Due to increasing demand for rehabilitation and therapy for cerebrovascular diseases, patients require advanced development of medical rehabilitation robots. In our laboratory, we focus on the formation capability of the substitute neural path caused by brain plasticity using the kinesthetic illusion (KI), which is effective for therapies using robots. In KI, people perceive an illusionary limb movement without an actual movement when a vibration stimulus is applied to a limb’s tendons. In previous research, the optimal frequency that induces the maximum KI has a correlation factor of about 0.5 with the tendon’s natural frequency when a human subject is in a state of laxity. However, we do not know whether the above finding can be applied to actual rehabilitation because muscles and tendons are sometimes in tonus during rehabilitation, a state that varies the natural frequency. In this study, we investigate the correlation between the optimal and natural frequencies of tendon by systematically changing their tension to clarify the effects on the illusion induced by the muscle and the tendon when they are in tonus. We identified a negative correlation between the optimal and natural frequencies when they are in tonus, although a positive correlation appeared when they are in laxity. This result suggests that KI’s optimal frequency should be changed based on the degree of the tendon and muscle tonus. Therefore, our present findings provide a suitable vibration frequency that induces KI due to the degree of the tendon and muscle tonus during robot therapies.

To achieve adaptive gait planning of humanoid robots, a hierarchical central pattern generator (H-CPG) model with a basic rhythmic signal generation layer and a pattern formation layer is proposed to modulate the center of mass (CoM) and the online foot trajectory. The entrainment property of the CPG is exploited for adaptive walking in the absence of a priori knowledge of walking conditions, and the sensory feedback is applied to modulate the generated trajectories online to improve walking adaptability and stability. The developed control strategy is verified using a humanoid robot on sloped terrain and shows good performance.

The article proposes a nonlinear optimal (H-infinity) control approach for a type of underactuated power-line inspection robots. To implement this control scheme, the state-space model of the power-line inspection robots undergoes first approximate linearization around a temporary operating point, through first-order Taylor series expansion and through the computation of the associated Jacobian matrices. To select the feedback gains of the controller an algebraic Riccati equation is solved at each time step of the control method. The global stability properties of the control loop are proven through Lyapunov analysis. The significance of the article’s results is outlined in the following: (i) the proposed control method is suitable for treating underactuated robotic systems and in general nonlinear dynamical systems with control inputs gain matrices which are in a nonquadratic form, (ii) by achieving stabilization of the power-line inspection robots in underactuation conditions the proposed control method ensures the reliable functioning of these robotic systems in the case of actuators’ failures or enables the complete removal of certain actuators and the reduction of the weight of these robotic systems, (iii) the proposed control method offers a solution to the nonlinear optimal control problem which is of proven global stability while also remaining computationally tractable, (iv) the proposed nonlinear optimal control method retains the advantages of linear optimal control that is fast and accurate tracking of reference setpoints under moderate variations of the control inputs, and (v) by minimizing the amount of energy that is dispersed by the actuators of the power-line inspection robots the proposed control method improves the autonomy and operational capacity of such robotic systems.

The conventional product of exponentials $\left(\rm POE\right)$ -based methods dissatisfy the parametric minimality for the kinematic calibration of serial robots due to overlooking the magnitude and pitch constraints. Thus, the minimal kinematic model is presented to solve this problem, which can be developed further. This paper puts forward an improved algorithm for the minimal parameter calibration. An actual kinematic model with the minimal parameters $\left(\rm MP\right)$ is constructed according to the geometric properties of actual joint twists in the auxiliary frames established on the basis of the nominal joint axes. Then, the initial pose error is defined in the tool coordinate frame, which is expressed as the exponential map of the twist, and all twist descriptions are unified, so as to give a unified kinematic model in mathematics. By differentiating the kinematic model, a minimal error model is derived in explicit form. Subsequently, we propose a novel parameter identification method, which identifies the orientation error and position error parameters separately by the iterative least-squares method and updates the MP uniformly. Finally, the simulations and experiments on the different serial robots are conducted to verify the correctness and effectiveness of the proposed algorithm. The simulation results show our calibration algorithm outperforms the existing ones in the accuracy aspect, and the experiment result shows that the absolute pose accuracy of the UR5 industrial robot is upgraded about 9 times under a statistics sense after the calibration.

Multi-legged robots with rigid-flexible coupling grippers have appealing applications to asteroid exploration with the microgravity. However, these robots usually have significantly complicated structures, which leads to a great challenge for the kinematic design.This paper proposes the kinematic design method for a novel multi-legged robot with the microspine gripper. First, the structure of the multi-legged asteroid exploration robot and the microspine gripper are demonstrated. Second, four performance evaluation indices, which are used to evaluate the stiffness, velocity, motion / force transfer efficiency and gripper attachment efficiency of the robot, are derived from the kinematic model. Non-dimensional design spaces of parameters to be optimized are drawn, and performance atlases are presented in design spaces. Third, the stiffness model of the microspine is derived. In addition, the constraint condition of the restoring spring is established, and the stiffness of restoring springs are optimized using the genetic algorithm. Several experiments are conducted to verify the stiffness model of the microspine. Finally, the prototype is developed and the experimental results validates the kinematic design method.

Repetitive motion planning (RMP) is important in operating redundant robotic manipulators. In this paper, a new RMP scheme that is based on the pseudoinverse formulation is proposed for redundant robotic manipulators. Such a scheme is derived from the discretization of an existing RMP scheme by utilizing the difference formula. Then, theoretical analysis and results are presented to show the characteristic of the proposed RMP scheme. That is, this scheme possesses the characteristic of cube pattern in the end-effector planning precision. The proposed RMP scheme is further extended and studied for redundant robotic manipulators under joint constraint. Based on a four-link robotic manipulator, simulation results substantiate the effectiveness and superiority of the proposed RMP scheme and its extended one.

This paper proposes a vision-based kinematic analysis and kinematic parameters identification of the proposed architecture, designed to perform the object catching in the real-time scenario. For performing the inverse kinematics, precise estimation of the link lengths and other parameters needs to be present. Kinematic identification of Delta based upon Model10 implicit model with ten parameters using the iterative least square method is implemented. The loop closure implicit equations have been modelled. In this paper, a vision-based kinematic analysis of the Delta robots to do the catching is discussed. A predefined library of ArUco is used to get a unique solution of the kinematics of the moving platform with respect to the fixed base. The re-projection error while doing the calibration in the vision sensor module is 0.10 pixels. Proposed architecture interfaced with the hardware using the PID controller. Encoders are quadrature and have a resolution of 0.15 degrees embedded in the experimental setup to make the system closed-loop (acting as feedback unit).

This paper proposes a novel multi-step broadcast control (MBC) scheme to deploy a group of autonomous mobile agents for accomplishing coverage tasks in a bounded region. Traditional broadcast control (BC) schemes use a one-to-all communication framework to transmit a uniform signal to all agents, making it cost-effective compared with any all-to-all communication-based scheme for a multi-agent system. However, as BC schemes are based on a single-step view of the environment for decision-making, the environment’s varying distribution density is not known immediately to the agents, resulting in suboptimal performance. To overcome this drawback, this paper proposes an MBC scheme, where agents use a predictive multi-step view and are able to detect the varying densities in the environment ahead of time. The local controller output is estimated using a weighted averaging technique which assigns a higher weight to immediate steps; this feature compensates for any decrease in prediction accuracy as the number of steps increases. We demonstrate the effectiveness of the proposed MBC scheme using a coverage task over a region with uneven population density. Compared to existing BC schemes, the proposed MBC scheme shows superior convergence characteristics in task accomplishment and deployment efficiency.

Over the years, face recognition has been the research topic that has attracted many researchers around the world. One of the most significant applications of face recognition is the access control system. The access control system allows authorized persons to enter or exit certain or restricted areas. As a result, it will increase the security situation without over-investment in staff security. The access information can be the identification, time, and location, etc. It can be used to carry out human resource management tasks such as attendance and inspection of employees in a more fair and transparent manner. Although face recognition has been widely used in access control systems because of its better accuracy and convenience without requiring too much user cooperation, the 2D-based face recognition systems also retain many limitations due to the variations in pose and illumination. By analyzing facial geometries, 3D facial recognition systems can theoretically overcome the disadvantages of prior 2D methods and improve robustness in different working conditions. In this paper, we propose the 3D facial recognition algorithm for use in an access control system. The proposed algorithm includes the preprocessing, feature extraction, and classification stages. The application of the proposed access control system is the automatic sliding door, the controller of the system, the web-based monitoring, control, and storage of data.

We establish a highly feasible algorithm for time-optimal cornering trajectory planning (TP) for car-like mobile robots (CLMRs) based on a dynamic model that contains actuator dynamics. First, we formulate an accurate dynamic model of a robot that contains DC motor actuators; this includes steering braking (caused by the lateral force of the front steering wheel) and two types of friction (viscous and Coulomb) under a nonslip condition. Our TP algorithm can utilize the full power of the DC motor actuators within proper pulse width modulation bounds and generated torque limits. Then, we establish an algorithm for a time-optimal cornering trajectory planning for CLMRs (TOCTP-CLMR). Our algorithm divides the trajectory into five sections comprising three turnings and two translations to minimize the travel distance. Then, we utilize the quickest rotation when turning to construct the time-optimal trajectory that satisfies the bang-bang principle. In addition, simulations are performed to demonstrate the validity of this method. Finally, we conduct open-loop experiments to validate our dynamic model and a trajectory tracking experiment to demonstrate the feasibility of the TOCTP-CLMR trajectory.

By recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. To improve the accuracy of motion intention recognition based on sensor data, the research based on deep learning was carried out. Eighty healthy subjects performed gait experiments under five different gait environments (flat ground, 10 ${}^\circ$ upslope and downslope, and upstairs and downstairs) by simulating stroke patients. To facilitate the training and classification of the neural network, this paper presents template processing schemes to adapt to different data formats. The novel algorithm model of a hybrid network model based on convolutional neural network (CNN) and Long–short-term memory (LSTM) model is constructed. To mitigate the data-sparse problem, a spatial–temporal-embedded LSTM model (SQLSTM) combining spatial–temporal influence with the LSTM model is proposed. The proposed CNN-SQLSTM model is evaluated on a real trajectory dataset, and the results demonstrate the effectiveness of the proposed model. The proposed method will be used to guide the control strategy design of robot system for active rehabilitation training.

This study focuses on development, task planning, and dynamic analysis of a previously proposed spherical parallel robot manipulator that is conceptually enhanced to adapt various brain surgery scenarios. Conceptual design of the proposed manipulator was briefly introduced and explained. In order to simulate one of the possible surgery scenarios, a case study of craniotomy was designed along with its trajectory planning. Dynamic analysis of proposed manipulator was performed by Lagrange method, and required actuator torque values were calculated for the desired trajectory. At the end of the study, hardware verification was carried out on the manufactured prototype of the system by comparing both calculated/acquired torque values and desired/actual trajectories. Promising verification results in terms of system dynamics and trajectory execution were introduced.

This article presents a methodology to reduce the energy consumption of an industrial robot. We propose a design for a 3R serial manipulator of general geometry. We show an analytical model aiming to analyze the search space of architectures based on the torsion angles of the robot to determine the optimal architecture that allows the efficient use of energy. The analytical model provides a theoretical estimation of the energy consumption and is validated by monitoring the experimental robot. The numerical calculations obtained with a particular case reduced the energy consumption by approximately 7.5%.

Every year the shortage of biosamples is increasing, while the requirements for their quality are constantly tightening, which requires the introduction of new technological solutions. To solve these problems, a robotic system for aliquoting biological liquid was developed. The aliquotation process is described. The station includes a serial robot on which a gripper based on a globoid worm is installed. The gripping device is parameterized and takes into account the gripping force for different finger deflections. 3D models were developed using Computer Aided Design (CAD) system tools, after which working layouts were created using 3D printing. The design process and test results are discussed to show the efficiency of the built prototype with lab tests.

At present, the study on autonomous unmanned ground vehicle navigation in an unstructured environment is still facing great challenges and is of great significance in scenarios where search and rescue robots, planetary exploration robots, and agricultural robots are needed. In this paper, we proposed an autonomous navigation method for unstructured environments based on terrain constraints. Efficient path search and trajectory optimization on octree map are proposed to generate trajectories, which can effectively avoid various obstacles in off-road environments, such as dynamic obstacles and negative obstacles, to reach the specified destination. We have conducted empirical experiments in both simulated and real environments, and the results show that our approach achieved superior performance in dynamic obstacle avoidance tasks and mapless navigation tasks compared to the traditional 2-dimensional or 2.5-dimensional navigation methods.

In this paper, the assembly problem of circular-rectangular compound peg and hole parts in automatic assembly is proposed for the first time, and an automatic assembly method based on six-dimensional force sensor is summarized. Firstly, according to the quasi-static equilibrium condition, the contact states are summarized. It is concluded that there are 7 categories of 44 contact states in insertion stage and the forces conditions to maintain each contact state; Secondly, according to the analysis of the force/moment relationship, the jamming diagram is drawn, and the conditions of jamming are analyzed. Thirdly, the assembly strategy is discussed, including contact state recognition strategy, hole searching strategy, and pose adjustment strategy. Finally, combined with impedance control, the assembly theory is verified in ADAMS, and the assembly with minimum clearance of 0.1 mm is achieved.

Currently, the body balance control algorithm of a quadruped robot executing trot gait motion is more complex and computationally intensive, which is not conducive to improving the real-time control performance of the robot. This paper proposes a six-dimensional spatial mechanics decoupling algorithm to enhance the balance control accuracy during trot gait while optimizing the computational effort. A 6 × 6 matrix is established to describe the relationship between six ground reaction forces of the diagonal supporting leg and six spatial forces and torques controlling robot degrees of freedom, which is optimized to reach the full rank. Decoupling calculation is adopted to obtain required ground reaction forces by matrix inverse operation, and forces are converted to joint motor torques utilizing the Jacobian matrix. The trajectory of the swing leg foot is generated based on cubic interpolation, and the robot motion speed is adjusted by selecting the landing point. This paper also proposes a region intervention control method based on center of mass projection to regulate the moving speed while ensuring the balance of the robot. Finally, the algorithm is verified by simulation using open source software Webots. The results show that when the robot moves at an average speed of 0.5 m/s, the lateral displacement change of the robot is less than 0.009 m, the height change is less than 0.003 m, and the rotating angles around the x, y, and z axes are less than 0.0036 rad, 0.0013 rad, and 0.001 rad, respectively.

Deploying quadcopters for aerial transportation can be cost effective in impromptu material handling applications. However, such applications are limited mainly due to the requirement of onboard localization sensors and associated computation. The current work presents a human-controlled modality to successfully execute spontaneous outdoor flight of a quadcopter with a cable-suspended payload. Stable and smooth flights are achieved through an onboard integration of a custom-built sensor system and a controller to minimize payload oscillations. The feasibility of the proposed modality is demonstrated by conducting outdoor experiments and a case study in an unstructured environment.

For diagnostic and therapeutic applications in spacious spots of the gastrointestinal (GI) tract, the single rigid body capsule clinically applied is difficult to realize the fix-point posture adjustment function manipulated by the external permanent magnet system using the static balance control because the posture alignment and the locomotion interfere with each other. To realize this function easily, the dual hemisphere capsule robot (DHCR) is proposed, based on tracking effect—the axis of DHCR keeps tracking the normal orientation of the spatial universal rotating magnetic vector (SURMV). Since tracking effect employs dynamic balance control, dynamic stability of the DHCR system affects posture alignment performance. This paper focuses on posture alignment dynamic modeling and the influence of the magnetic flux density and the angular velocity of the SURMV, along with the damping coefficient of the GI tract surface on stability, obtaining the stability domains of parameters. Furthermore, to reduce error due to the uncertainties in complex GI tract environment, the sliding mode controller based on nominal model is proposed to achieve more accurate dynamic tracking, and Lyapunov theorem is employed to assess stability of controller. Finally, the tracking effect is verified through simulations and experiments, indicating that the fix-point posture adjustment can be realized with higher accuracy and efficiency.

This paper presents a comparative study of two translational parallel manipulators (TPMs) with three Degrees of Freedom (3-DOF) based on the orientation errors due to joint clearances. In fact, the kinematic and static models of the manipulators have been used to determine the analytical model of the orientation errors. Then, the maximum and the sensitivity of the orientation errors have been considered as criteria to compare the precision of the DELTA and the RAF manipulators. The maximum orientation error is determined by solving an optimization problem of the previous analytical model. The sensitivity of the orientation errors is divided into two types: one related to the sensitivity of the orientation errors to the geometric parameters and another one related to the sensitivity of the orientation error to the joint clearances. The results show that the RAF robot precision is more sensitive to the joint clearances than the DELTA one. However, this advantage of the DELTA is limited to a portion of the workspace, which is free from singular configurations.