Handover Control for Human-Robot and Robot-Robot Collaboration

Modern scenarios in robotics involve human-robot collaboration or robot-robot cooperation in unstructured environments. In human-robot collaboration, the objective is to relieve humans from repetitive and wearing tasks. This is the case of a retail store, where the robot could help a clerk to refill a shelf or an elderly customer to pick an item from an uncomfortable location. In robot-robot cooperation, automated logistics scenarios, such as warehouses, distribution centers and supermarkets, often require repetitive and sequential pick and place tasks that can be executed more efficiently by exchanging objects between robots, provided that they are endowed with object handover ability. Use of a robot for passing objects is justified only if the handover operation is sufficiently intuitive for the involved humans, fluid and natural, with a speed comparable to that typical of a human-human object exchange. The approach proposed in this paper strongly relies on visual and haptic perception combined with suitable algorithms for controlling both robot motion, to allow the robot to adapt to human behavior, and grip force, to ensure a safe handover. The control strategy combines model-based reactive control methods with an event-driven state machine encoding a human-inspired behavior during a handover task, which involves both linear and torsional loads, without requiring explicit learning from human demonstration. Experiments in a supermarket-like environment with humans and robots communicating only through haptic cues demonstrate the relevance of force/tactile feedback in accomplishing handover operations in a collaborative task

Control of sliding velocity in robotic object pivoting based on tactile sensing

Control of robots manipulating objects using only the sense of touch is a challenge. In-hand motion of the manipulated object highly depends on the friction forces acting at the contact surfaces. Soft contacts allow torsional frictions as well as friction forces, therefore robots can perform more complex manipulation abilities, like object pivoting. Control of the pivoting sliding motion is very difficult especially without any visual feedback. The paper proposes a novel method to control the sliding velocity of the object by using a simple parallel gripper endowed with force/tactile sensors only. The strategy is based on a nonlinear observer that estimates the sliding velocity from force/torque measurements and a model of the sliding dynamics

Recurring patterns in stationary intervals of abdominal uterine electromyograms during gestation

Abdominal uterine electromyograms (uEMG) studies have focused on uterine contractions to describe the evolution of uterine activity and preterm birth (PTB) prediction. Stationary, non-contracting uEMG has not been studied. The aim of the study was to investigate the recurring patterns in stationary uEMG, their relationship with gestation age and PTB, and PTB predictivity. A public database of 300 (38 PTB) three-channel (S1-S3) uEMG recordings of 30 min, collected between 22 and 35 weeks' gestation, was used. Motion and labour contraction-free intervals in uEMG were identified as 5-min weak-sense stationarity intervals in 268 (34 PTB) recordings. Sample entropy (SampEn), percentage recurrence (PR), percentage determinism (PD), entropy (ER), and maximum length (L MAX) of recurrence were calculated and analysed according to the time to delivery and PTB. Random time series were generated by random shuffle (RS) of actual data. Recurrence was present in actual data (p<0.001) but not RS. In S3, PR (p<0.005), PD (p<0.01), ER (p<0.005), and L MAX (p<0.05) were higher, and SampEn lower (p<0.005) in PTB. Recurrence indices increased (all p<0.001) and SampEn decreased (p<0.01) with decreasing time to delivery, suggesting increasingly regular and recurring patterns with gestation progression. All indices predicted PTB with AUC≥0.62 (p<0.05). Recurring patterns in stationary non-contracting uEMG were associated with time to delivery but were relatively poor predictors of PTB

Formal techniques in the safety analysis of software components of a new dialysis machine

The paper is concerned with the practical use of formal techniques to contribute to the risk analysis of a new neonatal dialysis machine. The described formal analysis focuses on the controller component of the software implementation. The controller drives the dialysis cycle and deals with error management. The logic was analysed using model checking techniques and the source code was analysed formally, checking type correctness conditions, use of pointers and shared memory. The analysis provided evidence of the verification of risk control measures relating to the software component. The productive dialogue between the developers of the device, who had no experience or knowledge of formal methods, and the analyst using the formal analysis tools, provided a basis for the development of rationale for the effectiveness of the evidence. (C) 2019 Elsevier B.V. All rights reserved.This work has been funded by: EPSRC research grants EP/G059063/1 and EP/J008133/1: CHI+MED (Computer -Human Interaction for Medical Devices); and NanoSTIMA (ref. NORTE-01-0145-FEDER-000016) financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Leo Freitas would like to acknowledge EPSRC Trams2 project for financial support, Andrew Sims for providing access to the dialyser, which was used as our case study and Aleksandrs Baklanovs for doing some of the source analysis as part of an undergraduate project

The Candida Genome Database (CGD), a community resource for Candida albicans gene and protein information

The Candida Genome Database (CGD) is a new database that contains genomic information about the opportunistic fungal pathogen Candida albicans. CGD is a public resource for the research community that is interested in the molecular biology of this fungus. CGD curators are in the process of combing the scientific literature to collect all C.albicans gene names and aliases; to assign gene ontology terms that describe the molecular function, biological process, and subcellular localization of each gene product; to annotate mutant phenotypes; and to summarize the function and biological context of each gene product in free-text description lines. CGD also provides community resources, including a reservation system for gene names and a colleague registry through which Candida researchers can share contact information and research interests. CGD is publicly funded (by NIH grant R01 DE15873-01 from the NIDCR) and is freely available at http://www.candidagenome.org/

Sequence resources at the Candida Genome Database

The Candida Genome Database (CGD, ) contains a curated collection of genomic information and community resources for researchers who are interested in the molecular biology of the opportunistic pathogen Candida albicans. With the recent release of a new assembly of the C.albicans genome, Assembly 20, C.albicans genomics has entered a new era. Although the C.albicans genome assembly continues to undergo refinement, multiple assemblies and gene nomenclatures will remain in widespread use by the research community. CGD has now taken on the responsibility of maintaining the most up-to-date version of the genome sequence by providing the data from this new assembly alongside the data from the previous assemblies, as well as any future corrections and refinements. In this database update, we describe the sequence information available for C.albicans, the sequence information contained in CGD, and the tools for sequence retrieval, analysis and comparison that CGD provides. CGD is freely accessible at and CGD curators may be contacted by email at [email protected]

CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations

The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation