Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework

In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time computation of the minimum distance between the manipulator and other objects in the environment using depth measurements has been implemented, effectively allowing obstacle avoidance tasks. Experiments with a Jaco$^2$ manipulator, operating in an environment where an RGB-D sensor is used for the obstacles detection, show the effectiveness of the developed system

A Data-Driven Approach for Contact Detection, Classification and Reaction in Physical Human-Robot Collaboration

This paper considers a scenario where a robot and a human operator share the same workspace, and the robot is able to both carry out autonomous tasks and physically interact with the human in order to achieve common goals. In this context, both intentional and accidental contacts between human and robot might occur due to the complexity of tasks and environment, to the uncertainty of human behavior, and to the typical lack of awareness of each other actions. Here, a two stage strategy based on Recurrent Neural Networks (RNNs) is designed to detect intentional and accidental contacts: the occurrence of a contact with the human is detected at the first stage, while the classification between intentional and accidental is performed at the second stage. An admittance control strategy or an evasive action is then performed by the robot, respectively. The approach also works in the case the robot simultaneously interacts with the human and the environment, where the interaction wrench of the latter is modeled via Gaussian Mixture Models (GMMs). Control Barrier Functions (CBFs) are included, at the control level, to guarantee the satisfaction of robot and task constraints while performing the proper interaction strategy. The approach has been validated on a real setup composed of a Kinova Jaco2 robot.Comment: Accepted to 2021 IEEE International Conference on Robotics and Automatio

ISME trends: Autonomous surface and underwater vehicles for geoseismic survey

The paper presents the recent and ongoing activities of the Italian Center named ISME on the use of Autonomous Surface Crafts (ASCs) and Autonomous Underwater Vehicles (AUVs) for geoseismic survey. In particular, the paper will focus on the technologies and the algorithms developed in the framework of the H2020 European Project WiMUST

Adaptive trajectory tracking for quadrotor MAVs in presence of parameter uncertainties and external disturbances

International audienceThe paper presents an adaptive trajectory track- ing control strategy for quadrotor Micro Aerial Vehicles. The proposed approach, while keeping the typical assumption of an orientation dynamics faster than the translational one, removes that of absence of external disturbances and of perfect symmetry of the vehicle. In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments, and to the uncertainty of dynamic parameters as the position of the center of mass of the vehicle. A stability analysis as well as numerical simulations are provided to support the control design

ISME activity on the use of autonomous surface and underwater vehicles for acoustic surveys at sea

The paper presents an overview of the recent and ongoing research activities of the Italian Interuniversity Center on Integrated Systems for the Marine Environment (ISME) in the field of geotechnical seismic surveying. Such activities, performed in the framework of the H2020 European project WiMUST, include the development of technologies and algorithms for Autonomous Surface Crafts and Autonomous Underwater Vehicles to perform geotechnical seismic surveying by means of a team of robots towing streamers equipped with acoustic sensors

Children’s and families’ determinants of health-related behaviors in an italian primary school sample: The “seven days for my health” project

Childhood obesity is an established health problem, and there is a growing need for health promotion interventions focused on healthy behaviors in collaboration with parents and schools. The Mediterranean diet (MD) could help to tackle obesity, but it is essential to maintain a good level of physical activity (PA) and limit time spent in sedentary activities (ST). To explore family determinants, adherence to the MD and PA levels as potential predictors of a child’s health-related behaviors, we performed a cross-sectional analysis of 368 Italian primary school children with a mean age of 8.95 years (SD = 1.43). Data were collected from May to June 2017 using a weekly diary, an interactive tool to assess the child’s and parents’ lifestyle. The child’s degree of adherence to the MD was calculated using the KIDMED index. Adherence to the MD was high, medium and poor in 5.2%, 62.5% and 32.3% of children, respectively. Higher maternal educational level was positively associated with children’s MD and PA (p < 0.05) and negatively correlated to ST. Maternal fruit and vegetable consumption was positively related to the MD and negatively related to ST (p < 0.05). Maternal PA was positively associated with the MD (p < 0.001). Paternal PA, and fruit and vegetable consumption, were positively associated with children’s PA (p < 0.05). Our results underline the need for future studies, mainly focused on school-based and family-based interventions, to promote healthy lifestyles and nutritional habits