Our unmanned surface vehicle, the SeaCat from SubseaTech, scans the area of interest of the sea bottom using a multibeam echosounder, which produces a 3D bathymetry map of the bottom. This serves as a reference map to which all other information about litter will be added. Some large litter like tires or pipes may already be detectable from the bathymetry data, in which case this litter is already marked on the map.
The SeaCat also serves as the "mothership" of the system: all other robots deploy from and return to it. Robots communicate to the USV and get power from it via tethers. The computational resources required for sensing, control, and artificial-intelligence components are also hosted by the SeaCat.
When the water is sufficiently transparent, an unmanned aerial vehicle (UAV or drone) searches for litter from the air. Larger litter pockets are expected to be identifiable in this way, and inform the more detailed search using the underwater robot in the next step. We are also investigating whether we can find correlations between surface and underwater litter pockets.
Our drone is a DJI Matrice M210 RTK, which we are modifying by adding a power and data tether to connect it with the surface vehicle.
In murky waters, the UAV still remains useful by scanning the surrounding area for obstacles.
A small unmanned underwater vehicle (UUV) is deployed from the USV and performs close-up targeted scans of the sea bottom to find smaller litter. To this end, it uses a camera and a forward-looking sonar, together with possibly other sensors such as metal detectors. Identified litter is placed on the reference map.
Litter is identitied with artificial intelligence, deep-learning object recognition techniques. These deep networks are trained so as to differentiate litter from sea-life and thereby ensure the system only collects what it should.
Our observation UUV is the miniTortuga from SubseaTech.
A larger brother of the observation UUV, called the Tortuga, goes to each piece of litter on the map and grabs it with a gripper that is custom-made to interface with the Tortuga. This gripper is equipped with a suction device that will help with picking up litter in difficult circumstances, such as when it is lying among plants. Each piece of litter is reacquired with high accuracy and then picked up.
To plan the paths and control the motion of both the observation and collection UUVs, we exploit intelligent techniques such as reinforcement learning and data-driven control.
A basket is deployed from the USV, and the collection UUV Tortuga takes each piece of litter to deposit it in the basket for transportation to the shore. The basket opening is specially designed to interface effectively with the gripper, and to prevent floating litter to escape back into the water.
The basket is not just a passive component, but actively sends signals to help the collection UUV localize itself relative to the opening.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871295.
The SEACLEAR project spans four years, running from January 1st, 2020 to December 31st, 2023.