In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

We examined how individual and institutional factors in health care settings affected discrimination toward persons with HIV/AIDS. A representative sample of 1101 Chinese service providers was recruited in 2005, including doctors, nurses, and laboratory technicians. Multiple regression models were used to describe associations among identified variables, the relationships with HIV-related personal prejudicial attitudes, and perceived institutional support and discrimination at work. Multivariate analyses revealed that respondents' general view of persons living with HIV/AIDS and their perceived levels of support from their institutions regarding protection procedures were both important predictors for discrimination intent. Perceived institutional support varied according to age, gender, ethnicity, and training background. A better understanding of HIV-related discrimination in health care settings requires consideration of both individual and institutional factors.

Human detection and tracking are essential aspects to be considered in service robotics, as the robot often shares its workspace and interacts closely with humans. This paper presents an online learning framework for human classification in 3D LiDAR scans, taking advantage of robust multi-target tracking to avoid the need for data annotation by a human expert. The system learns iteratively by retraining a classifier online with the samples collected by the robot over time. A novel aspect of our approach is that errors in training data can be corrected using the information provided by the 3D LiDAR-based tracking. In order to do this, an efficient 3D cluster detector of potential human targets has been implemented. We evaluate the framework using a new 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments analyse the real-time performance of the cluster detector and show that our online learned human classifier matches and in some cases outperforms its offline version.

This study assessed the effect of a brief intervention aimed at reducing HIV-related stigma among service providers in China. From December 2005 to June 2006, 138 service providers from four county hospitals in the Yunnan province of China were randomly assigned into either an intervention or a control condition. HIV stigma reduction concepts were conveyed through participatory small group activities, including role-plays, games, group discussions, and testimony by an HIV advocate. Participants were assessed at baseline before the intervention, and at 3- and 6-month follow-ups. Data were analyzed using a logistic regression mixed-effects model. Service providers in the brief intervention condition were significantly more likely to report better protection of patients' confidentiality and right to HIV testing, lower levels of negative feelings toward people living with HIV/AIDS, and more accurate understanding and practice of universal precautions. This brief intervention pilot showed potential in reducing HIV stigma and discrimination among service providers in China. Further intervention trials are needed to test the efficacy and long-term outcomes of this intervention.

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