International Journal of Humanoid Robotics

In this paper, the exploration and recognition in unknown object perception by robot hand is discussed. Inspired by the touch and exploration of human hand, a haptic exploration strategy for multi-fingered robot hand is proposed. Based on the observations from human experiments, the proposed strategy can be used to guide the robot hand to plan a series of movements to get tactile information from different unknown objects, with the precondition of avoiding unexpected collisions with the objects. A recognition approach is then presented to recognize object shapes based on the tactile point data collected by the strategy. Geometric feature vectors are extracted from tactile point locations and normal vectors after clustering, and the object shapes are recognized by the random forests classifier. Simulations and experiments results show that the exploration strategy can be used to guide the robot to gather tactile information from unknown object automatically, and the recognition approach is effective and robust in object shape recognition work. This framework provides a sensible solution for robot unknown object perception problem, which is suitable for the multi-fingered robot hand with low-resolution tactile sensors.

This paper proposes a novel coupled and active (CA) grasping mode for robot finger. While coupled grasping mode enables the phalanges of the finger to rotate in an anthropomorphic way, the active grasping mode enables the finger to adapt to the object’s shape automatically. Combination of these grasping modes can greatly reduce the controlling complexity and enlarge the application of coupled hands. The CA finger is developed based on the pulley-belt mechanism. It is composed of a finger base and two mobile phalanges. The control process of the finger is also introduced in this paper. Grasping capabilities is validated by experiments. Theoretical and experimental results show that the CA grasping mode is effective.

Underactuated robotic hands get a lot of attention in recent years. In order to analyze the grasp ability and optimize structure parameters of underactuated hands for effective grasp, new grasp performance metrics have been defined. The new metrics quantify the effective grasp region and grasp stability of underactuated hands. A general method to calculate the new performance metrics is introduced by developing a static grasp model. Then an optimization method of underactuated hands is proposed based on the new metrics and structure parameters of a general underactuated hand are optimized. Finally, the performance of the optimized hand is experimentally validated.

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination ($R^2)$ value of $0.934\pm 0.013$ and the normalized root-mean-square error (RMSE) of $11.58\%\pm 1.44\%$.

Robot-assisted therapy has played a significant role in helping the disabled patients to restore motor functions. In this paper, a redundant exoskeleton is developed for upper-limb rehabilitation. An analytical methodology for obtaining the inverse kinematic solution of the exoskeleton is presented to provide synchronized movement with patients and ensure natural human–robot interaction. To mathematically express the redundancy problem, the swivel angle of elbow is introduced as an additional parameter to specify the human arm congratulation with a predefined wrist location. A kinematic criterion is proposed to determine the swivel angle by imitating the natural reflexive reaction of human arm. The effectiveness of the proposed strategy is experimentally evaluated via four representative types of upper-limb motion tasks. During the experiments, the actual kinematic data of human arm is collected by utilizing an articulated motion capture system integrated with inertial sensors and, after that, compared to the estimation results generated by the proposed redundancy resolution. The experimental results indicate that the kinematic criterion of swivel angle is suitable to describe the free reaching movement without additional constraints. Moreover, with the estimated swivel angles, the root mean square errors between the actual and calculated joint angles are normally less than 8${}^{\circ }$.

This paper describes a novel zero moment point (ZMP) tracking control strategy using a disturbance observer (DOB) in the presence of ground slope change for balance control of an android robot. With regard to conventional ZMP controls, many researchers have studied ZMP tracking control strategies using an inverted pendulum model on flat level ground, and they have solved a slow response problem of nonminimum phase systems by using suitable feedforward motions called walking patterns. However, the conventional methods lead to ZMP offset errors in the presence of ground slope change; it is hence necessary to quickly eliminate the ZMP offset errors to realize robust balance control. In this paper, we rapidly eliminate the ZMP offset errors through a DOB using a model inversion for robust balance control in the presence of ground slope change. In particular, a dynamic model that uses the projected center of mass (CoM) position on the ground is additionally used as an output to solve a problem that generates an unstable pole during model inversion. Finally, the proposed control strategy is verified through MATLAB simulations and experiments using a real android leg.

Human controls dozens of muscles for different hand postures in a coordinated manner. Such coordination is referred to as a postural synergy. Postural synergy has enabled an anthropomorphic robotic hand with many actuators to be applied as a prosthetic hand and controlled by two to three channels of biological signals. Principle component analysis (PCA) of the hand postures has become a popular way to extract the postural synergies. However, relatively big errors are often produced while the hand postures are reconstructed using these PCA-synthesized synergies due to the linearity nature of this method. This paper presents a comparative study in which the postural synergies are synthesized using both linear and nonlinear methods. Specifically, the Gaussian process latent variable model (GPLVM), as a nonlinear dimension reduction method, is implemented to produce nonlinear postural synergies and the hand postures can then be reconstructed from the two-dimensional synergy plane. Computational and experimental verifications show that the posture reconstruction errors are greatly reduced using this nonlinear method. The results suggest that the use of nonlinear postural synergies should be considered while applying a dexterous robotic hand as prosthesis. Versatile hand postures could be formed via only two channels of bio-signals.

A significant amount of work has been reported in the area of vision-based stabilization of wheeled robots during the last decade. However, almost all the contributions have not considered the actuator dynamics in the controller design. Considering the unknown parameters of the robot kinematics and dynamics incorporating the actuator dynamics, this paper presents a vision-based robust adaptive controller for the stabilization of a wheeled humanoid robot by using the adaptive backstepping approach. For the controller design, the idea of backstepping is used and the adaptive control technique is applied to treat all parametric uncertainties. Moreover, to attenuate the effect of the external disturbances on control performance, smooth robust compensators are employed. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, simulation results are given to verify the effectiveness of the proposed controller.

In order to enhance consistency in simultaneous localization and mapping (SLAM), in this paper, this problem is considered as a solely localization problem in the presence of unknown parameters. In this approach, the proposal distribution is generated based on marginal extended particle filter and static map is considered as a parametric estimation that is estimated by maximum likelihood techniques. Significant improvement of the filtering result from this viewpoint is demonstrated in terms of estimation performance and consistency. Some simulations and experiments are presented to evaluate the algorithm’s performance in comparison to conventional methods.