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Robotics, Volume 14, Issue 4 (April 2025) – 3 articles

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4 pages, 141 KiB  
Editorial
Special Issue on Social Robots for Human Well-Being
by Martin Cooney and Mariacarla Staffa
Robotics 2025, 14(4), 37; https://doi.org/10.3390/robotics14040037 - 24 Mar 2025
Viewed by 105
Abstract
Social robots are rapidly emerging as a transformative technology aimed at enhancing human well-being [...] Full article
(This article belongs to the Special Issue Social Robots for the Human Well-Being)
26 pages, 10785 KiB  
Article
Real Time MEMS-Based Joint Friction Identification for Enhanced Dynamic Performance in Robotic Applications
by Paolo Righettini, Giovanni Legnani, Filippo Cortinovis, Federico Tabaldi and Jasmine Santinelli
Robotics 2025, 14(4), 36; https://doi.org/10.3390/robotics14040036 - 21 Mar 2025
Viewed by 132
Abstract
The mechatronic design approach to robotics deploys, inter alia, widely available mechanical design engineering tools that, together with standard production techniques, allow the accurate quantification of the system’s mass properties. While this enables the synthesis of model-based centralized controllers, friction still limits the [...] Read more.
The mechatronic design approach to robotics deploys, inter alia, widely available mechanical design engineering tools that, together with standard production techniques, allow the accurate quantification of the system’s mass properties. While this enables the synthesis of model-based centralized controllers, friction still limits the achievable dynamic performances, as its prediction at the design stage is hampered by complex dependencies on loads, temperature, wear, and lubrication. Further uncertainties affecting mechatronic devices stem from the actuation systems, whose parameters are specified by the manufacturer with relatively loose accuracy. These challenges are addressed here through a method based on MEMS IMUs for the real-time estimation of both friction effects and uncertain actuator parameters. The resulting model, inclusive of the frictionless dynamics, is applied in a closed loop to improve the control performance. An experimental comparison with decentralized and non-adaptive regulators highlights severalfold reductions in tracking errors; the ability to track temperature-dependent friction variations is also shown. From this work, it may be concluded that the use of MEMS sensors, together with identification and adaptive control algorithms, sensibly increases the dynamic performance of robotic systems. The real-time properties of the method also enable future investigations into topics such as MEMS-based diagnostics and predictive maintenance. Full article
(This article belongs to the Section Intelligent Robots and Mechatronics)
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16 pages, 13516 KiB  
Article
DUnE: A Versatile Dynamic Unstructured Environment for Off-Road Navigation
by Jack M. Vice and Gita Sukthankar
Robotics 2025, 14(4), 35; https://doi.org/10.3390/robotics14040035 - 21 Mar 2025
Viewed by 192
Abstract
Navigating uneven, unstructured terrain with dynamic obstacles remains a challenge for autonomous mobile robots. This article introduces Dynamic Unstructured Environment (DUnE) for evaluating the performance of off-road navigation systems in simulation. DUnE is a versatile software framework that implements the [...] Read more.
Navigating uneven, unstructured terrain with dynamic obstacles remains a challenge for autonomous mobile robots. This article introduces Dynamic Unstructured Environment (DUnE) for evaluating the performance of off-road navigation systems in simulation. DUnE is a versatile software framework that implements the Gymnasium reinforcement learning (RL) interface for ROS 2, incorporating unstructured Gazebo simulation environments and dynamic obstacle integration to advance off-road navigation research. The testbed automates key performance metric logging and provides semi-automated trajectory generation for dynamic obstacles including simulated human actors. It supports multiple robot platforms and five distinct unstructured environments, ranging from forests to rocky terrains. A baseline reinforcement learning agent demonstrates the framework’s effectiveness by performing pointgoal navigation with obstacle avoidance across various terrains. By providing an RL interface, dynamic obstacle integration, specialized navigation tasks, and comprehensive metric tracking, DUnE addresses significant gaps in existing simulation tools. Full article
(This article belongs to the Section AI in Robotics)
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