Aquaculture Europe 2023

September 18 - 21, 2023


Add To Calendar 19/09/2023 14:15:0019/09/2023 14:30:00Europe/ViennaAquaculture Europe 2023RESIFARM: TOWARDS RESILIENT ROBOTIC AUTONOMY FOR UNDERWATER OPERATIONS IN FISH FARMSStolz 2The European Aquaculture Societywebmaster@aquaeas.orgfalseDD/MM/YYYYaaVZHLXMfzTRLzDrHmAi181982


Marios Xanthidis1*, Eleni Kelasidi1, Kostas Alexis2

1Aquaculture Technology Department, SINTEF Ocean, Trondheim, Norway
2Department of Engineering Cybernetics, NTNU , Trondheim, Norway


Aquaculture is an established industrial sector of progressively increasing importance for food production as an alternative that minimizes negative impact on climate change. Continuously growing demand of aquaculture infrastructure  introduces important yet-to-be-addressed technological and logistical challenges, with reliance solely on human operators becoming unattainable in the future. Robotics and automation could become a leading force on expanding aquaculture operations and resolving scalability issues in a consistent and sustainable way [1] . However,  exposed  aquaculture settings are among the most challenging domains for robots to operate, due to uncertainty introduced from limited and low-quality sensor readings, lack of static reference caused by changing surroundings due the constant deformation of the fish cage structure, and necessity for real-time decision in environments with currents, surge, moving obstacles, and uncertainty. The ResiFarm project, aims to address such problems regarding scalability and safe operations in exposed aquaculture settings by providing fundamental technologies enabling  underwater  robots to operate safely, accurately, and resiliently. The potential of d ifferent types of autonomous robots  is explored for the produced technology, such as ROVs and Underwater Swimming Manipulators (USMs), Figure 1.

Resilient autonomy depends on solving robustly two fundamental problems in robotics: State Estimation, which is the problem of localizing the robot with its surroundings, and Motion Planning, which is the problem of deciding on efficient and safe actions that the robot could take towards accomplishing a task. Unfortunately, both are becoming more challenging to solve in the underwater domain, and especially in aquaculture settings.  The focus of this paper lies on safe and robust motion planning.

 Even assuming perfect state estimation — a prerequisite for motion planning — safe autonomous aquaculture operations require addressing simultaneously and in real-time a combination of unique challenges, such uncertainty, unexpected surge and currents, potential control errors, moving obstacles, and  moving deformable nets. Several motion planning approaches exist , such as sampling-based, lattice-based ,  and optimization-based motion planning . We focused on path optimization, due to its computational efficiency, quick replanning frequency, and guarantees, as shown in past work [2 ].

Such techniques  can provide very quickly locally optimal solutions ,  which are sufficient given the limited on-board sensing range. Though, the most important advantage of these techniques is that user-defined cost functions could be implemented making them highly adjustable to different platforms, and for different tasks, such as for inspection [3].

 Towards  enabling autonomy, a novel framework , called ResiNav, was developed that guarantees safe navigation in the presence of motion errors due to currents, waves, or imperfect controls, localization and map uncertainty, and detected dynamic obstacles. ResiNav [4] deals with all these challenges in real-time in a holistic perspective by informing the path optimization process with the past experienced conditions, adjusting automatically the clearance needed. The necessary clearance is computed analytically with minimal information and provides a tight worst-case boundary  that guarantees safety.

Currently,  several motion planning concepts  have been  tested i n simulation.  The AquaVis [3]  pipeline was  applied for cage inspection .  Virtual visual objectives were placed on the net , enabling the robot to inspect it from a desired proximity. The robot inspected the net , while also avoiding  safely dynamic obstacles  executing unknown trajectories in simulation, Figure 2. Additionally, ResiNav has been implemented and rigorously tested for different conditions, environments, and robot velocities in simulation .  An example of one test case with strong side current is shown below in Figure 3. We speculate that, almost certainl y, a motion planner with such capabilities  will be at the core  of future  safe  and robust  autonomy applied  for ope rations in  exposed aquaculture infrastructure.

Conclusion and Future Work

 A collection of approaches is investigated to realize the ambitious goals of ResiFarm, towards  robust motion planning, with a subset of them described briefly above. Future focus will be directed towards data collection and testing of  robust motion planning concepts in real fish farms (infrastructure provided by SINTEF ACE) using ROVs and investigating extensions to different platforms, such as the Eelume USM. We aspire that the result from ResiFarm would provide fundamental technologies  and  insights to support scaling aquaculture operations in the future , safely, accurately, and resiliently.


 This work was financed by the Research Council of Norway through the project



[4]                       M. Xanthidis, et al., 2023 , ResiPlan: Closing the Planning-Acting Loop for Safe Underwater Navigation, ICRA. (Accepted)