A reliable and fast sensing of the environment is a fundamental necessity of mobile platforms. Unfortunately conventional cameras due to the current frame-based acquisition paradigm output low temporal dynamics and redundant data flow leading to high computational costs. It is obviously incompatible with the necessities of mobile platforms where energy consumption and computational load are a major issue. The restrictions of the frame-based paradigm are contradictory with applications requiring high speed sensor-based reactive control. This paper introduces a fast obstacle avoidance using the output of an asynchronous event-based time encoded imaging sensor. The approach is event-based in the sense that every incoming event adds to the computation process thus allowing fast avoidance responses. It introduces an event-based time-to-contact approach relying on the computation of visual event-based motion flows. Experiments on a mobile robot are presented in an indoor environment. Time to contact results are compared with those provided by a laser range finder showing that event-based sensing offers new perspectives for mobile robotics sensing.

This paper presents some recent developments in YARP middleware, aimed to improve its integration with ROS. They include a new mechanism to read/write ROS transform frames and a new set of standard interfaces to intercommunicate with the ROS navigation stack. A novel set of YARP companion modules, which provide basic navigation functionalities for robots unable to run ROS, is also presented. These modules are optional, independent from each other, and they provide compatible functionalities to well-known packages available inside ROS framework. This paper also discusses how developers can customize their own hybrid YARP-ROS environment in the way it best suits their needs (e.g., the system can be configured to have a YARP application sending navigation commands to a ROS path planner, or vice versa). A number of available possibilities is presented through a set of chosen test cases applied to both real and simulated robots. Finally, example applications discussed in this paper are also made available to the community by providing snippets of code and links to source files hosted on github repository https://github.com/robotology.¹

Conventional approaches to robot navigation in unstructured environments rely on information acquired from the LiDAR mounted on the robot base to detect and avoid obstacles. This approach fails to detect obstacles that are too small, or that are invisible because they are outside the LiDAR’s field of view. A possible strategy is to integrate information from other sensors. In this paper, we explore the possibility of using depth information from a movable RGB-D camera mounted on the head of the robot, and investigate, in particular, active control strategies to effectively scan the environment. Existing works combine RGBD-D and 2D LiDAR data passively by fusing the current point-cloud from the RGB-D camera with the occupancy grid computed from the 2D LiDAR data, while the robot follows a given path. In contrast, we propose an optimization strategy that actively changes the position of the robot’s head, where the camera is mounted, at each point of the given navigation path; thus, we can fully exploit the RGB-D camera to detect, and hence avoid, obstacles undetected by the 2D LiDAR, such as overhanging obstacles or obstacles in blind spots. We validate our approach in both simulation environments to gather statistically significant data and real environments to show the applicability of our method to real robots. The platform used is the humanoid robot R1.

In standard clinical protocols, the result of neuromotor rehabilitation programs is evaluated through validated clinical scales and tests able to measure the motor performance of the patients and monitor their improvements over time. The Timed Up and Go (TUG) test is one of the most common assessments used to evaluate patients’ dynamic balance, as well as their mobility. However, in its traditional version, the TUG test does not provide quantitative information on gait performances—only subjectively observed by the clinician—and does not measure the timing of the different phases involved in its execution. The availability of additional quantitative information would indeed be useful for the clinicians to formulate a more accurate assessment of patient performance and define a personalized treatment plan. In this sense, the use of Socially Assistive Robots (SARs) could help improving the assessment of patient performance, relieving at the same time the physiotherapists from time consuming tasks. The goal of this feasibility study is twofold: (1) to assess the quality of the TUG functionality implemented on the robot from a technical standpoint and (2) to evaluate the perception of the “R1-TUG” solution from its potential end-users point of view, in terms of usability and acceptability. A set of tests involving a sample of healthy volunteers revealed that the adoption of a SAR as an assessment tool, could improve the ability of the physiotherapist to objectively assess the subject’s movement performance while ensuring an adequate level of usability and acceptability from the participants. This work represents a promising result for the future use of the robotic solutions within a rehabilitation context.