Real-time Stereo Visual Servoing for Rose Pruning with Robotic Arm

The paper presents a working pipeline which integrates hardware and software in an automated robotic rose cutter. To the best of our knowledge, this is the first robot able to prune rose bushes in a natural environment. Unlike similar approaches like tree stem cutting, the proposed method does not require to scan the full plant, have multiple cameras around the bush, or assume that a stem does not move. It relies on a single stereo camera mounted on the end-effector of the robot and real-time visual servoing to navigate to the desired cutting location on the stem. The evaluation of the whole pipeline shows a good performance in a garden with unconstrained conditions, where finding and approaching a specific location on a stem is challenging due to occlusions caused by other stems and dynamic changes caused by the win

Heuristics for the General Multiple Non-linear Knapsack Problem

We propose heuristic algorithms for the multiple non-linear knapsack problem with separable non-convex profit and weight functions. First, we design a fast constructive algorithm that provides good initial solutions. Secondly, we improve the quality of these solutions through local search procedures. We compare the proposed methods with exact and heuristic algorithms for mixed integer non-linear programming problems, proving that our approach provides good-quality solutions in smaller CPU time

Automated Boxwood Topiary Trimming with a Robotic Arm and Integrated Stereo Vision^∗

This paper presents an integrated hardware-software solution to perform fully automated robotic bush trimming to user-specified shapes. In contrast to specialized solutions that can trim only bushes of a certain shape, the approach ensures flexibility via a vision-based shape fitting module that allows fitting an arbitrary mesh into a bush at hand. A trimming planning method considers the available degrees of freedom of the robot arm to achieve effective cutting motions. The performance of the mesh fitting module is assessed in multiple experiments involving both artificial and real plants with a variety of shapes. The trimming accuracy of the overall approach is quantitatively evaluated by inspecting the bush pointcloud before and after robotic trimming, and measuring the change in the deviation from the originally computed target mesh.</p

Local interfractional setup reproducibility for 2 individual head and neck supports in head and neck cancer patients

In radiation therapy, head and neck (H&N) supports and thermoplastic masks are used to reproduce the setup of patients for H&N treatment. Individualized supports that include the shoulders may improve the immobilization of the upper thorax region. The purpose of this study was to compare the local misalignment of the supraclavicular region using a vacuum cushion H&N support to a more simple in-house modified, clinically standard H&N support. Two groups of 15 patients were evaluated: the first group of patients was positioned using a vacuum cushion as an individual head support and the second group with a modified Posifix headrest (MPH). A total of 316 cone beam computed tomography (CBCTs; ~ 10 scans per patient) were evaluated using a multiple region of interest registration protocol. Local setup errors were measured using chamfer matching on the CBCT scan to the planning CT scan for 9 bony structures (cervical vertebrae 1, 3, 5, and 7 [C7], lower jaw, hyoid bone, larynx, skull, and jugular notch). In this study, we compared the local residual misalignments of the bony structures and in particular those of the jugular notch and C7 as surrogates of the shoulders and thorax region. The workload was qualitatively evaluated on the basis of open interviews. The significant differences in group mean, systematic error, and random error of the local residual misalignments between the 2 groups for jugular notch and C7 were equal or smaller than 0.5 mm and 0.1 degrees, and for the other 7 bony structures were equal to or smaller than 0.6 mm and 1.2 degrees (larynx). There were no large differences in workload. No clinically relevant differences were found between a modified Posifix headrest and an individual vacuum cushion for H&N cancer patients in local posture change at the level of the clavicle and upper thora

Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review

This paper presents and evaluates a method for detecting and counting demersal fish species in complex, cluttered, and occluded environments that can be installed on the conveyor belts of fishing vessels. Fishes on the conveyor belt were recorded using a colour camera and were detected using a deep neural network. To improve the detection, synthetic data were generated for rare fish species. The fishes were tracked over the consecutive images using a multi-object tracking algorithm, and based on multiple observations, the fish species was determined. The effect of the synthetic data, the amount of occlusion, and the observed dorsal or ventral fish side were investigated and a comparison with human electronic monitoring (EM) review was made. Using the presented method, a weighted counting error of 20% was achieved, compared to a counting error of 7% for human EM review on the same recordings

Data underlying the publication: Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review

Data for training and evaluation of a method for detection and counting demersal fish species in complex, cluttered and occluded environments that can be installed on the conveyor belts of fishing vessels. The data mainly exists of images of fish on a conveyer belt with the corresponding annotations. This was used to train a neural network (YOLOv3) to detect and classify fish species. Because each fish is visible in multiple images, the fishes were tracked over consecutive images and the total number of fish per specie was counted. These counts were compared to human review

Correction strategies to manage deformations in head-and-neck radiotherapy

Background and purpose: To optimize couch shifts based on multiple region-of-interest (ROI) registrations and derive criteria for adaptive replanning for management of deformations in head-and-neck (H&N) cancer patients.Materials and methods: Eight ROIs containing bony structures were defined on the planning-CT and individually registered to daily cone-beam CTs for 19 H&N cancer patients. Online couch shifts were retrospectively optimized to correct the mean setup error over all ROIs (mean correction) or to minimize the maximum error (MiniMax correction). Residual error distributions were analyzed for both methods. The number of measurements before adaptive-intervention and corresponding action-level were optimized.Results: Overall residual setup errors were smallest for the mean corrections, while MiniMax corrections reduced the largest errors. The percentage of fractions with residual errors >5 mm was 38% versus 19%. Reduction of deformations by single plan adaptation was most effective after eight fractions: systematic deformations reduced from 1.7 to 0.9 mm. Fifty percent of this reduction can already be achieved by replanning 1/3 of the patients.Conclusion: Two correction methods based on multiple ROI registration were introduced to manage setup errors from deformations that either minimize overall geometrical uncertainties or maximum errors. Moreover, the registrations could be used to select patient with large deformations for replanning. (C) 2009 Elsevier Ireland Ltd. All rights reserved. Radiotherapy and Oncology 94 (2010) 199-205Biological, physical and clinical aspects of cancer treatment with ionising radiatio