An omnidirectional surface vehicle with the ability to inspect fish cage in aquaculture sites
May 11, 2018 | Atlanta, GA
Aquaculture currently provides around half of the human consumption on fish, which will raise to about two-thirds by 2030. However, significant economic loss and environmental impact may happen if the farm fish escape from the holding facilities. Damaged fish cage is the dominant cause for escapes. Currently, the cages are inspected by divers. This is a risky job for human life and health. Such risk to the divers may raise significantly as fish farms are being deployed further away from the shore.
Underwater remotely operated vehicles (ROVs) have been tested for aquaculture applications. However, cost of an ROV is usually too high for many fish farms, and experienced operators are needed to drive the tethered vehicle in complex environment like aquaculture sites. A more affordable robotic platform is needed to facilitate frequent inspection and early repair of the fish cage nets.
Researchers at the Georgia Tech Systems Research Lab (GTSR) have developed an omni-directional surface vehicle (OSV) that achieves unmanned fish cage inspection. The unique design of the OSV allows safe and convenient operation in fish farms. The underwater camera of the OSV features motorized depth adjustment, which can inspect the cage at different depths. Algorithms onboard the OSV can recognize damage to the net in real-time. During each inspection, photos and locations of the damage are saved automatically for further analysis and repair.
- Aquaculture facility inspection
- Submerged infrastructure inspection
- Rescue and recovery operations
- Environmental monitoring
- Mobile underwater acoustic communication gateway
- Robotic platform for research and education
Benefits & Advantages:
- Low-cost, easy to operate and maintain
- Real-time fish cage net damage detection
- Safe operation in complex environment like fish farm
- Multihull design, good stability, manuvability, and redundency
- Modular and multipurpose design, good expandability
Qiuyang Tao – PhD student
Bo Guo - Undergraduate Student
Kuo Huang - Undergraduate Student
Robin Lam - Undergraduate Student
Chang Qin - Undergraduate Student