Published papers

© 2005-2012 IEEE. Robotic teleoperation with a bilateral control system has attracted attention owing to its haptic transmission performance. However, conventional bilateral control systems require broadband communication to transmit the vivid haptic sensation. This problem limits the application range of the bilateral control systems. The communication traffic can be reduced by predicting and extrapolating the incoming data. However, in the conventional prediction-based methods, only one type (e.g., position, velocity, or force) of data is transmitted per one direction because of the difficulty of predicting multiple independent data. The novelty of this article is the realization of the prediction-based traffic reduction in the four-channel bilateral control system that transmits accurate haptic sensation by communicating both position and force data. By equivalently transforming this control scheme in the structure of impedance control, the transmit data are summed up to one data, equilibrium force. The equilibrium force is not only transmitted but extrapolated on the receiver side. As a result, the communication frequency becomes low without degrading haptic transmission performance. The validity of the proposed method was confirmed through experiments and succeeded to reduce the communication data size to less than 3.0%. The proposed method helps to realize a high-performance bilateral control system on band-limited networks.

Depth, proximity, and tactile sensors are often used in robotic object manipulation. The three types of sensors are combined to address each sensor limitation: Depth use is limited by blind spots and measurable range, proximity sensor is limited to millimetric distances, and the tactile sensor requires direct contact with the object. Here we present a novel sensor that can simultaneously measure millimetric distance and surface tilt, as well as contact force. We demonstrated the advantage of this novel sensor during a contact task. There, we implemented an admittance control and succeeded in achieving smooth transition from zero- to infinite-impedance contact condition and vice-versa. This novel sensor affords the realization of advanced interactions between robot and objects and contrary to previous approaches - limited by distance, blind spots, and impact force during initial contact - demonstrated good performance at all times with smooth and continuous measurements during contact.

Human support robots are in high demand and the performance enhancement through force control has been extensively studied. However, the design of the force controller and selection of appropriate gains are sometimes difficult because they are affected by the conditions of motion such as environmental impedance or model uncertainties. This study discusses force control with a disturbance observer and applies a neural network (NN) into its controller; the NN works as both the feedback and feedforward components. The contribution of this study is to show the development method of force control using disturbance observer and a NN, which enhances the performance of force control from the perspective of both feedback and feedforward components. The structure of the controller and composition of the NN were selected through simulation results; moreover, the compensator based on NN was designed in a frequency range higher than the cutoff frequency of the observer with a small number of hidden layers. Moreover, this study discusses a training method of weights in real time. Simulations and experiments were performed for showing the effectiveness of the proposal.

This paper analyses Disturbance Observer- (DOb-) based robust force control systems in the discrete-time domain. The robust force controller is implemented using velocity and acceleration measurements. A DOb is employed in an inner-loop to achieve robustness, and another DOb, viz. Reaction Force Observer (RFOb), is employed in an outer-loop to estimate interaction forces and improve the performance of force control. First, the inner-loop is analysed. It is shown that the DOb works as a phaselead/lag compensator tuned by the nominal design parameters in the inner-loop. The phase margin of the inner-loop controller and the bandwidth of the velocity-based (i.e., conventional) DOb are constrained not only by noise-sensitivity but also by the waterbed effect. This explains why we observe unstable responses as the bandwidth of the conventional DOb increases in practice. To eliminate the design constraint due to the waterbed effect, this paper proposes an acceleration-based DOb. Then, the robust force controller is analysed. It is shown that the design parameters of the RFOb have a notable effect on the stability of the robust force control system. For example, the robust force controller has a non-minimum phase zero (zeros) when the RFOb is not properly tuned. This may cause severe stability and performance problems when conducting force control applications. By using the stability and robustness analyses, this paper proposes new design tools which enable one to synthesize a high-performance robust force control system. Simulations and experiments are presented to validate the proposed analysis and synthesis methods.

The development of automatically controlled medical robots (ACMR) has certain limitations. Due to their rigidity, if they are used in surgery, it would be extremely difficult to protect the patient's soft blood vessels and nerves. Hence, ACMR have not been put into practical use. The main purpose of this research is to address the limitations in order to develop the world's first ACMR that has a successful practical application within surgery. The first surgical procedure we aim to apply the ACMR to is Le Fort I osteotomy in maxillofacial surgery. While performing Le Fort I osteotomy, the robots must avoid damage to the descending palatine artery, which can cause fatal bleeding. We succeeded in automatically stopping the osteotomy immediately before the artery without the need to acquire model numerical information in advance, that is, with a model-free detection. The proposed method for auto-stopping is to identify the osteotomy reaction force obtained by the reaction force observer using the concept of the Z-score.