Mobile Formation Coordination and Tracking Control for Multiple Non-holonomic Vehicles

This paper addresses forward motion control for trajectory tracking and mobile formation coordination for a group of non-holonomic vehicles on SE(2). Firstly, by constructing an intermediate attitude variable which involves vehicles' position information and desired attitude, the translational and rotational control inputs are designed in two stages to solve the trajectory tracking problem. Secondly, the coordination relationships of relative positions and headings are explored thoroughly for a group of non-holonomic vehicles to maintain a mobile formation with rigid body motion constraints. We prove that, except for the cases of parallel formation and translational straight line formation, a mobile formation with strict rigid-body motion can be achieved if and only if the ratios of linear speed to angular speed for each individual vehicle are constants. Motion properties for mobile formation with weak rigid-body motion are also demonstrated. Thereafter, based on the proposed trajectory tracking approach, a distributed mobile formation control law is designed under a directed tree graph. The performance of the proposed controllers is validated by both numerical simulations and experiments

Can robots possess knowledge? : Rethinking the DIK(W) pyramid through the lens of employees of an automotive factory

Knowledge, information, and data are increasingly processed in human–robot collaboration. This study tackles two requirements for revising the concepts of knowledge, information, and data. First is developing robots’ knowledge capabilities and transparency and ensuring effective division of tasks between humans and robots to increase the productivity of robotised factories. Employees’ interpretations of robots’ abilities to possess knowledge reveal their assumptions of robots’ possibilities and limitations to create knowledge-based products with humans. Second, the classic DIK(W) pyramid of data, information, knowledge, and wisdom is a theoretical construct requiring additional empirical research. This empirical exploratory study develops the DIK(W) further and applies it as a tool to understand employees’ perspectives of robots and knowledge. Do people believe robots possess knowledge? What kind of knowledge can (or cannot) robots possess? A survey (n = 269) was collected from the most robotised factory in Finland, Valmet Automotive. Half of the respondents think robots can possess knowledge, but only with humans. These respondents were more likely to trust robots compared to those who think robots cannot possess knowledge. As the key contribution, the DIK(W) pyramid is reconceived by (i) acknowledging robots and humans, (ii) turning the pyramid upside down, and (iii) recognising knowledge as a dividing concept.© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visithttp://creativecommons.org/licenses/by/4.0/.I thank Olli Nevalainen for collecting the survey. The research was funded by the Academy of Finland, project nr. 319872 Second Machine Age Knowledge Co-Creation Processes in Space and Time.fi=vertaisarvioitu|en=peerReviewed

Co-creating Knowledge with Robots: System, Synthesis, and Symbiosis

In the contemporary robotizing knowledge economy, robots take increasing responsibility for accomplishing knowledge-related tasks that so far have been in the human domain. This profoundly changes the knowledge-creation processes that are at the core of the knowledge economy. Knowledge creation is an interactive spatial process through which ideas are transformed into new and justified outcomes, such as novel knowledge and innovations. However, knowledge-creation processes have rarely been studied in the context of human–robot co-creation. In this article, we take the perspective of key actors who create the future of robotics, namely, robotics-related students and researchers. Their thoughts and actions construct the knowledge co-creation processes that emerge between humans and robots. We ask whether robots can have and create knowledge, what kind of knowledge, and what kind of spatialities connect to interactive human–robot knowledge-creation processes. The article’s empirical material consists of interviews with 34 robotics-related researchers and students at universities in Finland and Singapore as well as observations of human–robot interactions there. Robots and humans form top-down systems, interactive syntheses, and integrated symbioses in spatial knowledge co-creation processes. Most interviewees considered that robots can have knowledge. Some perceived robots as machines and passive agents with rational knowledge created in hierarchical systems. Others saw robots as active actors and learning co-workers having constructionist knowledge created in syntheses. Symbioses integrated humans and robots and allowed robots and human–robot cyborgs access to embodied knowledge.© The Author(s) 2022. Published by Springer. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed