How Socio-Technical Factors Can Undermine Expectations of Human-Robot Cooperation in Hospitals

This research analysed human–robot cooperation and interaction in the basement of a Danish hospital, where kitchen staff and porters conducted their daily routines in an environment shared with mobile service robots. The robots were installed to ease the everyday routines of kitchen staff and carry out physically demanding tasks, such as transporting heavy cargo between destinations in the hospital basement. The cooperation and interaction were studied through ethnographic inspired fieldwork and the results highlighted how robots affect the real-life environments into which they are gradually moving. The analysis revealed how the great human expectations of robots clashed with reality and identified three key elements that influence human–robot cooperation in hospitals: 1) environmental factors, 2) behavioural factors and 3) factors related to human reliance on robots. We emphasise the importance of considering socio-technical factors when deploying robots to cooperate with humans in hospital environments.

Intelligent support of logistics missions in medical institutions by mobile service robots AMUR-307

Technologies of missions’ generation for the mobile robots (MR) Amur-307 by the parallelized automatic scheduler and their implementation by the integration software (SW) with the hardware block (HB) on 6 power channels (6x30A), with the branched sensorics are considered. The program generator is forming the tasks in the PDDL language based on the route map in vector graphic format, parameters of objects moved by robots, and equipment installed on robots. The developed plan is implemented by the HB and the navigation control program, based on topological information extracted from the map, based on passive oriented visual markers installed in the walls. The robot can work both in real and in virtual environments, using the Webots emulator and/or relying on its own original developments.

Dynamic categorization of 3D objects for mobile service robots

Purpose This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them. Design/methodology/approach The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed. Findings The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general. Research limitations/implications The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation. Practical implications The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks. Originality/value The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.