Pau Muñoz-Benavent

In this article, the evolution of BFT (bluefin tuna) sizes in fattening cages is studied, for which it was necessary to perform exhaustive monitoring with stereoscopic cameras and an exhaustive analysis of the data using automatic procedures. Exploring the size evolution of BFT over a long period is an important step in inferring their growth patterns, which are essential for designing smart aquaculture and sustainable fishing, and even assessing their health status. An important objective of this work was to verify whether tuna in captivity, in addition to fattening, grow in length. To this end, our autonomous monitoring system, equipped with stereoscopic cameras, was installed from 28 July 2020 to 23 May 2021 in a fattening cage in the Mediterranean containing 724 free-swimming tuna. This system provides thousands of images that, grouped by time intervals, allow us to conduct our studies. An automatic procedure, already introduced in a previous work and capable of processing large volumes of data, is used to estimate the length and width of individuals in ventral stereoscopic images of fish, and the evolution over time is analysed for each biometric characteristic. However, verifying the evolution of length and width based only on means or medians of these measurements may be inconsistent and insufficiently accurate to support our study objectives, as individuals of different sizes and ages may grow at different rates. Therefore, a modal analysis (Bhattacharya’s method) was undertaken to identify the cohorts within the population. The results showed that each modal length surpassed the length of the next cohort and that there was accelerated growth in cages compared to the wild. In addition, we proved that using a length–width–weight relationship to estimate fish weight gives more accurate results than traditional length–weight relationships for fish fattened in cages.

This paper develops the application of the Dual Rate Dual Sampling Reference Filtering Control Strategy to 2D and 3D visual feedback control. This strategy allows to overcome the problem of sensor latency and to address the problem of control task failure due to visual features leaving the camera field of view. In particular, a Dual Rate Kalman Filter is used to generate inter-sample estimations of the visual features to deal with the problem of vision sensor latency, whereas a Dual Rate Extended Kalman Filter Smoother is used to generate more convenient visual features trajectories in the image plane. Both 2D and 3D visual feedback control approaches are widely analyzed throughout the paper, as well as the overall system performance using different visual feedback controllers, providing a set of results that highlight t he improvements in terms of solution reachability, robustness, and time domain response. The proposed control strategy has been validated on an industrial system with hard real-time limitations, consisting of a 6 DOF industrial manipulator, a 5 MP camera, and a PLC as controller.

This work develops a method to perform surface treatment tasks using a bimanual robotic system, i.e. two robot arms cooperatively performing the task. In particular, one robot arm holds the workpiece while the other robot arm has the treatment tool attached to its end-effector. Moreover, the human user teleoperates all the six coordinates of the former robot arm and two coordinates of the latter robot arm, i.e. the teleoperator can move the treatment tool on the plane given by the workpiece surface. Furthermore, a force sensor attached to the treatment tool is used to automatically attain the desired pressure between the tool and the workpiece and to automatically keep the tool orientation orthogonal to the workpiece surface. In addition, to assist the human user during the teleoperation, several constraints are defined for both robot arms in order to avoid exceeding the allowed workspace, e.g. to avoid collisions with other objects in the environment. The theory used in this work to develop the bimanual robot control relies on sliding mode control and task prioritisation. Finally, the feasibility and effectiveness of the method are shown through experimental results using two robot arms.

A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor’s field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes’ ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna’s Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.

This paper presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, finishing, deburring, etc. The proposed scheme is based on task priority and non-conventional sliding mode control. Furthermore, the proposal includes two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The applicability and feasibility of the proposed collaborative solution for robotic surface treatment are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot.

This work presents an automated solution for tool changing in industrial robots using visual servoing and sliding mode control. The robustness of the proposed method is due to the control law of the visual servoing, which uses the information acquired by a vision system to close a feedback control loop. Furthermore, sliding mode control is simultaneously used in a prioritised level to satisfy the constraints typically present in a robot system: joint range limits, maximum joint speeds and allowed workspace. Thus, the global control accurately places the tool in the warehouse, but satisfying the robot constraints. The feasibility and effectiveness of the proposed approach is substantiated by simulation results for a complex 3D case study. Moreover, real experimentation with a 6R industrial manipulator is also presented to demonstrate the applicability of the method for tool changing.

This work presents a hybrid position-force control of robots for surface polishing using task priority. The robot force control is designed using sliding mode ideas in order to benefit from its inherent robustness and low computational cost. In order to avoid the chattering drawback typically present in sliding mode control, several chattering-free controllers are evaluated and tested. A distinctive feature of the method is that the sliding mode force task is defined using not only equality constraints but also inequality constraints, which are satisfied using conventional and nonconventional sliding mode control, respectively. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. The applicability and the effectiveness of the proposed approach considering the mentioned chattering-free controllers are substantiated by experimental results using a redundant 7R manipulator.