Arthropod animals like scorpions with modular body parts can be an inspiration for a robot’s structure. The design presented here relays on inter-connected origami towers, but could also be easily disassembled. Each origami tower is fully autonomous and at the same time is part of the robot as a whole. The towers are positioned between two platforms that enable modularity. The scorpion’s tale shape is achieved by the varying platform diameter resulting in cone-like form. Each tower is actuated independently to enable multiple degrees of freedom. Maneuvering with separated units, assists in easier reparation as well as replacement. Detaching the towers into separate parts makes this structure develop more precise movements, since every unit will move autonomously. Therefore, having a higher number of separated movements combined leads to a smooth bionic movement. So, the overall hierarchy will be modular contributing to a greater curvature bending of the whole structure. Actuating and maneuvering the robot in the main concept is done by separated electro motors, built in the platform. The basic structure will be built from thick paper with plastic coatings. The thick paper itself is lightweight, but at the same time flexible. To protect the paper towers, double plastic foil is placed as an outer coating which acts as an origami cover. This transparent layer is elastic hence it can follow and support the individual units’ movements. This work is focused on understanding origami towers kinematics and different combinations of inter-connected towers to achieve multiple degrees of freedom. A conceptual model is developed, supported by CAD and mathematical models. At the end a prototype is presented.

Many real-world tasks require multiple agents to work together. When talking about multiple agents in robotics, it is usually referenced to multiple manipulators in collaboration to solve a given task, where each one is controlled by a single agent. However, due to the increasing development of modular and re-configurable robots, it is also important to investigate the possibility of implementing multi-agent controllers that learn how to manage the manipulator’s degrees of freedom (DoF) in separated clusters for the execution of a given application (e.g., being able to face faults or, partially, new kinematics configurations). Within this context, this paper focuses on the decentralization of the robot control action learning and (re)execution considering a generic multi-DoF manipulator. Indeed, the proposed framework employs a multi-agent paradigm and investigates how such a framework impacts the control action learning process. Multiple variations of the multi-agent framework have been proposed and tested in this research, comparing the achieved performance w.r.t. a centralized (i.e., single-agent) control action learning framework, previously proposed by some of the authors. As a case study, a manipulation task (i.e., grasping and lifting) of an unknown object (to the robot controller) has been considered for validation, employing a Franka EMIKA panda robot. The MuJoCo environment has been employed to implement and test the proposed multi-agent framework. The achieved results show that the proposed decentralized approach is capable of accelerating the learning process at the beginning with respect to the single-agent framework while also reducing the computational effort. In fact, when decentralizing the controller, it is shown that the number of variables involved in the action space can be efficiently separated into several groups and several agents. This simplifies the original complex problem into multiple ones, efficiently improving the task learning process.

The limitations of passive noise control methods impose a need for new technical solutions to solve the problem of reducing low-frequency noise, which is considered to be a dominant component of noise disturbance. In recent years, the subject of intensive research are the active noise control systems, which have aroused considerable interest and represent a promising solution to the problem of low-frequency noise control. This paper proposes a robust methodology for simplified design and analysis of an experimental active noise control system for real-time control of acoustic environment in a duct. The proposed feedback control model is based on using the LMS algorithm, combined with FxLMS algorithm for estimation and neutralization of the secondary path in the electro-acoustic system. The study shows the potential of the FPGA module and the Real-time module of cRIO from National Instruments, combined with the LabView software environment when applied in adaptive system for active noise control. The reliability and validity of the developed active noise control system is tested for a frequency range of 100 to 1000 [Hz], by measuring the amplitude-time domain in [V] and sound level in [dB]. The comparison of the experimental results shows great efficiency of the system at lower frequency range from 200 to 400 [Hz], where a maximum reduction in sound level achieved at a frequency of 200 [Hz] is 14 [dB] or 17 [%]. A significant sound level reduction is also achieved at both 300 [Hz] and 400 [Hz] which is 12 % or 10 [dB] in both cases. Given the analysis of the challenges and opportunities of the developed active noise control system, recommendations for advancements and future work are proposed.

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The advances of artificial intelligence approaches for automatically extracting and classifying disturbing sounds have great potential and application in the development of smart cities. In this paper, an urban sound event classification system based on deep learning technologies has been created by using the MEL-frequency cepstral coefficients as feature extractors and the Convolutional Neural Networks as classifiers. The designed system was trained and tested using the UrbanSound8K dataset which resulted in high classification accuracy of 92.67% of the tested results. In addition to this, validation of unknown sound events was applied. Furthermore, we propose the creation of a stand-alone system capable of recording and classifying sounds within an urban environment and sending the classification data to the cloud. The implementation of such units could result in real-time sound classification which can be used in smart cities based on the Internet of Things technology. According to this, an IoT smart city framework using AI for urban sound classification was proposed. The created system could find its use in many applications by making contribution in creating a feasible and deployable real-time sound classification system.