The Industry 5.0 Human-Robot Interaction (HRI) goals have the potential for significantly reducing production costs, but require factory workers to trust that robots will not cause them physical harm when sharing the workspace in order to be achieved. To this end, we propose a model that adjusts the robot’s actions to an estimate of the workers’ awareness of its behavior. Its premise is that mutual awareness should be continuous, and if no recent visual contact has been established, then the robot should adapt its range of motion and speed in order to minimize the intersection between the robot’s space and the worker’s space. On the other hand, if the worker steadily establishes visual contact from times to times, the robot operates under a normal or a faster configuration. After a period of habituation, this awareness model can be further tweaked to continuously improve production times, while improving worker’s safety and trust. We present preliminary results of a pilot study with 32 participants, to asses if the existence of this awareness model impacts the outcomes of the combined task. The obtained tepidly optimistic results are the key to build upon in future designs.

International audience

The path planning method is an important element in the architecture of all mobile robots with certain degree of autonomy. An appropriate path planning method provides the robot the ability to move in a previously known environment while avoiding collisions with obstacles along the way. Additionally, this method should generate optimal trajectories from one point to another. In this work the optimal path is defined as the shortest one. Path planning is not an easy task, some algorithms and methods have been developed for solving this problem, however, its success is not guaranteed. On the other hand, Homotopy Path Planning Method (HPPM) is a new tool used to find collision-free paths, this takes the properties of the Homotopy Continuation Methods (HCM) to find a successful path. However, the path found by this method is not the optimal. In this work, a path planning method for a terrestrial mobile robot based on HPPM and Spherical Algorithm (SA) is presented. Furthermore, a new strategy able to obtain the optimal path is proposed. Finally, simulation results for several environment maps with hundreds of circular obstacles are shown.

3D perception has known impressive advances in the past 3 years; it corresponds to several technological improvements, plus many new development teams providing open sources. First of all, researchers in Robotics and 3D Perception have made profit of the Kinect sensor; some works were already devoted to 3D cameras, using more expensive Time-of-Flight optical devices. Another common way to acquire dense 3D data, is by scanning the environment by a laser range finder (LRF); as for example, the Hokuyo tilting LRF integrated on the PR2 robot by Willow Garage. To build a dense geometrical model of an indoor environment, several sensors could be selected in order to acquire 3D data. This paper aims at giving some insights on this selection, presenting some pros and cons for Kinect, Hokuyo and ToF optical sensors.

In this paper is described the current work towards the design of a shopping assistant robot system. This system will allow users to keep control of what they are buying; the robot will help the customer by handling its shopping list, carrying with all the products, and serving as a companion. In this work is also shown the acceptability studies for this kind of robot.

As a part of the final goal of introducing robots in the human environment, it also comes the problem of the close interaction between these two beings. Close cooperative activities illustrate well this problem and rises new challenges to solve.

Trajectories followed by the humans can be interpreted as an attitude gesture. Based on this interpretation an autonomous mobile robot can decide how to initiate interaction with a given human. In this work is presented a modular control system to analyze human walking trajectories in order to engage a robot on a Human-Robot interaction. When the robot detects a human with their vision system a visual tracking module begins to work over the Pan/Tilt/Zoom (PTZ) camera unit. Camera parameters configuration and global robot localization are then used by another module to filter and track human's legs over the laser range finder (LRF) data. Path followed by the human over the global reference frame is then processed by another module which determines the kind of attitude showed by the human. Based on the result the robot decides if an interaction is needed and who is expected to begin it. At this moment are used only three kinds of attitudes: confidence, curiosity and nervousness.

This paper describes how to integrate tracking functions on a mobile robot. Tracking is required during autonomous robot navigation in different situations: landmark tracking to guarantee real-time robot localization, target tracking for a sensor based motion or obstacle tracking to control an obstacle avoidance procedure. These objects - landmarks, targets, and obstacles - have different characteristics: this paper shows that a single tracking method could not overcome all the tracking tasks. Several methods must be integrated on the robot. A tracker controller is in charge to activate a specific method with respect to the object type and to a transition model used to recover the tracking failures. This method is validated using four trackers, based on: template differences, set of points, lines and snakes. Experimental results provided on numerous image sequences are presented.