Abstract

Abstract

Abstract
With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human-robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human-robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers' published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human-robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution's target audience, and on establishing standards and guidelines for developing AR assistance systems.

Abstract

Abstract
This paper studies the state-of-the-art of active perception solutions for manipulation in agriculture and suggests a possible architecture for an active perception system for harvesting in agriculture. Research and developing robots for agricultural context is a challenge, particularly for harvesting and pruning context applications. These applications normally consider mobile manipulators and their cognitive part has many challenges. Active perception systems look reasonable approach for fruit assessment robustly and economically. This systematic literature review focus in the topic of active perception for fruits harvesting robots. The search was performed in five different databases. The search resumed into 1034 publications from which only 195 publications where considered for inclusion in this review after analysis. We conclude that the most of researches are mainly about fruit detection and segmentation in two-dimensional space using evenly classic computer vision strategies and deep learning models. For harvesting, multiple viewpoint and visual servoing are the most commonly used strategies. The research of these last topics does not look robust yet, and require further analysis and improvements for better results on fruit harvesting.