Programme. The CoRoT research group has developed a mobile robot demonstrating autonomous operations particularly aiming for applications within flexible manufacturing factories of small to medium enterprises (SMEs). The robot’s capability of full separation classifies it as a modular robotic system. Although the separation is possible, it introduces challenges in the areas of staff training, physical labour and workshop planning. This paper introduces a new method resolving the overt issues of lifting the robot, storing the robot and making use of the two systems as well as highlighting emergency stop regulations and maintaining the warranty of the equipment. By using a steel framed pneumatic lift, the top module can be removed and held in place, anchoring the arm for full operations whilst freeing the base to perform delivery tasks enabling seamless separation of modular robotic systems.

Using a combination of active Radio-Frequency Identification tracking and staff interviews with members from an aerospace manufacturing company, it was uncovered that over 80 hours per week was spent in the manual movement of goods between departments. On a site of over 1000 employees that uses dedicated build cells in separated departments, this mixed-use facility proves challenging for the adoption of an autonomous delivery system due to its narrow corridors and high occupancy, however by investigating the concerns of employees and suggesting low-cost retroactive solutions, this project seeks to justify the transition from manual to automated onsite logistics. The conclusion found that indeed the company does have the transport yields to justify the use of Autonomous Mobile Robots, that the robots would supplement rather than replace workers and that safety was a key factor to address when using robots on a site of this configuration.

Manufacturing is a knowledge-intensive business in which companies are distinguished for their ability to deliver products and services using numerous forms of information and knowledge. Various information and communication technologies and systems have been created throughout the years to effectively manage information and knowledge in industry. However, capturing knowledge, particularly tacit knowledge, remains a challenge. Virtual Reality (VR) and Augmented Reality (AR) technologies for industrial tasks have been developed by the research and practitioner communities. For example, in manufacturing, VR/AR have been used to aid in different machining and assembly tasks. This paper presents a proposed an AR equipment ‘Head Mounted Device (HMD)’ under development, as a tool for capturing manufacturing expertise’s knowledge. The technology intends to capture tacit knowledge using a 3D camera installed on the operator’s HMD. The proposed approach also supplies the operator with information on production and ongoing processes.