Autistic traits modulate mimicry of social but not nonsocial rewards

Autism Spectrum Conditions (ASC) are associated with diminished responsiveness to social stimuli, and especially to social rewards such as smiles. Atypical responsiveness to social rewards, which reinforce socially appropriate behavior in children, can potentially lead to a cascade of deficits in social behavior. Individuals with ASC often show diminished spontaneous mimicry of social stimuli in a natural setting. In the general population, mimicry is modulated both by the reward value and the sociality of the stimulus (i.e., whether the stimulus is perceived to belong to a conspecific or an inanimate object). Since empathy and autistic traits are distributed continuously in the general population, this study aimed to test if and how these traits modulated automatic mimicry of rewarded social and nonsocial stimuli. High and low rewards were associated with human and robot hands using a conditioned learning paradigm. Thirty-six participants from the general population then completed a mimicry task involving performing a prespecified hand movement which was either compatible or incompatible with a hand movement presented to the participant. High autistic traits (measured using the Autism Spectrum Quotient, AQ) predicted lesser mimicry of high-reward than low-reward conditioned human hands, whereas trait empathy showed an opposite pattern of correlations. No such relations were observed for high-reward vs. low-reward conditioned robot hands. These results demonstrate how autistic traits and empathy modulate the effects of reward on mimicry of social compared to nonsocial stimuli. This evidence suggests a potential role for the reward system in underlying the atypical social behavior in individuals with ASC, who constitute the extreme end of the spectrum of autistic traits.

The cyber challenge: a robotics project to enthuse

Robotics is a key theme in many of the degrees offered in Systems Engineering. The topic has proved useful in attracting students to the University, and it also provides the basis of much practical and project work throughout the degrees. This paper focuses on one aspect, a Part 2 project in which students doing various degrees work together to develop a mobile robot which is controlled remotely to navigate an environment and perform specific tasks. In addition to providing practical experience of relevant academic topics, this project helps to contribute to key teaching and learning priorities including problem based learning, motivation and important employability skills.

Estimation of magnet separation for magnetic suspension applications

This thesis describes a form of non-contact measurement using two dimensional hall effect sensing to resolve the location of a moving magnet which is part of a ‘magnetic spring’ type suspension system. This work was inspired by the field of Space Robotics, which currently relies on solid link suspension techniques for rover stability. This thesis details the design, development and testing of a novel magnetic suspension system with a possible application in space and terrestrial based robotics, especially when the robot needs to traverse rough terrain. A number of algorithms were developed, to utilize experimental data from testing, that can approximate the separation between magnets in the suspension module through observation of the magnetic fields. Experimental hardware was also developed to demonstrate how two dimensional hall effect sensor arrays could provide accurate feedback, with respects to the magnetic suspension modules operation, so that future work can include the sensor array in a real-time control system to produce dynamic ride control for space robots. The research performed has proven that two dimensional hall effect sensing with respects to magnetic suspension is accurate, effective and suitable for future testing.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard CCD camera and a ToF camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time of light information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localization. Further development of these methods will make it possible to identify objects and their position in the real world, and to use this information to prevent possible collisions between the robot and such objects.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

Proposal of a floating mechanism based on magnetic levitation method toward EEG-driven robotic system

We discussed a floating mechanism based on quasi-magnetic levitation method that can be attached at the endpoint of a robot arm in order to construct a novel redundant robot arm for producing compliant motions. The floating mechanism can be composed of magnets and a constraint mechanism such that the repelling force of the magnets floats the endpoint part of the mechanism stable for the guided motions. The analytical and experimental results show that the proposed floating mechanism can produce stable floating motions with small inertia and viscosity. The results also show that the proposed mechanism can detect small force applied to the endpoint part because the friction force of the mechanism is very small.

A compact low-cost electronic hardware design for actuating soft robots

A low cost, compact embedded design approach for actuating soft robots is presented. The complete fabrication procedure and mode of operation was demonstrated, and the performance of the complete system was also demonstrated by building a microcontroller based hardware system which was used to actuate a soft robot for bending motion. The actuation system including the electronic circuit board and actuation components was embedded in a 3D-printed casing to ensure a compact approach for actuating soft robots. Results show the viability of the system in actuating and controlling siliconebased soft robots to achieve bending motions. Qualitative measurements of uniaxial tensile test, bending distance and pressure were obtained. This electronic design is easy to reproduce and integrate into any specified soft robotic device requiring pneumatic actuation.

Feasibility study on EEG driven robotic system to realize efficient stroke rehabilitation

We aim to develop an efficient robotic system for stroke rehabilitation, in which a robotic arm moves the hemiplegic upper limb when the patient tries to move it. In order to achieve this goal we have considered a method to detect the patient's intended motion using EEG (Electroencephalogram), and have designed a rehabilitation robot based on a Redundant Drive Method. In this paper, we propose an EEG driven rehabilitation robot system and present initial results evaluating the feasibility of the proposed system.

Simultaneous mobile sink allocation in home environments with applications in mobile consumer robotics

This paper presents a novel mobile sink area allocation scheme for consumer based mobile robotic devices with a proven application to robotic vacuum cleaners. In the home or office environment, rooms are physically separated by walls and an automated robotic cleaner cannot make a decision about which room to move to and perform the cleaning task. Likewise, state of the art cleaning robots do not move to other rooms without direct human interference. In a smart home monitoring system, sensor nodes may be deployed to monitor each separate room. In this work, a quad tree based data gathering scheme is proposed whereby the mobile sink physically moves through every room and logically links all separated sub-networks together. The proposed scheme sequentially collects data from the monitoring environment and transmits the information back to a base station. According to the sensor nodes information, the base station can command a cleaning robot to move to a specific location in the home environment. The quad tree based data gathering scheme minimizes the data gathering tour length and time through the efficient allocation of data gathering areas. A calculated shortest path data gathering tour can efficiently be allocated to the robotic cleaner to complete the cleaning task within a minimum time period. Simulation results show that the proposed scheme can effectively allocate and control the cleaning area to the robot vacuum cleaner without any direct interference from the consumer. The performance of the proposed scheme is then validated with a set of practical sequential data gathering tours in a typical office/home environment.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.