Abstract

Abstract
In recent years, scientific and technological advances in robotics, have enabled the development of disruptive solutions for human interaction with the real world. In particular, the application of robotics to support people with physical disabilities, improved their life quality with a high social impact. This paper presents a stereo image based perception solution for autonomous wheelchairs navigation. It was developed to extend the Intellwheels project, a development platform for intelligent wheelchairs. The current version of Intellwheels relies on a planar scanning sensor, the Laser Range Finder (LRF), to detect the surrounding obstacles. The need for robust navigation capabilities means that the robot is required to precept not only obstacles but also bumps and holes on the ground. The proposed stereo-based solution, supported in passive stereo ZED cameras, was evaluated in a 3D simulated world scenario designed with a challenging floor. The performance of the wheelchair navigation with three different configurations was compared: first, using a LRF sensor, next with an unfiltered stereo camera and finally, applying a stereo camera with a speckle filter. The LRF solution was unable to complete the planned navigation. The unfiltered stereo camera completed the challenge with a low navigation quality due to noise. The filtered stereo camera reached the target position with a nearly optimal path.

Abstract
The independence and autonomy of both elderly and disabled people have been a growing concern of today's society. Consequently, the increase in life expectancy combined with the ageing of the population has created the ideal conditions for the introduction of Intelligent Wheelchairs (IWs). For this purpose, several adapted sensors should be used to optimize the control of a wheelchair. During this work, the Leap Motion sensor was analyzed to convert the user's will into one of four fundamental driving commands, move forward, turn right, left, or stop. Leap Motion aims to determine the direction to follow according to the hand gesture identified. For this task, data was collected from volunteers while they were performing certain gestures. Thereby it was possible to produce a data set that after being processed and extracted some features enabled the classification of the data with an F1-Score higher than 0.97. Additionally, when tested in a real-time application, this sensor reinforced its high performance.

Abstract
This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking.

Abstract
The introduction of Cyber-Physical Systems (CPS) in the industry through the digitalization of equipment, also known as Digital Twins, allows for a more customized production. Due to high market fluctuation, the implementation of a CPS should guarantee a high flexibility in both hardware and software levels to achieve a high responsiveness of the system. The software reconfiguration, specifically, introduces a question: “With heterogeneous equipment with different capabilities - namely processing and memory capabilities - where a certain software module should execute?”; that question fits on the task/resource allocation area applied to CPS software reconfiguration. Although in task allocation issue several approaches address such a problem, only a few of them focus on CPS resources optimization. Given that, an approach based on the Dutch Auction algorithm is proposed, implemented at the CPS level enables the software reconfiguration of the CPS according to the existing equipment resources. This approach, besides the optimization of the CPS resources and the energy consumption, transforms the CPS in more reliable and fault-tolerant systems. As shown by the results, despite the demonstration of its suitability in task/resource allocation problems in decentralized architectures, the proposed approach also as a major advantage of quickly finding a near-optimal solution. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.