Current robotics reports最新文献

Purpose of review: To discuss the current state of reproducibility of research in human-robot interaction (HRI), challenges specific to the field, and recommendations for how the community can support reproducibility.

Recent findings: As in related fields such as artificial intelligence, robotics, and psychology, improving research reproducibility is key to the maturation of the body of scientific knowledge in the field of HRI. The ACM/IEEE International Conference on Human-Robot Interaction introduced a theme on Reproducibility of HRI to their technical program in 2020 to solicit papers presenting reproductions of prior research or artifacts supporting research reproducibility.

Summary: This review provides an introduction to the topic of research reproducibility for HRI and describes the state of the art in relation to the HRI 2020 Reproducibility theme. As a highly interdisciplinary field that involves work with technological artifacts, there are unique challenges to reproducibility in HRI. Biases in research evaluation and practice contribute to challenges in supporting reproducibility, and the training of researchers could be changed to encourage research reproduction. The authors propose a number of solutions for addressing these challenges that can serve as guidelines for the HRI community and related fields.

Purpose of review: Robots are increasingly being adopted in healthcare to carry out various tasks that enhance patient care. This scoping review aims to establish the types of robots being used in healthcare and identify where they are deployed.

Recent findings: Technological advancements have enabled robots to conduct increasingly varied and complex roles in healthcare. For instance, precision tasks such as improving dexterity following stroke or assisting with percutaneous coronary intervention.

Summary: This review found that robots have played 10 main roles across a variety of clinical environments. The two predominant roles were surgical and rehabilitation and mobility. Although robots were mainly studied in the surgical theatre and rehabilitation unit, other settings ranged from the hospital ward to inpatient pharmacy. Healthcare needs are constantly evolving, as demonstrated by COVID-19, and robots may assist in adapting to these changes. The future will involve increased telepresence and infrastructure systems will have to improve to allow for this.

Supplementary information: The online version contains supplementary material available at 10.1007/s43154-022-00095-4.

Purpose of review: The goal of this review is to provide an overview of current robotic approaches to precision weed management. This includes an investigation into applications within this field during the past 5 years, identifying which major technical areas currently preclude more widespread use, and which key topics will drive future development and utilisation.

Recent findings: Studies combining computer vision with traditional machine learning and deep learning are driving progress in weed detection and robotic approaches to mechanical weeding. Integrating key technologies for perception, decision-making, and control, autonomous weeding robots are emerging quickly. These effectively save effort while reducing environmental pollution caused by pesticide use.

Summary: This review assesses different weed detection methods and weeder robots used in precision weed management and summarises the trends in this area in recent years. The limitations of current systems are discussed, and ideas for future research directions are proposed.

Purpose of review: This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems.

Recent findings: Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machine learning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the "sim-to-real" gap in this context.

Summary: We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.