Predictive Trawl Net Position Control to Avoid Seabed Contact
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Solution Overview
Problem
Trawling methods, particularly bottom trawling, cause environmental damage, increase fuel consumption, and result in undesirable bycatch due to inaccurate position control of trawl nets, which can lead to bottom contact and non-targeted fish capture.
Innovation Solution
Implementing semi-autonomous or autonomous position control systems for trawl nets using user-defined control modes and setpoints, combined with sensor data and predictive algorithms to adjust the trawl net's position, enabling accurate control and obstacle avoidance, thereby reducing environmental impact and improving catch selectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bottom trawling is used to maximize catch quantity, then productivity increases, but environmental damage and fuel consumption increase due to bottom contact
Solution Approach 1:
The system dynamically adjusts the trawl net's depth position in real-time based on bathymetry data and sensor feedback. The net transitions from static bottom trawling to dynamic midwater or hybrid trawling, automatically adapting to seabed topography to avoid contact while maintaining fishing efficiency
Solution Approach 2:
The system uses sensor data from echosounders and other detection devices to continuously monitor the trawl net's position relative to the seabed. This feedback loop enables automatic adjustment of net depth and towing parameters to prevent bottom contact, reducing environmental damage while maintaining catch productivity
2Use of energy by moving object
If midwater trawling is used to reduce environmental impact, then fuel consumption decreases, but position control accuracy deteriorates leading to bottom contact
Solution Approach 1:
The system introduces intermediary components including echosounders, depth sensors, and control systems that act as mediators between the vessel and the trawl net. These intermediaries provide continuous position monitoring and automatic control adjustments, enabling accurate midwater position control without requiring constant manual intervention
Solution Approach 2:
The system replaces manual mechanical position control with automated electronic control systems. The control system processes sensor data and automatically adjusts winch operations and net depth, substituting human-operated mechanical systems with automated electro-mechanical control to improve position accuracy in midwater trawling
3Device complexity
If manual position control is used to simplify the system, then device complexity decreases, but position control accuracy deteriorates resulting in bottom contact and ghost gear
Solution Approach 1:
The system enables the trawl net to control its own position automatically through self-service mechanisms. The integrated control system monitors net depth, processes bathymetry data, and adjusts towing parameters without external intervention, making the system self-regulating and reducing reliance on complex manual control while improving reliability
4Device complexity
If sensor data frequency is reduced to simplify data processing, then processing complexity decreases, but position control responsiveness deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and filtering sensor data in real-time, preparing it for rapid control responses. Data filtering, validation, and predictive algorithms are applied in advance to reduce processing complexity while maintaining high responsiveness to position changes and seabed features
Data Source
AI summary
A method of controlling position of a trawl net is provided. The method may comprise: receiving, by a processor, a control input, wherein the control input comprises: (i) a selection of one of a plurality of position control modes, and (ii) a position control setpoint for the trawl net; receiving, by the processor, sensor data indicating a sensed position of the trawl net; and generating, by the processor, a trawl position control output based on the control input, the sensor data and predicted trawl net position data. The predicted trawl net position data may be generated by: defining, by the processor, a plurality of parameters; receiving, by the processor, input data comprising vessel speed data, tow line payout data, and a candidate drag coefficient value for the trawl net; and determining, by the processor, a tow angle based on the plurality of parameters and the input data.


