While existing approaches to human-robot collaboration typically focus on how to build robots that can work safely and fluently with humans on collaborative tasks, our research focuses on how people experience interaction with a robot and interpret its behaviour as cooperative or uncooperative. A microsociological theory was used to analyse the process of interaction as it unfolds, aiming to examine human perception of the cooperativeness and uncooperativeness of a robot and identify the boundary between them in the context of human-robot collaboration. Our hypothesis was that an unexpected robot movement during human-robot interaction will cause a negative perception of uncooperativeness. An experiment where the interaction was ‘disrupted’ by the robot’s movement during a collaborative task was conducted with 21 participants. Our findings, obtained through qualitative analysis based on semi-structured interviews and observations, show that the disruption leads certainly to a negative perception of the robot. The perception of robot cooperativeness or uncooperativeness, however, includes complex processes, and its boundary is not rigid, but flexible and nuanced.

While existing approaches to human-robot collaboration typically focus on how to build robots that can work safely and fluently with humans on collaborative tasks, our research focuses on how people experience interaction with a robot and interpret its behaviour as collaborative or non-collaborative. By applying a microsociological theory to analyse the process of interaction as it unfolds, our project aims to identify the boundary between cooperativeness and uncooperativeness of the robot in the shared task of a human and a robot carrying an object to a destination. The paper presents the experimental protocol designed based on the microsociological theory that is less known in current human-robot interaction studies. The aim of the experiment is to enable participants to experience expected and unexpected interactions with a robot and to account for how the interaction affects participant interpretation of robot cooperativeness and uncooperativeness.

Experiments were conducted to quantify the direction of robot approach that a pair of seated people find most comfortable. Three maximally-different seating configurations and eight directions of robot approach were considered. The data were analysed using Rayleigh's test of uniformity, a directional statistics method. Results from 140 participants showed robot approach directions that minimise participant discomfort align spatially with regions that allow good sight of the robot by both people and are centred on the pair's largest unoccupied area of p-space.

The study of human-robot interaction commonly occurs in a laboratory environment in an endeavour to control the experimental conditions. Although convenient for the experimenters, a laboratory setting can constrain and therefore alter a participant's behaviour. In this paper we introduce the method of `open experimentation' in which human-robot interaction studies take place outside the laboratory in a public social environment. Results from over 4,300 participants interacting with a humanoid robot, Diamandini, demonstrate that the general population preferred to observe the robot from a social distance, while children were comfortable to interact more closely with it and for extended periods of time.

This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional 'skeleton' data from an inexpensive consumer-level sensor, the Microsoft 'Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as a candidate unsupervised clustering algorithm - is able to cluster gait samples from four persons with a nett accuracy of 43.6%.

Disturbances that arise in material removal by repeated attempts to track the same path have the particular characteristics of non-repetitive magnitudes, but nearly-repetitive or gradual gradient transitions. This paper proposes and validates Iterative Learning Control (ILC) with a PD-type learning function for this class of disturbance as a predictive controller for autonomous excavation. However, parameters of the PD learning function may require different tunings for different excavation conditions, and convergence can be slow when compared to changes in excavation dynamics. In order to improve convergence, a plant inversion learning function is reinterpreted as a disturbance observer in the iteration domain, effectively rendering a disturbance learning controller (DLC). A hydraulic mini-excavator was used to evaluate experimentally the performance of the conventional ILC and the DLC against a robust controller. ILC achieved a desired cut profile with non-monotonic transients and DLC converged faster by learning disturbances directly from command discrepancies.

Gesture recognition is an important aspect of interpersonal social interaction. Developing a similar capacity in a robot will improve human-robot interaction. Various unsupervised clustering methods applied to clustering a set of dynamic human arm gestures are compared. Unsupervised clustering is important in gesture recognition as it imposes no a priori bound on the set of gestures. Results are compared using v-measure, a metric that allows differential weighting between clustering homogeneity and completeness. Experiments show that the best clustering method depends on the desired balance between homogeneity and completeness.

During social interaction, humans extract important information from tactile stimuli that improves their understanding of the interaction. The development of a similar capacity in a robot will contribute to the future success of intuitive human-robot interaction. This paper presents a method of touch sensing based on the principle of electrical impedance tomography (EIT) that can be used to implement a large, flexible and stretchable artificial sensitive skin for robots. A classifier based on the “LogitBoost” algorithm is used to classify the modality of six different types of touch on an experimental EIT-based skin. Experiments showed that the modality of touch was correctly classified in approximately 80% of the trials. This is comparable with the experimental accuracy of a human touch recipient. The classification accuracies show significant improvements from previous classification algorithms applied to different artificial sensitive skins.

Operational space control has a number of desirable characteristics but is sensitive to model accuracy. For heavy machines the dynamics are difficult to model due to their friction and dynamic coupling, thus making full compensation imprecise. This work presents an approach in which a simplified model gives partial compensation via an open-loop feedforward input, pre-calculated in forward simulation. In this way, effects that are difficult to compensate for can be partially corrected without causing instability. Since the reference trajectory is known a priori, dynamic model parameters are tuned in its neighbourhood, reducing the burden of global modelling. The feasibility and performance of this approach is shown experimentally via improved free motion tracking of an excavator arm. This framework further supports efforts for direct impedance control between bucket tip and soil.

Electrical impedance tomography (EIT) is a technique used to estimate the internal conductivity of an electrically conductive body by using measurements made only at its boundary. If this body is made of a thin, flexible, and stretchable material that responds to touch with local changes in conductivity, it can be used to create an artificial sensitive skin. Mathematically, the EIT reconstruction problem is an ill-posed nonlinear inverse problem in which it is commonly assumed that electrodes are located only on the surface of the body. In a thin sensitive skin, however, electrodes can readily be located within the 2-D conducting domain. This paper compares existing electrode-drive patterns with new patterns in which a number of reference electrodes are located inside of the sensitive skin. Simulation results and experimental data show improvements in both resolution and robustness to noise of the reconstructed image. These improvements are shown to be consistent for several commonly used regularization methods.

This paper presents the design of fuzzy rotating sliding mode controllers with an application to the swing control problem of electrohydraulic servo systems of a robotic excavator. By introducing the integral of differential pressure as an auxiliary state, the dynamic model of an electrohydraulic servo system can be cast as a second-order coupled nonlinear system. Using a successive loop closure procedure, the differential pressure is first considered as the control input for position tracking. The servovalve command voltage required to track the desired differential pressure is then computed. Fuzzy rotating sliding surfaces are applied to achieve fast tracking and robustness to uncertainties. The validity and advantage of the proposed technique are verified through simulation of the cab swing electrohydraulic servo system of a robotic excavator. Control implementation and experimental results are provided. The results obtained indicate the feasibility of the control technique in lower level control applications of the hydraulically-actuated robotic excavator.

In this paper, a fuzzy global controller based on sliding mode control is proposed for a reasonable amalgamation of local controllers to meet various control requirements for complex systems operating in highly uncertain environments. Also, a fuzzy logic approach is developed for the smooth allocation of several algorithms of a controller to gain the advantages, and at the same time to alleviate the disadvantages of each control algorithm. Illustrative examples are provided.

Compensating for friction is considered in the paper using the variable structure systems approach. First, variable structure-based observers are developed for friction estimation in mechanical systems with or without information of velocity. The estimates are then used for a model-based feedforward compensation for friction. For a non-model based approach, a robust sliding mode controller can also be used to cancel the influence of friction. Sigmoidal functions are used in lieu of signum functions to reduce chattering. Simulation results verify the validity of the proposed technique to compensate for friction of both static and dynamic models.

Modelling and experimental identification of friction has been carried out for an asymmetric hydraulic cylinder. A practical model of friction for this class of actuators is described. The model captures the magnitude and direction of friction forces without explicitly including a velocity-dependent term. Instead, it uses values of fluid pressure at the two cylinder ports, to yield a model that is less sensitive to load variation. Experimental parameter identification was undertaken in parallel for a classical viscous-Coulomb friction model. Two distinct friction regimes have been revealed, corresponding to "low" and "high" velocities. A comparison between the two models shows that the loss in accuracy in the pressure-based model is less than 1% when compared with the conventional one. Stiction analysis has also been performed. It has been found in this case that it is reasonable to assume invariance of stiction with piston position and rate of applied input. Friction was found to be highly dependent on the direction of piston motion in asymmetric hydraulic cylinders. Finally, computer simulation results show agreement with experimental data.

A crucial problem in crane control is to identify exactly the position and orientation of the load in space. This paper describes a new non-contact method for determining the crane load location by means of an inertial navigation system (INS) and a Kalman filter. The Kalman filter estimates the spreader position, which is not observed by the INS. Experiments were conducted on a 1/15th geometric scale model of a quay-crane. The work has potential application in the development of integrated estimation and control systems for full-scale quay-cranes.

This paper presents a novel robust sliding mode controller for a class of nonlinear systems. The control signal consists of: an equivalent control to assign the desired dynamics to the closed-loop system, a switching control to guarantee a sliding mode, and a fuzzy control to enhance fast tracking and to attenuate chattering. Sufficient conditions for asymptotic tracking are achieved when matching conditions are satisfied. The control method is applied to the position control of an electrohydraulic servo system. To verify the potential and effectiveness of the proposed controller, simulation results are given to demonstrate its strong robustness against a large range of parameter variations, load disturbance and nonlinear spring stiffness.

This paper describes the design and implementation of a semi-autonomous, and ultimately fully autonomous, container quay-crane. The new crane is based on a novel reeving arrangement which allows both fast and accurate gross motion as well as fine micropositioning. The paper describes the essential theory behind this design and presents experimental results from a 1/15th scale model. The proposed instrumentation of this crane is also briefly described as are key elements of the operator interface.

Real time estimation of soil parameters is essential in achieving precise, robust autonomous guidance and control of a tracked vehicle. The paper shows that the slip of the tracks over the terrain can be identified from trajectory data using an extended Kalman filter. The use of a suitable soil model can then allow key soil parameters to be estimated as the vehicle passes over the soil. Knowledge of the soil parameters may in turn be used to allow reference trajectories and control algorithms to be adjusted to suit the soil conditions.