A Multi-Sensor System for Enhancing Situational Awareness and Stress Management for People with ASD in the Workplace and in Everyday Life

Autism spectrum disorders (ASD) present challenges for affected people at work and in everyday life. The barrier increases further with changing environmental situations. Deviations in factors like lighting or sound may lead to increased stress. The intervention plans to instil positive behaviour support (PBS) suggest that a customised environment can minimise the impacts due to these variations. This work proposes a novel framework which leverages the information from multi-sensor channels in a combined manner to customise the environment so that situational awareness (SA) can be improved. The proposed framework allows for monitoring the environment by combining the information from different sensor channels including both personal sensors (i.e. on board of a mobile device) as well as environmental sensors/actuators (i.e. embedded in smart-buildings). In this preliminary work, the system architecture is introduced. To demonstrate the potential of the proposed system, a case study is also considered through the development of a prototype for a mobile application and by reporting results on a scale model of a smart workplace with customisable environment

ALTERNATIVE AND FLEXIBLE CONTROL METHODS FOR ROBOTIC MANIPULATORS: On the challenge of developing a flexible control architecture that allows for controlling different manipulators

Robotic arms and cranes show some similarities in the way they operate and in the way they are designed. Both have a number of links serially attached to each other by means of joints that can be moved by some type of actuator. In both systems, the end-effector of the manipulator can be moved in space and be placed in any desired location within the system’s workspace and can carry a certain amount of load. However, traditional cranes are usually relatively big, stiff and heavy because they normally need to move heavy loads at low speeds, while industrial robots are ordinarily smaller, they usually move small masses and operate at relatively higher velocities. This is the reason why cranes are commonly actuated by hydraulic valves, while robotic arms are driven by servo motors, pneumatic or servo-pneumatic actuators. Most importantly, the fundamental difference between the two kinds of systems is that cranes are usually controlled by a human operator, joint by joint, using simple joysticks where each axis operates only one specific actuator, while robotic arms are commonly controlled by a central controller that controls and coordinates the actuators according to some specific algorithm. In other words, the controller of a crane is usually a human while the controller of a robotic arm is normally a computer program that is able to determine the joint values that provide a desired position or velocity for the end-effector. If we especially consider maritime cranes, compared with robotic arms, they rely on a much more complex model of the environment with which they interact. These kinds of cranes are in fact widely used to handle and transfer objects from large container ships to smaller lighters or to the quays of the harbours. Therefore, their control is always a challenging task, which involves many problems such as load sway, positioning accuracy, wave motion compensation and collision avoidance. Some of the similarities between robotic arms and cranes can also be extended to robotic hands. Indeed, from a kinematic point of view, a robotic hand consists of one or more kinematic chains fixed on a base. However, robotic hands usually present a higher number of degrees of freedom (DOFs) and consequentially a higher dexterity compared to robotic arms. Nevertheless, several commonalities can be found from a design and control point of views. Particularly, modular robotic hands are studied in this thesis from a design and control point of view. Emphasising these similarities, the general term of robotic manipulator is thereby used to refer to robotic arms, cranes and hands. In this work, efficient design methods for robotic manipulators are initially investigated. Successively, the possibility of developing a flexible control architecture that allows for controlling different manipulators by using a universal input device is outlined. The main challenge of doing this consists of finding a flexible way to map the normally fixed DOFs of the input controller to the variable DOFs of the specific manipulator to be controlled. This process has to be realised regardless of the differences in size, kinematic structure, body morphology, constraints and affordances. Different alternative control algorithms are investigated including effective approaches that do not assume a priori knowledge for the Inverse Kinematic (IK) models. These algorithms derive the kinematic properties from biologically-inspired approaches, machine learning procedures or optimisation methods. In this way, the system is able to automatically learn the kinematic properties of different manipulators. Finally, a methodology for performing experimental activities in the area of maritime cranes and robotic arm control is outlined. By combining the rapid-prototyping approach with the concept of interchangeable interfaces, a simulation and benchmarking framework for advanced control methods of maritime cranes and robotic arms is presented. From a control point of view, the advantages of releasing such a flexible control system rely on the possibility of controlling different manipulators by using the same framework and on the opportunity of testing different control approaches. Moreover, from a design point of view, rapidprototyping methods can be applied to fast develop new manipulators and to analyse different properties before making a physical prototype

The Aquatic Surface Robot (AnSweR), a lightweight, low cost, multipurpose unmanned research vessel

Author's accepted version (postprint).This is an Accepted Manuscript of an article published by Springer in Communications in Computer and Information Science on 15/03/2021.Available online: https://link.springer.com/chapter/10.1007/978-3-030-71711-7_21acceptedVersio

uMemristorToolbox : Open source framework to control memristors in Unity for ternary applications

Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).publishedVersio

A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

publishedVersio

A comparison between a two-feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison

Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction

Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype.publishedVersio