Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~7% of a typical body dimension, and orientation error of 12 degrees, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Comment: 15 pages, including supplementary figures. 1 supplemental movie available as an ancillary fil

Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor

Manipulation tasks often require robots to be continuously in contact with an object. Therefore tactile perception systems need to handle continuous contact data. Shear deformation causes the tactile sensor to output path-dependent readings in contrast to discrete contact readings. As such, in some continuous-contact tasks, sliding can be regarded as a disturbance over the sensor signal. Here we present a shear-invariant perception method based on principal component analysis (PCA) which outputs the required information about the environment despite sliding motion. A compliant tactile sensor (the TacTip) is used to investigate continuous tactile contact. First, we evaluate the method offline using test data collected whilst the sensor slides over an edge. Then, the method is used within a contour-following task applied to 6 objects with varying curvatures; all contours are successfully traced. The method demonstrates generalisation capabilities and could underlie a more sophisticated controller for challenging manipulation or exploration tasks in unstructured environments. A video showing the work described in the paper can be found at https://youtu.be/wrTM61-pieUComment: Accepted in ICRA 201

Software Reuse across Robotic Platforms: Limiting the effects of diversity

Robots have diverse capabilities and complex interactions with their environment. Software development for robotic platforms is time consuming due to the complex nature of the tasks to be performed. Such an environment demands sound software engineering practices to produce high quality software. However software engineering in the robotics domain fails to facilitate any significant level of software reuse or portability. This paper identifies the major issues limiting software reuse in the robotics domain. Lack of standardisation, diversity of robotic platforms, and the subtle effects of environmental interaction all contribute to this problem. It is then shown that software components, fuzzy logic, and related techniques can be used together to address this problem. While complete software reuse is not possible, it is demonstrated that significant levels of software reuse can be obtained. Without an acceptable level of reuse or portability, software engineering in the robotics domain will not be able to meet the demands of a rapidly developing field. The work presented in this paper demonstrates a method for supporting software reuse across robotic platforms and hence facilitating improved software engineering practices

The PRIMO FORTE framework for good governance in public, private and civic organisations : an analysis on small EU states

Purpose: In this article we lay out and discuss a framework proposed by the Public Risk Management Organisation (PRIMO) (https://www.primo-europe.eu/) of which the authors are board members and the results of a test on public and private entities of EU small jurisdictions, specifically Malta, Slovenia, Luxembourg, Lithuania, Latvia, Estonia and Cyprus. These are countries within the EU having less than 3 million people population. Design/methodology/approach: We collected our primary data by using a semi-structured questionnaire and administering it to participants who are working directly or indirectly with entities within these EU states. The questionnaire was structured using the FORTETM acronym as themes, ‘Financial and compliant design’, ‘Object orientation and delivery’, ‘Responsibility and stewardship’, ‘Tools and processes for creation’ and ‘Environmental awareness and interaction’, with 5 statements under each theme to which participants were required to answer using a 5-point Likert-scale ranging from “Strongly Disagree” to “Strongly Agree”. We, however, allowed the participants to open up and discuss each statement and recorded these comments. Some demographic data was also collected as to the type of entity the participants are working with, the level of expertise on governance of the participant and the size of the entity. The quantitative data was subjected to statistical analysis while the results from the open ended question was analysed using the Thematic approach. Findings: Factor analysis provided support for the FORTE Good Governance model for both the Private and Public entities, no-matter if they are small or large. Originality/value: The study provides a better understanding and supports the FORTE Model established by PRIMO-Europe, after approximately 15 years of collecting data on public risks and for the first time tests it on both Private and Public entities, in large and small firms in small EU Jurisdictions. Moreover, this model contributed to the vast literature on models of risk management within organisations, but was not validated empirically for reliability of the factors, and on small jurisdictions. Therefore, the significance and importance of such a study lies firstly on the premise that testing on small countries, can be deemed as small laboratories for more complex politics, regulations and policies of larger countries.peer-reviewe