Additive Building Manufacturing (ABM) is transforming the construction industry through the 3D printing of buildings and building components. A number of countries are now demonstrating ABM can substantially reduce construction time, material and transport costs, improve worker safety standards and alleviate construction's impact on urban traffic congestion and the environment. ABM also provides geometrical variety at no additional cost. In contrast to most manufacturing sectors, variety is a necessity within construction to satisfy different client requirements and adapt to unique terrain, boundary and laws governing each physical site. However, current ABM systems are difficult to deploy on construction sites due to their large size and fixed 3D Print build volumes that are not sufficiently flexible to deal with the complexities of most building scenarios, or provide adequate measures for human safety. These ABM technologies are unable to undertake maintenance and repair work, or construct buildings in many urban or elevated sites. They are also not able to be utilised for post-disaster reconstruction activities where their manufacturing speed would be of great assistance. To address this limitation, this research proposal aims to develop the world's first Aerial Additive Building Manufacturing (Aerial ABM) System consisting of a swarm of aerial robots (Unmanned Aerial Systems (UAS)) that can autonomously assess and manufacture building structures. Aerial ABM offers major improvements to human safety, speed, flexibility, and manufacturing efficiency compared to existing ABM and standard building construction technologies. We have already developed and demonstrated pilot results using UAS that can extrude 3D Print material during flight and we have developed simulation environments that allow for autonomous planning and execution of manufacturing with swarms of UAS working in collaboratively. Using the resources of the EPSRC grant, we will co-develop and demonstrate a working Aerial ABM system that will manufacture structural elements such as walls and a freeform building pavilion. This will require innovation and major technical contributions in Hardware, Autonomy as well as in Materials and Structures. Building on the consortium's world-leading expertise in these areas and support from industrial partners (Skanska, Ultimaker, BuroHappold, Dyson and BRE), we aim at delivering the following main research contributions through this grant: Aerial ABM Hardware - A novel Aerial ABM robot design with autonomous vision based stabilisation, navigation and mapping of a dynamically changing environment that is optimised for flight and 3D Printing tasks. Aerial ABM Autonomy - A framework for autonomous manufacturing that utilises swarm intelligence for collaborative robot-to-robot operations, dynamic task sharing/allocation, adaptive response to context and dynamic environment content involving functions such as new methods of collision avoidance. - Develop new modes of communication and control that enable the safe co-existence and cooperation of human workers, other robots and Aerial ABM robots on construction sites. Novel research in human-robot interaction, feedback and haptic interface functionalities will enable manufacturing flexibility suitable for construction sites that are always unique in size, shape and contextual complexity. - An integrated design and real-time structural analysis software that delivers optimal structural integrity from minimal material weight within building design strategies that leverage this free-form manufacturing process to create innovative building design possibilities. Aerial ABM Materials and Structures - Development of new high-performance 3D-printable composite material and deposition procedures for the additive manufacture (3D Printing) of free-form light-weight building structures utilising autonomous UAS.
|Effective start/end date||1/05/16 → 30/04/20|
- Engineering and Physical Sciences Research Council
Human robot interaction
RCUK Research Areas
- Information and communication technologies
- Artificial Intelligence Technologies
- Manufacturing Machinery & Plant