When a manipulator captures its target by a visual servo system, uncertainties can arise because of mechanical system and visual sensors exist error. This paper proposes an intelligent method to predict the successful rate for a manipulator to capture its target with motion and sensor errors. Because the mapping between the joint space of the manipulator and the Cartesian space at the end of the manipulator is nonlinear, when there is a bounded error of the manipulator’s joint, the error range of the end motion is constantly changing with the different joint positions. And at the same time, the visual servo camera will also measure the target from different positions and postures, so as to produce measurement results with different error ranges. The unknown time-varying error property not only greatly affects the stability of the closed-loop control but also causes the capture failure. The purpose of this paper is to estimate the success probability of different capture trajectories by establishing the nondeterministic model of manipulator control system. First, a system model including motion subsystem and feedback subsystem was established with system error described by Gaussian probability. And then Bayesian estimation was introduced into the system model to estimate the executing state of the predefined trajectory. Linear least quadratic regulators (LQR) control is used to simulate the input correction in the closed-loop control between motion subsystem and feedback subsystem. At last, the successful probability of capturing the target is established by the Gaussian distribution at the end point of the trajectory with geometric relationship calculation between tolerance range and error distribution. The effectiveness and practicability of the proposed method are proved by simulation and experiment.

This paper proposes an aerodynamic analysis of the shuttlecock and a novel method for predicting shuttlecock trajectory. First, we have established a shuttlecock track data set by an infrared-based binocular vision system. Then the unscented Kalman filter algorithm is designed to further filter the noise and visual recognition algorithm errors. Third, the radial basis function (RBF)-based track prediction model is designed. This method offers a concept to obtain the neural network model of different kinds of flying or moving objects. The experimental results show that the proposed method can predict the shuttlecock trajectory in real time at high accuracy and can be used for implementing the algorithm of return strategies in the near future.

Success of any needle-based medical procedures depends on accurate placement of the needle at the target location. However, accurate targeting and control of flexible self-actuating (active) needle are challenging. We have developed a shape memory alloy-actuated flexible needle steered by a 3D Cartesian robot and performed a comparative study of four, non-model-based, coordinated control methodologies for the combined robot steering and flexible-needle insertion process for surgical interventions. Investigated four controllers are: proportional–integral–derivative (PID), PID with the cubic of positional error term (PID-P3), static PID sliding mode controller, and robust adaptive PID sliding mode controller (RAPID-SMC). Relative efficacies of these controllers are demonstrated by performing experiements using a tissue-mimicking soft material phantom. Results from experiments have reavealed that RAPID-SMC is superior to other three controllers.

A kinetostatic approach applied to the design of a backflip strategy for quadruped robots is proposed in this paper. Inspired by legged animals and taking the advantage of the leg workspace, this strategy provides an optimal design idea for the low-cost quadruped robots to achieve self-recovery after overturning. Through kinetostatic and energy analysis, a four-stepped backflip strategy based on the selected rotation axis with minimum energy is proposed, with a process of selection, lifting, rotating, and protection. The kinematic factors that affect the backflip are investigated, along with the relationship between the design parameters of the leg and trunk being analyzed. At the end of this paper, the strategy is validated by a simulation and experiments with a prototype called DRbot, demonstrating that the strategy endows the robot a strong self-recovery ability in various terrains.

This article addresses the problem of pose and force control in a cooperative system comprised of multiple n-degree-of-freedom (n-DOF) electrically driven robotic arms that move a payload. The proposed controller should be capable of maintaining the position and orientation of the payload in the desired path. In addition, the force exerted by robot end effectors on the object must remain limited. The system has unmodeled dynamics, and measuring the robot joint velocities is impossible. Therefore, a FAT-based observer–controller is designed to estimate the uncertainty and velocities based on universal approximation property of Fourier series expansion. The stability of the system is confirmed based on Lyapunov’s stability theorem. Finally, the proposed adaptive controller–observer is applied on two 3-DOF cooperative robotic arms carrying a payload, and the results are precisely analyzed. The results of the proposed approach are also compared with two state-of-art powerful approximation method.

This study presents an under-actuated snake arm maintainer (SAM) for complex and extreme environments such as nuclear power plants. The structure adopts the layered cable drive principle, whereby a single drive layer drives multiple joints. This design significantly reduces the complexity of the control system while increasing the spatial curvature. The traction of multiple wire ropes with a composite capstan drives the synchronous angular motion of several adjacent joints. By changing the number of joints in the single driver layer of the snake arm, the arm can be adapted to various complex environments. The trajectory planning and trajectory tracking motion control methods of the under-actuated SAM are established based on the improved backbone method and the variable rod length algorithm. Finally, a 10-joint prototype with an arm length of 2300 mm is designed for nuclear reactor maintenance. Trajectory experiments confirmed the rationality of the under-actuated SAM, the correctness of the inverse kinematics, and the effectiveness of the motion control methods.

Overconstrained manipulators in lower subspaces with unique motions can be created and analyzed. However, far too little attention has been paid to creating a generic method for overconstrained manipulators kinematic analysis. This study aimed to evaluate a generic methodology for kinematic analysis of overconstrained parallel manipulators with partial subspaces (OPM-PS) using decomposition to parallel manipulators (PMs) in lower subspaces. The theoretical dimensions of the method are depicted, and the use of partial subspace for overconstrained manipulators is portrayed. The methodology for the decomposition method is described and exemplified by designing and evaluating the method to two overconstrained manipulators with 5 degrees of freedom (DoF) and 3 DoF. The inverse kinematic analysis is detailed with position analysis and Jacobian along with the inverse velocity analysis. The workspace analysis for the manipulators using the methodology is elaborated with numerical results. The results of the study show that OPM-PS can be decomposed into PMs with lower subspace numbers. As imaginary joints are being utilized in the proposed methodology, it will create additional data to consider in the design process of the manipulators. Thus, it becomes more beneficial in design scenarios that include workspace as an objective.

Bipedal robots by their nature show both hybrid and underactuated system features which are not stable and controllable at every point of joint space. They are only controllable on certain fixed equilibrium points and some trajectories that are periodically stable between these points. Therefore, it is crucial to determine the trajectory in the control of walking robots. However, trajectory optimization causes a heavy computational load. Conventional methods to reduce the computational load weaken the optimization accuracy. As a solution, a variable time interval trajectory optimization method is proposed. In this study, optimization accuracy can be increased without additional computational time. Moreover, a five-link planar biped walking robot is designed, produced, and the dynamic walking is controlled with the proposed method. Finally, cost of transport (CoT) values are calculated and compared with other methods in the literature to reveal the contribution of the study. According to comparisons, the proposed method increases the optimization accuracy and decreases the CoT value.

This paper presents a numerical and experimental validation of ExoFing, a two-degrees-of-freedom finger mechanism exoskeleton. The main functionalities of this device are investigated by focusing on its kinematic model and by computing its main operation characteristics via numerical simulations. Experimental tests are designed and carried out for validating both the engineering feasibility and effectiveness of the ExoFing system aiming at achieving a human index finger motion assistance with cost-oriented and user-friendly features.

A mathematical model is proposed for a revolute joint mechanism with an n-degree of freedom (DOF). The matrix approach is used for finding the relation between two consecutive links to determine desired link parameters such as position, velocity and acceleration using the forward kinematic approach. The matrix approach was confirmed for a proposed 10 DOF revolute type (R-type) human upper limb model with servo motors at each joint. Two DOFs are considered each at shoulder, elbow and wrist joint, followed by four DOF for the fingers. Two DOFs were considered for metacarpophalangeal (mcp) and one DOF each for proximal interphalangeal (pip) and distal interphalangeal (dip) joints. MATLAB script function was used to evaluate the mathematical model for determining kinematic parameters for all the proposed human upper limb model joints. The simplified method for kinematic analysis proposed in this paper will further simplify the dynamic modeling of any mechanism for determining joint torques and hence, easy to design control system for joint movements.

In order to improve the speed of motion planning, this paper proposes an improved RRTConnect algorithm (SDPS-RRTConnect) based on sparse dead point saved strategy. Combining sparse expansion strategy and dead point saved strategy, the algorithm can reduce the number of collision detection, fast convergence, avoid falling into local minimum, and ensure the completeness of search space. The algorithm is simulated in different environments. The results show that in complex environments, the sparse dead point saved strategy plays a good effect. In simple environments, the greedy connection strategy works well. Compared with the standard RRT algorithm, the standard RRTConnect algorithm, and the SDPS-RRT algorithm, the SDPS-RRTConnect algorithm has the shortest planning time, and it is suitable for both simple and complex environments. The 500 experiments show that the algorithm has strong robustness. The actual robot experiments show that the path planned by SDPS-RRTConnect algorithm is effective.

This article proposes an observer design for two important variables in the studies of single-leg hopping robot (SLHR), the apex height, and the vertical velocity of SLHR during its stance phase. At first, the Euler–Lagrange (EL) dynamics of SLHR are obtained and apex height is identified in the state-space representation of the EL dynamics. Apex height is the state variable that represents the robot body’s height at the top point, which keeps on changing as the robot functions. Vertical velocity is the velocity of the robot in the vertical direction. An observer design is presented in this article which will estimate these variables when required. The quality of the estimation is validated by the simulation results where the estimation error is zero which means the model output is correct and observer performance is good.

An impedance matching control framework between a human and a haptic joystick for long-term teleoperation is proposed in this research. An impedance model of the human arm is established analyzing the characteristics of human perception, decision, and action. The coefficients of the human arm’s impedance have been identified using a least squares method. The human arm’s impedance matching algorithm generates a corresponding motion vector for the human arm, which is determined by the interaction force measured by a force/torque sensor considering the impedance modeling of the human arm. The impedance control has been adopted for the haptic joystick to match the desired impedance to that of the human arm, which is aimed to minimize the energy consumption of the human arm for long-term teleoperation. By minimizing the fatigue of the operator, the remote control accuracy of the teleoperation can be improved. A PD control with gravity compensation algorithm has been adopted to maintain desired trajectory for the joystick by the operator more conveniently. The effectiveness of matching control has been demonstrated by trajectory following experiments for a mobile robot.

The present research aims to model, simulate and implement a new hybrid control approach based on a combination of proportional integral derivative (PID) Controller and Model Reference Adaptive Controller (MRAC), in which Lyapunov’s theory is used to ensure asymptotic stability to control a two degrees of freedom (DoF) manipulator driven by McKibben’s artificial pneumatic muscles. The MRAC controller works as a nonlinearity compensator and PID controller works during the transient period, as the MRAC performs poorly in this regime. This new approach is entitled Hybrid Model Reference Adaptive Controller (H-MRAC) and it has an unprecedented topological structure based on three terms. The feedforward term acts in disturbances rejection, the derivative term in oscillations damping and the feedback term acts in error convergence to zero. In this article, a control system dedicated to pneumatic manipulators was developed. As a result, proof of asymptotic convergence was performed for the proposed topological approach, which was validated on a two DoF manipulator. The proposed mechanism satisfactorily met the ISO/TS 15066 standard, and the position tracking obtained a global error of 37.69% and 51.01% smaller than found in the literature examples, entitled MRAC and A-PID, respectively, for simulations and 37.46% and 30.25% for experiments.

This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

A magnetically controlled spiral capsule robot is designed. When the robot is running in a pipe filled with mucus, computational fluid dynamics is used to analyze the fluid field (velocity, streamlines, and vorticity) in the pipe, and particle image velocimetry is used to measure the above fluid field surrounding the robot. The measured fluid field is basically similar to the numerical result. The relationship between the operating parameters of the robot and the performance of the robot is further calculated and analyzed. The results show that the resistance to the robot in the forward direction, average turbulent intensity of the fluid surrounding the robot, and maximum fluid pressure to the pipe wall are proportional to the robotic translational speed. The resisting moment of the robot in the forward direction, average turbulent intensity of the fluid surrounding the robot, and maximum fluid pressure to the pipe wall are proportional to the robotic rotational speed.

In this paper, an improved adaptive motion-force control approach is introduced to control the cooperative manipulators transporting a shared object under limited communication. The adaptive controller is designed based on the backstepping approach to control the motion of the handled object in the presence of uncertainties and external disturbances. Moreover, the force controller is established to maintain constant internal forces. An event-triggered (ET) mechanism is derived based on the Lyapunov analysis to deal with the bandwidth restrictions and maintain the system stability during the cooperative manipulation. The effectiveness of the proposed control scheme is investigated by comparing it with the existing variations of adaptive backstepping control (i.e., traditional and state augmented schemes). Moreover, the designed triggering mechanism is compared with different triggering conditions presented in the literature. The proposed control approach is further validated in a more realistic virtual robot experimentation platform (i.e., V-REP) using two SCORBOT-ER VPlus manipulators. From the TrueTime-based simulation runs, the proposed control scheme exhibits superior performance in tandem with efficient utilization of the network resources during the transportation task.

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.

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 article discusses the intervertebral motion present in the craniovertebral junction (CVJ) region. The CVJ region is bounded by the first three vertebras from the spinal column. It helps in bringing most of the neck motion. Intervention in this region requires surgery in which an implant is placed to stabilize the whole system. The various available implants need to undergo performance evaluation as their performance varies from region and anatomical diversity. For the Indian population, we are targeting to evaluate the performance of such an implant, testing it into a cadaver. The region of interest will be loaded as per the loading condition of an average human. Motion in these regions is evaluated using the camera. A preliminary test was done on a saw bone model of CVJ to assess the performance of segmentation methods. Multiple such ArUco markers are used to increase pose accuracy further, and the pose of the entire board of multiple tags provides us with reliable pose estimation. The absolute error ranged from a minimum of 0.1 mm to a maximum of 16 mm. At the same time, the mean and median absolute errors were 3.8961 mm and 3.35 mm. By considering the absolute lengths, the percentage error showed the following trends. The percentage error was between 3.9168% and 0.0230%.