Implementation of Static RFID Landmarks in SLAM for Planogram Compliance

Autonomous robotic systems are becoming increasingly prevalent in everyday life and exhibit robust solutions in a wide range of applications. They face many obstacles with the foremost of which being SLAM, or Simultaneous Localization and Mapping, that encompasses both creation of the map of an unknown environment and localization of the robot in said environment. In this experiment, researchers propose the use of RFID tags in a semi-dynamic commercial environment to provide concrete landmarks for localization and mapping in pursuit of increased locational certainty. With this obtained, the ultimate goal of the research is to construct a robotics platform for planogram compliance and inventory management to provide consistency between online retail platforms and brick and mortar stores. The platform of choice is the Turtlebot3 Burger platform, by ROBOTIS, modified to hold an RFID reader. With existing packages, researchers are provided with the ability to essentially perform SLAM on a base level using an inbuilt Lidar sensor. It is from these existing packages that researchers plan to build a system to localize RFID tags in generated maps to provide a quantifiable decrease in localization time and increase in certainty

Particle Observers for Contracting Dynamical Systems

In the present paper we consider a class of partially observed dynamical systems. As in the Rao-Blackwellized particle filter (RBPF) paradigm (see e.g., Doucet et al. 2000), we assume the state x can be broken into two sets of variables x=(z,r) and has the property that conditionally on z the system’s dynamics possess geometrical contraction properties, or is amenable to such a system by using a nonlinear observer whose dynamics possess contraction properties. Inspired by the RBPF we propose to use particles to approximate the r variable and to use a simple copy of the dynamics (or an observer) to estimate the rest of the state. This has the benefits of 1- reducing the computational burden (a particle filter would sample the variable x also), which is akin to the interest of the RBPF, 2- coming with some indication of stability stemming from contraction (actual proofs of stability seem difficult), and 3- the obtained filter is well suited to systems where the dynamics of x conditionally on z is precisely known and the dynamics governing the evolution of z is quite uncertain

Spatial context-aware person-following for a domestic robot

Domestic robots are in the focus of research in terms of service providers in households and even as robotic companion that share the living space with humans. A major capability of mobile domestic robots that is joint exploration of space. One challenge to deal with this task is how could we let the robots move in space in reasonable, socially acceptable ways so that it will support interaction and communication as a part of the joint exploration. As a step towards this challenge, we have developed a context-aware following behav- ior considering these social aspects and applied these together with a multi-modal person-tracking method to switch between three basic following approaches, namely direction-following, path-following and parallel-following. These are derived from the observation of human-human following schemes and are activated depending on the current spatial context (e.g. free space) and the relative position of the interacting human. A combination of the elementary behaviors is performed in real time with our mobile robot in different environments. First experimental results are provided to demonstrate the practicability of the proposed approach