SiMuR research frame (starter-kit)
The research lines of this group revolve around three topics. The companion references can be taken as a starter-kit to work in the lab.
1. Robot motion planning
A fundamental robotics task is to plan collision-free motions for complex bodies from a start to a goal position among a collection of obstacles .
2. Human motion capture
Motion tracking technology has become increasingly miniaturized and more available . Optical motion capture (oMC) operation is based on stereophotogrammetry where the three-dimensional coordinates of points (markers) on an object are produced in two or more photographic images taken from different positions . Wearable motion capture (wMC) relies on acceleration and rotational velocity measurements from miniature wearable triaxial accelerometers, gyroscopes and IMUs .
3. Human-robot collaboration
Human–robot collaboration contributes to the achievement of higher productivity and greater efficiency, by breaking with the established safety procedures as the separation of workspaces between robot and human is removed . Motion tracking is a vital component of developing collaborative workspaces: robots must be able to perceive human motion in order to interact, co-operate, or imitate. In industry, regulations that incorporate these robot related risks for workers include the international standard ISO 10218 and the Technical Specification ISO/TS 15066:2016 , the American ANSI/RIA R15.06 and the European EN 775. For robot navigation among people, the presence of humans requires novel approaches that take into account the constraints of human comfort as well as social rules .
 M. Planning, L. E. Kavraki, and S. M. Lavalle, “Robot Motion Planning,” in Springer Handbook of Robotics, pp. 139–161. M. Field, D. Stirling, F. Naghdy, and Z. Pan, “Motion capture in robotics review,” 2009 IEEE Int. Conf. Control Autom. ICCA 2009, no. May 2016, pp. 1697–1702, 2009. G. Nagymáté and R. M. Kiss, “Application of OptiTrack motion capture systems in human movement analysis A systematic literature review,” Recent Innov. Mechatronics, vol. 5, no. 1, pp. 1–9, 2018. M. Kok, J. D. Hol, and T. B. Schön, “Using Inertial Sensors for Position and Orientation Estimation,” 2017. P. A. Lasota, T. Fong, and J. A. Shah, “A Survey of Methods for Safe Human-Robot Interaction,” Found. Trends® Robot., vol. 5, no. 4, pp. 261–349, 2017. Robotiq, “ISO/TS 15066 Explained,” 2016. T. Kruse et al., “Human-Aware Robot Navigation : A Survey,” vol. 61, no. 12, pp. 1726–1743, 2018.
Ambulatory measurement of human motion: model-based design of wearable multisensor systems (optimized arrangements of sensors) let us to improve accuracy, robustness, fault-tolerance and auto-configuration properties.
Last publications: J11, J09
Motion prediction and anticipation: prediction is a step forward from just motion measurement, which allow us to solve problems that demand a tight action coupling between humans and machines, e.g. working in robots close proximity.
Last publications: J12
Human-Robot motion coordination: the ambulatory measurement of human behavior in real-time opens different ways for designing new methods for harmonious human-centered robot motion, from gross motor movement to planning.
Last publications: J07, P13