A selective attention-based contextual perception approach for a humanoid robot

A humanoid robot is always flooded by sensed information when sensing the environment, and it usually needs significant time to compute and process the sensed information. In this paper, a selective attention-based contextual perception approach was proposed for humanoid robots to sense the environment with high efficiency. First, the connotation of attention window (AW) is extended to make a more general and abstract definition of AW, and its four kinds of operations and state transformations are also discussed. Second, the attention control policies are described, which integrate intensionguided perceptual objects selection and distractor inhibition, and can deal with emergent issues. Distractor inhibition is used to filter unrelated information. Last, attention policies are viewed as the robot’s perceptual modes, which can control and adjust the perception efficiency. The experimental results show that the presented approach can promote the perceptual efficiency significantly, and the perceptual cost can be effectively controlled through adopting different attention policies.     

Shared control architecture based on RFID to control a robot arm using a spontaneous brain–machine interface

This paper describes a shared control architecture combining a Brain–Machine Interface (BMI) with Radio-frequency Identification (RFID) technology to control a robot arm in pick and place operations. A non-invasive spontaneous BMI capable of distinguishing between three different mental tasks has been designed. Using the BMI, the user can control the robot in order to perform complex actions (e.g. pick and place operations). RFID tags have been placed in the experimental setup to give information about the position of the objects in the scene. With this information, the user is able to pick and place the objects with a robot arm by performing simple commands: move left, move right, pick or place, with the only help of the BMI. Four volunteers have successfully controlled the robot arm, and time and accuracy have been measured.     

A neural model for eye–head–arm coordination

The coordinated movement of the eyes, the head and the arm is an important ability in both animals and humanoid robots. To achieve this, the brain and the robot control system need to be able to perform complex non-linear sensory-motor transformations in the forward and inverse directions between many degrees of freedom. In this article, we apply an omnidirectional basis function neural network to this task. The proposed network can perform 3-D coordinated gaze shifts and 3-D arm reaching movements to a visual target. Particularly, it can perform direct sensory-motor transformations to shift gaze and to execute arm reach movements and can also perform inverse sensory-motor transformations in order to shift gaze to view the hand.     

Incremental learning of gestures for human–robot interaction

For a robot to cohabit with people, it should be able to learn people’s nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human–robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing incremental neural network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: “Keep on going left slowly” has three meanings such as, “Keep on (1)”, “going left (2)”, “slowly (3)”. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN.     

A unified multimodal control framework for human–robot interaction

In human–robot interaction, the robot controller must reactively adapt to sudden changes in the environment (due to unpredictable human behaviour). This often requires operating different modes, and managing sudden signal changes from heterogeneous sensor data. In this paper, we present a multimodal sensor-based controller, enabling a robot to adapt to changes in the sensor signals (here, changes in the human collaborator behaviour). Our controller is based on a unified task formalism, and in contrast with classical hybrid visicn–force–position control, it enables smooth transitions and weighted combinations of the sensor tasks. The approach is validated in a mock-up industrial scenario, where pose, vision (from both traditional camera and Kinect), and force tasks must be realized either exclusively or simultaneously, for human–robot collaboration.     

A client–server architecture for remotely controlling a robot using a closed-loop system with a biological neuroprocessor

This paper introduces an open-source real-time system that remotely controls a robot using human neuroblastoma cultures and a client–server architecture. Multielectrode array set-ups have been designed for direct culturing of neural cells over silicon or glass substrates, providing the capability simultaneously to stimulate and record populations of neural cells. However, it is very difficult to attach these neural cells to the robot structure due to the special conditions of the biological material. The main objective of this research is to build a client–server system for remotely connecting a robot to a neural culture in a closed-loop experimentation. The robot sensors will feed the biological neuroprocessor, while the neural activity will be used for guiding the robot, controlling in this way the robotic behaviour.     

Visualisation of constrained predictive control of a liquid–liquid extractor

The extraction of aqueous oxalic acid into a mixture of tributyl phospate and dodecane in a pulsed column exhibited strong coupling between the two inputs, solvent feed-rate and pulsation frequency, and the two outputs, raffinate pH and bottom conductivity (an indication of flooding). A visualisation technique in which both the outputs and inputs and their constraints were represented on a phase plane proved useful to explain circuitous reponses and settling points that did not agree with setpoints. The visualisation is proposed as a useful real-time diagnostic tool which condenses the available information and creates easily recognisable patterns as a basis for tuning or problem identification.     

A note on a family of acceptors for some families of developmental languages†

In a previous paper Rozenberg (1974) introduced the PDAC acceptors (pushdown-array-of-counters acceptors) and showed that the restricted PDACA characterize the EOL languages. In this present note the PDAPDA (pushdown-array-of-pushdowns acceptors) are introduced and it is shown that the restricted PDA² characterize the ETOL languages.     

Boundary control for a constrained two-link rigid–flexible manipulator with prescribed performance

In this paper, we consider a boundary control problem for a constrained two-link rigid–flexible manipulator. The nonlinear system is described by hybrid ordinary differential equation–partial differential equation (ODE–PDE) dynamic model. Based on the coupled ODE–PDE model, boundary control is proposed to regulate the joint positions and eliminate the elastic vibration simultaneously. With the help of prescribed performance functions, the tracking error can converge to an arbitrarily small residual set and the convergence rate is no less than a certain pre-specified value. Asymptotic stability of the closed-loop system is rigorously proved by the LaSalle's Invariance Principle extended to infinite-dimensional system. Numerical simulations are provided to demonstrate the effectiveness of the proposed controller.     

Tracking control of a two-wheeled mobile robot using input–output linearization

This paper discusses the tracking control of two-wheeled mobile robots that have more outputs than inputs. If the number of inputs and the number of outputs are not the same, the inverse of the decoupling matrix in the input–output linearization does not exist. Therefore, a modified input–output linearization method is proposed, to solve the problem by means of a generalized inverse that provides a least-squares solution. The experimental results show that the tracking of posture, position and orientation (the 3-output case) has advantages over position tracking (the 2-output case) from the viewpoint of input power efficiency, because smoother responses can be obtained by considering the orientation.     

A teaching–learning based optimization approach for economic design of X-bar control chart

Being simple to use X-bar control chart has been most widely used in industry for monitoring and controlling manufacturing processes. Measurements of a quality characteristic in terms of samples are taken from the production process at regular interval and the sample means are plotted on this chart. Design of a control chart involves the selection of three parameters, namely the sample size (n), the sampling interval (h) and the width of control limits (k). In case of economic design, these three control chart parameters are selected in such a manner that the total cost of controlling the process is the least. The effectiveness of this design depends on the accuracy of determination of these three parameters. In this paper, a new efficient and effective optimization technique named as teaching–learning based optimization (TLBO) has been used for the global minimization of a loss cost function expressed as a function of three variables n, h and k in an economic model of X-bar chart based on unified approach. In this work, the TLBO algorithm has been modified to simplify the tuning of teaching factor. A MATLAB computer program has been developed for this purpose. A numerical example has been solved and the results are found to be better than the earlier published results. Further, the sensitivity analysis using fractional factorial design and analysis of variance have been carried out to identify the critical process and cost parameters affecting the economic design.     

Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment

Most e-learning environments which utilize user feedback or profiles, collect such information based on questionnaires, resulting very often in incomplete answers, and sometimes deliberate misleading input. In this work, we present a mechanism which compiles feedback related to the behavioral state of the user (e.g. level of interest) in the context of reading an electronic document; this is achieved using a non-intrusive scheme, which uses a simple web camera to detect and track the head, eye and hand movements and provides an estimation of the level of interest and engagement with the use of a neuro-fuzzy network initialized from evidence from the idea of Theory of Mind and trained from expert-annotated data. The user does not need to interact with the proposed system, and can act as if she was not monitored at all. The proposed scheme is tested in an e-learning environment, in order to adapt the presentation of the content to the user profile and current behavioral state. Experiments show that the proposed system detects reading- and attention-related user states very effectively, in a testbed where children’s reading performance is tracked.

A study on a brachiation controller for a multi-locomotion robot — realization of smooth,continuous brachiation

In this paper, we present a control method to realize smooth continuous brachiation. The target brachiation is basically divided into two actions: a swing action and a locomotion action. In order to realize the continuous brachiation effectively and smoothly, it is necessary to start the swing action as soon as the robot grasps the front target bar at the end of the locomotion action. The collision, which occurs at the moment the robot grips the target bar, affects the pendulum motion of the robot. The action of bending the elbow joint of the swinging arm is proposed in order to solve this gripping problem. The elbow-bending action enables the robot to decrease the impact forces and use the excess mechanical energy after the end of the locomotion phase. Thus, there is no loss of energy and waste of time during the subsequent swing phase. Experimental results show that the robot can successfully achieve smooth, continuous brachiation.     

Design of a quadruped robot for human–elephant conflict mitigation

Introduction

Human–elephant conflict is a major problem leading to crop damage, human death by elephants and elephants being killed by people. The surveillance and tracking of elephant herds are difficult due to their size and nature of movement.

Materials

In this article, we propose a four-wheeled quadruped robot to mitigate human–elephant conflict. The robot can detect movement of wild pachyderms in certain pockets along the forest borders through which the elephants enter into the human living areas from the forest. The robot is so designed that it can navigate with wheels on flat terrains and with legs on unfriendly rugged terrains with the help of mounted cameras.

Methods

The images of the wild elephant are captured and transmitted to the base stations and an SMS is sent to the forest officials indicating an elephant is found. We obtain a suitable kinematic model for both legs and wheels with control algorithm for the quadruped robot to move along a predetermined path.

Conclusion

The quadruped robot proposed is a solution to detect elephant movement without affecting the ecological conditions to overcome human–elephant conflict. The unpredictability of time and location of elephant arrival into the villages are considered the major issues that are resolved in this work. The results of our work contribute to elephant conservation issues and are suitable for the detection of elephants in forest border areas.     

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

     

System identification and control of the ground operationmode of a hybrid aerial–ground robot

This paper presents an in-depth analytical and empirical assessment of the performance of DoubleBee, a novel hybrid aerial–ground robot. Particularly, the dynamic model of the robot with ground contact is analyzed, and the unknown parameters inthe model are identified. We apply an unscented Kalman filter-based approach and a least square-based approach to estimatethe parameters with given measurements and inputs at every time step. Real data are collected and used to estimate theparameters; test data verify that the values obtained are able to model the rotation of the robot accurately. A gain-scheduledfeedback controller is proposed, which leverages the identified model to generate accurate control inputs to drive the systemto the desired states. The system is proven to track a constant-velocity reference signal with bounded error. Simulations andreal-world experiments using the proposed controller show improved performance than the PID-based controller in trackingstep commands and maintaining attitude under robot movement.     

Human–robot collaboration and machine learning: A systematic review of recent research

Technological progress increasingly envisions the use of robots interacting with people in everyday life. Human–robot collaboration (HRC) is the approach that explores the interaction between a human and a robot, during the completion of a common objective, at the cognitive and physical level. In HRC works, a cognitive model is typically built, which collects inputs from the environment and from the user, elaborates and translates these into information that can be used by the robot itself. Machine learning is a recent approach to build the cognitive model and behavioural block, with high potential in HRC. Consequently, this paper proposes a thorough literature review of the use of machine learning techniques in the context of human–robot collaboration. 45 key papers were selected and analysed, and a clustering of works based on the type of collaborative tasks, evaluation metrics and cognitive variables modelled is proposed. Then, a deep analysis on different families of machine learning algorithms and their properties, along with the sensing modalities used, is carried out. Among the observations, it is outlined the importance of the machine learning algorithms to incorporate time dependencies. The salient features of these works are then cross-analysed to show trends in HRC and give guidelines for future works, comparing them with other aspects of HRC not appeared in the review.     

Robust variable admittance control for human–robot co-manipulation of objects with unknown load

Over the last years, physical Human–Robot Interaction (pHRI) has become a particularly interesting topic for industrial tasks. An important issue is allowing people and robots to collaborate in an useful, simple and safe manner. In this work, we propose a new framework that allows the person to collaborate with a robot manipulator, while the robot has its own predefined task. To allow the robot to smoothly switch from its own task to be a compliant collaborator for the person, a variable admittance control is developed. Furthermore, in general the task to accomplish requires the robot to carry variable, unknown, loads at the end-effector. To include this feature in our framework, a robust control is also included to preserve the performance of the robot despite uncertainties coming from the unknown load. To validate our approach, experiments were carried out with a Kuka LBR iiwa 14 R820, first to validate both parts of the controller, and finally, to study a use-case scenario similar to an industrial production line. Results show the efficiency of this approach to allow the person to collaborate at any moment while the robot is capable of performing another task. This flexible framework for object co-manipulation also allows unknown loads up to 2 kg to be handled without making the task more difficult for the person.     

Trajectory control of a two DOF rigid–flexible space robot by a virtual space vehicle

Model based control schemes use inverse dynamics of the robot arm to produce the main torque component necessary for trajectory tracking. For a model-based controller one is required to know the model parameters accurately. This is a very difficult job especially if the manipulator is flexible. This paper presents a control scheme for trajectory control of the tip of a two arm rigid–flexible space robot, with the help of a virtual space vehicle. The flexible link is modeled as an Euler–Bernoulli beam. The developed controller uses the inertial parameters of the base of the space robot only. Bond graph modeling is used to model the dynamics of the system and to devise the control strategy. The efficacy of the controller is shown through simulated and animation results.