Whole-finger manipulation with a two-fingered robot hand

This paper describes a method for whole-finger rolling manipulation using a two-fingered robot hand. 'Whole-finger' refers to the use of the complete phalangeal surface during the manipulation. An example of whole-finger manipulation by the human hand is the rolling of a pen between two fingers. The proposed method is based on a two-dimensional model for modelling an object manipulation and is derived from a study of the movement of the contact line between both fingers. Also, the method uses tactile sensor information to estimate the contact point position together with the local curvature of the object. This whole-finger dexterous manipulation is demonstrated on a prototype two-fingered hand. This 5 d.o.f. hand consists of a tendon driven index and thumb, and is equipped with force and tactile sensors. The dimensions and performance of this device are 'human-sized'. A hybrid force-position control scheme is used. The hierarchical control structure is implemented on a dual transputer system. This paper first describes the kinematic model used for whole-finger manipulation. In the second part, the main emphasis is put on the mechanical design and on the transputer-based control system.     

A sensor for dynamic tactile information with applications in human–robot interaction and object exploration

We present a novel tactile sensor, which is applied for dextrous grasping with a simple robot gripper. The hardware novelty consists of an array of capacitive sensors, which couple to the object by means of little brushes of fibers. These sensor elements are very sensitive (with a threshold of about 5 mN) but robust enough not to be damaged during grasping. They yield two types of dynamical tactile information corresponding roughly to two types of tactile sensor in the human skin. The complete sensor consists of a foil-based static force sensor, which yields the total force and the center of the two-dimensional force distribution and is surrounded by an array of the dynamical sensor elements. One such sensor has been mounted on each of the two gripper jaws of our humanoid robot and equipped with the necessary read-out electronics and a CAN bus interface. We describe applications to guiding a robot arm on a desired trajectory with negligible force, reflective grip improvement, and tactile exploration of objects to create a shape representation and find stable grips, which are applied autonomously on the basis of visual recognition.     

Development of soft and distributed tactile sensors and the application to a humanoid robot

This paper describes a comprehensive tactile sensor system which can cover wide areas of full-body robots. Based on design criteria which are introduced from requirements, we develop two types of tactile sensor elements. One is a multi-valued touch sensor which has multi-level pressure thresholds. It is capable of covering wide areas of robot surfaces. The other is made of soft, conductive gel, which has the advantage of compliance compared with other sheet-type tactile sensors. With these two types sensors, we develop the tactile sensor system on the full-body robot 'H4'. Details of the sensor system on the robot and some experiments using tactile information are described.     

Realizing whole-body tactile interactions with a self-organizing,multi-modal artificial skin on a humanoid robot

In this paper, we present a new approach to realize whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot. We, therefore, equipped the whole upper body of the humanoid HRP-2 with various patches of CellulARSkin – a modular artificial skin. In order to automatically handle a potentially high number of tactile sensor cells and motors units, the robot uses open-loop exploration motions, and distributed accelerometers in the artificial skin cells, to acquire its self-centered sensory-motor knowledge. This body self-knowledge is then utilized to transfer multi-modal tactile stimulations into reactive body motions. Tactile events provide feedback on changes of contact on the whole-body surface. We demonstrate the feasibility of our approach on a humanoid, here HRP-2, grasping large and unknown objects only via tactile feedback. Kinesthetically taught grasping trajectories, are reactively adapted to the size and stiffness of different test objects. Our paper contributes the first realization of a self-organizing tactile sensor-behavior mapping on a full-sized humanoid robot, enabling a position controlled robot to compliantly handle objects.     

A servo-controlled robot gripper with multiple sensors and its logical specification

The logical specification of a microprocessor-based air-servo-controlled robot hand is presented, as well as its actual implementation. This hand can accommodate a wide variety of workpieces and allows for flexible assembly through the use of an automatic quick-change fingertip. The changeable set of gripper fingers is equipped with sensors, including a tactile force sensor, a crossfire sensor, a proximity sensor, and a slip sensor. A changeable set of gripper fingers with different sensing ranges can cope with certain subranges of the workpiece spectrum. A considerable cost saving is achieved by not changing the gripper itself. This specially designed hardware and software system includes position and force feedback. A PUMA 560 is used to test the success of the entire process.     

On-line exploration of an unknown surface by a robotic probe

A control strategy is developed for a robotic probe with tactile sensing to explore an unknown surface without losing contact. Digital computer simulations are performed of a three-link, planar manipulator exploring an ellipsoidal surface in order to test the control strategy. The equations of motion are written and linear, time-varying, state-variable feedback is applied to stabilize and decouple the system. A tactile sensor is simulated to supply the normal force between the robot and the surface. From the magnitude and direction of this force, the desired trajectory is determined. An inverse plant and force feedback are implemented to provide the required input torques to the robot's actuators.     

机器人多维力传感器

多维力觉感知系统在柔性精密操控、零力示教、轮廓跟踪、自动柔性装配、机器人多手协作、机器人临场感和遥操作、机器人虚拟和远程手术、康复训练等场合有巨大的需求。力敏元件的性能是决定机器人多维力传感器的各项性能指标的关键因素之一。早在20世纪70年代初,力觉信息的获取和应用就已开始被国际上关注,并获得了初步的探讨。自20世纪80年代末起,我国学者也开始关注和研究力觉感知技术。对机器人多维力传感器力敏元件的设计和研究进展进行了综述,对高精度力信息获取面临的挑战展开了分析,并对机器人多维力传感器的发展趋势和解决思路提出了一些可行性的思路。     

机器人传感器触觉心理量检测的研究

为了让机器人传感器具有触觉心理感知的能力,提出从接触力信息中提取被接触者触觉心理量的方法。该方法从人类触觉的心理物理学定律出发,在实验测量基础上拟合出人体皮肤触觉心理量与外部刺激之间的数学关系,将其应用到机器人传感器中实现了对接触者的触觉心理量的检测。实验证明:这种方法具有信号处理简单、误差小等优点。     

Walking partner robot chatting about scenery

ABSTRACT

We believe that many future scenarios will exist where a partner robot will talk with people on walks. To improve the user experience, we aim to endow robots with the capability to select an appropriate conversation topics by allowing them to start chatting about a topic that matches the current scenery and to extend it based on the user's interest and involvement in it. We implemented a function to compute the similarities between utterances and scenery by comparing their topic vectors. First, we convert the scenery into a list of words by leveraging Google Cloud Vision library [Google Cloud Vision Api [Online]. Available from: https://cloud.google.com/vision/] . We form a topic vector space with the Latent Dirichlet Allocation method and transform a list of words into a topic vector. Our system uses this function to choose (from an utterance database) the utterance that best matches the current scenery. Then it estimates the user's level of involvement in the chat with a simple rule based on the length of their responses. If the user is actively involved in the chat topic, the robot continues the current topic using pre-defined derivative utterances. If the user's involvement sags, the robot selects a new topic based on the current scenery. The topic selection that is based on the current scenery was proposed in our previous work [Totsuka R, Satake S, Kanda T, et al. Is a robot a better walking partner if it associates utterances with visual scenes? ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2017), Aula der Wissenschaft, Vienna, Austria; 2017. p. 313–322]. Our main contribution is the whole chat system, which includes the user's involvement estimation. We implemented our system with a shoulder-mounted robot and conducted a user study to evaluate its effectiveness. Our experimental results show that users evaluated the robot with the proposed system as a better walking partner than one that randomly chose utterances.     

Electromyography-based control of active above-knee prostheses

This paper presents a new electromyography (EMG)-based control approach for above-knee (AK) prostheses, which enables the user to control the prosthesis motion directly with his or her muscle activating neural signals. Furthermore, the unique ‘active-reactive’ control structure mimics the actuation mechanism of a human biological joint, and thus provides the user an experience similar to that of a biological lower limb in the control process. In the proposed control approach, surface EMG is utilized to provide a non-intrusive interface to the user's central nervous system, through which the muscle-activating signals can be obtained. With the EMG signals as inputs, an ‘active-reactive’ control algorithm is developed based on the analysis on a simplified musculoskeletal structure of human biological joint. This control algorithm incorporates an ‘active’ component, which reflects the user's active effort to actuate the joint, and a ‘reactive’ component, which models the reaction of the joint to the motion as a result of the controllable impedance displayed on the joint. With this unique structure, the controller enables the active control of the joint motion, while at the same time achieves a natural interaction with the environment through the modulation of the joint impedance. The effectiveness of the proposed control approach was demonstrated through a set of free swing experiments, in which the user was able to control the prosthesis to follow arbitrary motion commands, and a set of level walking experiments, in which the user achieved natural walking gait similar to the typical walking gait of healthy subjects.     

Robust bin-picking system using tactile sensor

ABSTRACT

This paper presents a robust bin-picking system utilizing tactile sensors and a vision sensor. The object position and orientation are estimated using a fast template-matching method through the vision sensor. When a robot picks up an object, the tactile sensors detect the success or failure of the grasping, and a force sensor detects the contact with the environment. A weight sensor is also used to judge whether the lifting of the object has been successful. The robust and efficient bin-picking system presented herein is implemented through the integration of different sensors. In particular, the tactile sensors realize rope-shaped object picking that has yet to be made possible with conventional picking systems. The effectiveness of the proposed method was confirmed through grasping experiments and in a competitive event at the World Robot Challenge 2018.     

On cooperative manipulation of dynamic objects

This paper proposes a decentralized position/internal force hybrid control approach for multiple robot manipulators to cooperatively manipulate an unknown dynamic object. In this approach, each autonomous robot has its own controller and uses its own sensor information in performing the fast cooperation. This approach eliminates a lot of information communications between each robot and reduces numerous computations. The influences of the position and the internal force estimation errors to the overall control system is analyzed. A cooperative identification method for each autonomous robot to identify the object's complex dynamics, cooperatively, is presented. In addition, the trade-off between the unilateral force constraint and the robots' position response is studied. Experiments show the effectiveness of this control approach.     

Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

ABSTRACT

In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection.     

Fractional-order sliding mode control for a novel magneto-electro-elastic microtube robot

In this paper, a tracking controller based on a non-integer sliding surface is proposed for a magneto-electro-elastic (MEE) fluid-conveying microtube robot. The smart/adaptive MEE material enables us to control the robot with no need for external sensors and actuators. The micro-robot lateral motion is modeled by Euler–Bernoulli beam equations. The governing equation of the robot is derived using the constitutive equations of MEE materials and Maxwell's principle followed by Hamilton's variational method. Based on the extracted dynamic model, a novel non-integer order sliding mode controller is introduced to suppress the microtube vibration and to provide robust path following for the robot tip. This control approach is compatible with the parameter-varying nature of the robot dynamics. Theoretical analyses, based on Lyapunov theory, are also conducted to verify the stability of the closed-loop system. Comparative simulations are finally performed to show the efficiency of the proposed system in comparison with the conventional micro tubes made of smart materials and with an integer order sliding mode controller (SMC). The results demonstrate that the proposed robot properly meets the performance requirements in terms of vibration suppression and trajectory tracking, even in the presence of disturbances.     

Mobile robot navigation using vision and olfaction to search for a gas/odor source

This paper presents a new approach to search for a gas/odor source using an autonomous mobile robot. The robot is equipped with a CMOS camera, gas sensors, and airflow sensors. When no gas is present, the robot looks for a salient object in the camera image. The robot approaches any object found in the field of view, and checks it with the gas sensors to see if the object is releasing gas. On the other hand, if the robot detects the presence of gas while wandering around the area, it turns toward the direction of the wind that carries the gas. The robot then looks for any visible object in that direction. These navigation strategies are implemented into the robot under the framework of the behavior-based subsumption architecture. Experimental results on the search for a leaking bottle in an indoor environment are presented to demonstrate the validity of the navigation strategies.     

Mutual adaptation among man and machine by using f-MRI analysis

A prosthetic device for disabled people requires new and reliable robotics technology. This paper describes the interesting reaction of our brain to an adaptable prosthetic system. The adaptable prosthetic system is composed of an EMG signal controlled robot hand with an EMG pattern recognition learning function for Transradial (below elbow) prostheses. The mutual adaptation between the system and the human body is analyzed using functional magnetic resonance imaging (f-MRI) in order to clarify the plasticity of the motor and sensory cortex area according to the changes in the prosthetic system. The developed prosthetic hand has 13 DOF: three motors on the thumb, two motors for each finger, and two motors for the wrist. The tactile feedback is applied by using surface electrical stimulus. The f-MRI data shows the process of replacement from a phantom limb image to the prosthetic hand image.     

基于PVDF的三维力机器人触觉传感器的设计

当机器手抓取时物体受力信息检测是抓取过程顺利进行的基础,检测三维方向上的力便可充分反映物体的受力信息。当前用于抓取过程中三维力检测的触觉传感器还存在着一些不足,基于此,论文拟设计一种基于PVDF的三维力机器人触觉传感器。论文展示了传感器的结构设计,建立了压电薄膜及传感头结构的数学模型,设计了调理电路并对传感器进行测试和验证,结果表明该传感器能有效检测机器手抓取过程中的三维力信息。     

Adaptive hybrid control of manipulators on uncertain flexible objects

This paper presents an adaptive hybrid control approach for a robot manipulator to interact with its flexible object. Because of its flexibility, the object dynamics influence the robot's control system, and since it is usually a distributed parameter system, the object dynamics as seen from the robot change when the robot moves. The problem becomes further complicated such that it is difficult to decompose the robot's position and contact force control loops. In this paper, we approximate the object's distributed parameter model into a lumped 'position state-varying' model. Then, by using the well-known nonlinear feedback compensation, we decompose the robot's control space into a position control subspace and object torque control subspace. We design the optimal state feedback for the position control loop and control the robot's contact force through controlling the resultant torque of the object. We use the model-reference simple adaptive control strategy to control the torque control loop. We also study the problem on how to select a reasonable reference model for this control loop. Experiments of a PUMA robot interacting with an aluminum beam show the effectiveness of our approach.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

Robotic sanding system for new designed furniture with free-formed surface

In this paper, a sanding system based on an industrial robot with a surface following controller is proposed for the sanding process of wooden materials constructing furniture. Handy air-driven tools can be easily attached to the tip of the robot arm via a compact force sensor. The robotic sanding system is called the 3D robot sander. The robot sander has two novel features. One is that the polishing force acting between the tool and wooden workpiece is delicately controlled to track a desired value, e.g., 2 kgf. The polishing force is defined as the resultant force of the contact force and kinetic friction force. The other is that no complicated teaching operation is required to obtain a desired trajectory of the tool. Cutter location (CL) data, which are tool paths generated by a CAD/CAM system, are directly used for the basic trajectory of the handy tool attached to the robot arm. The robot sander can be applied to the sanding task of free-formed curved surface with which conventional sanding machines have not been able to cope. The effectiveness and promise are shown and discussed through a few experiments.