Lead: Rodolphe el-Khoury, Christopher Chung, Veruska Vasconez
Team: Stefani Fachini, Melodie Sanchez, Hongyang Wang
Our proposal, Robotic Cloud, utilizes the Sunbrella fabric as an inflatable dynamic mobile shading device capable of shading one individual or large groups of people across the Miami Design District. It is a two-part, worker-hive system loosely inspired by the honey bee colony.
The workers, a.k.a Drones, are automated quadcopters that carry helium inflated hexagons. These hexagon inflatables use Sunbrella marine fabrics that are engineered to withstand sun, wind, and rain. In order to make the fabric airtight, the Sunbrella fabric will be coated on the inside with liquid latex. The Drones are automated using an algorithm determined by the current weather conditions, swarming algorithms, and the visitors desire. As a result, the Drones can be configured in a variety of ways from flocking together and casting large shaded areas like an overcast, to hovering individually so it can be used as a personal shading device. In addition, the quadcopter carries an electronic box holding a Raspberry Pi, camera, pico projector, speaker, and microphone. The addition of these electronic devices gives the Drones greater flexibility to respond and communicate with the visitors desires and needs. The outer edges of the hexagon inflatable are fitted with proximity sensors as well as cathode and anode conductors to be used for charging upon physical contact.
The hives, a.k.a Docking Towers, are independent columns installed throughout the Miami Design District that store and charge the Drones. Each Docking Tower is outfitted with a micro wind turbine at the top to charge the Drones using wind power. The power is delivered to each Drone by cathode and anode rods placed on each side of the column.
As a result, Robotic Cloud proposes a dynamic shading system capable of providing shade for a wide range of conditions at the Miami Design District.
Robotic Cloud was selected as honourable mention in the Laka Reacts Competition 2017.