This research presents an upper limb cable-driven rehabilitating robot with one degree of redundancy to improve the movements of the injured. A spatial trajectory is planned through the joint limit avoidance approach to apply the limits of the joint angles, which is a new method for trajectory planning of joints with an allowed definite interval. Firstly, a Lyapunov-based control is applied to the robot with taking uncertainty and disturbances into consideration. To derive the best responses of the system with considering uncertainty and disturbances, a novel robust tracking controller, namely a computed-torque-like with independent-joint compensation, is introduced. The mentioned new robust controller has not been applied to any cable robot which is the novelty of this paper to derive a superior output and the robustness of the given approach. Stability analysis of both controllers is demonstrated and the outputs of the controllers are compared for an exact three-dimensional motion planning and desirable cable forces. Eventually, the proposed novel controller revealed a better function in the presence of uncertainties and disturbances with about 28.21% improvement in tracking errors and 69.22% improvement in the required cable forces as control inputs, which is a considerable figure.

This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system’s dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system’s dynamics is learned well.

As an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots’ joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity.

This article proposes a robust and adaptive controller for industrial robot arms with multiple degrees of freedom without the need for velocity measurement. Many of the controllers designed for manipulators are model-based and require detailed knowledge of the system model. In contrast to these methods, this paper proposes a model-free controller using the Philips q-Bernstein operator as universal approximator. The designed controller can approximate uncertainties including external disturbances and unmodeled dynamics based on its universal approximation capability. Besides, most of the controllers revealed for robot arms are torque-based, which is not a realistic presumption from a practical point of view. In the proposed control method, the voltage applied to the actuator is considered as the control signal. However, unlike many voltage-based methods, the need to know the exact models of the system and the actuator has been eliminated in the presented method. Also, adaptive rules are extracted during the Lyapunov analysis to ensure system stability. Finally, to analyze the performance of the presented controller, this method is simulated for an industrial robot arm, and the results are analyzed. The proposed methodology is also compared to those of a strong state-of-the-art approximator, the Chebyshev neural network.

With many micromanipulator designs emerging in micro and nanosystem applications, the element of compliance in the mechanisms is gaining attention. Several designs consider motions limited in a plane for high accuracy and repeatability as needed in micro/nano manipulation applications. Extending this to a full spatial configuration with coupled motions of series and parallel linkages with flexure joints of 1-degree-of-freedom (DOF) and 3-DOF needs a systematic analytical approach. One such approach for compliance analysis is presented in this article for a mechanism designed at Indian Institute of Technology Kharagpur. To validate the analytical models, finite element analysis simulations are performed with the help of the Abaqus-6.14 software package. Following the successful validation, the effect of structural parameters on the performance is presented with the help of the analytical expressions. We explore the performance of the mechanism with different dimensions of flexures of a particular type. Results indicate that the design with dissimilar dimensional parameters can give superior performance.

The realizing of variable output constant force has received wide attention. To achieve a force regulation in an economic way, a configuration of the constant force mechanism (CFM) referring to positive and negative stiffness combination method is proposed in this paper. By adjusting preloading displacement applied on positive-stiffness structure of the CFM, the variable constant force output can be realized. The force–displacement expression of the CFM in the non-preloaded condition is deduced by the established analytical models. Furthermore, parametric sensitivity analysis with several architectural parameters are conducted for optimizing physical structures. Finally, the correctness of the proposed principle is verified by experimental studies. The observed experimental results show that the CFM under different preloading displacements can provide required output constant force, which is consistent with proposed hypothesis.

During the movement of an optical mirror processing robot (OMPR), the movement error of each branch chain leads to contour errors of the grinding tool, which reduce the accuracy of the optical mirror surface. To improve the processing accuracy of an OMPR, it is necessary to study the control and compensation strategy of its contour error. In this study, first, a kinematics analysis of an OMPR is conducted, and the trajectory of the end execution point in the world coordinate system is transformed into the fixed coordinate system of the robot. Combined with the common trajectory of optical mirror processing, based on the Frenet coordinate system, contour error models of the OMPR in straight line, arc, and spiral trajectories are established. Subsequently, the contour error, feedforward channel gain, and compensation channel gain models of the parallel module are established in the task space, and concurrently, the control variables and stability of the system are analyzed. Finally, the established feedforward combined multi-axis cross-coupling contour control compensation strategy is analyzed experimentally to verify its accuracy and effectiveness. It provides a theoretical basis for a robot to directly face the precision processing object using the control and compensation strategy in a future research study to improve the molding accuracy of a surface and optimize the processing technology of a large-scale optical mirror.

With the increasing demand for rehabilitation and the lack of professional rehabilitation personnel, robot-assisted rehabilitation technology plays an increasingly important role in neurological rehabilitation. In order to recover the exercise ability of patients with waist injury, a new type of wire-driven waist rehabilitation training parallel robot (WWRTPR) is designed. According to the motion trajectory planning of waist rehabilitation training, two coordinate systems are established: moving coordinate system and static coordinate system. The inverse kinematics modeling analysis is carried out, and the dynamic model of the robot is established by using Newton–Euler method. An intelligent control method of force/position hybrid control based on radial basis function neural network is proposed. The stability of the closed-loop system is analyzed, and the results show that WWRTPR tends to be stable. The simulation analysis of rehabilitation training on WWRTPR is carried out, and the simulation results show that the proposed intelligent control method can effectively control the robot system, which provides a reference for the development of a flexible intelligent rehabilitation training robot.

With diverse areas of applications, wearable robotic exoskeleton devices have gained attention in the past decade. These devices cover one or more human limbs/joints and have been presented for rehabilitation, strength augmentation and interaction with virtual reality. This research is focused towards design, modeling and control of a novel series elastic actuation (SEA) based index finger exoskeleton with a targeted torque rendering capability of 0.3 Nm and a force control bandwidth of 3 Hz. The proposed design preserves the natural range of motion of the finger by incorporating five passive and two actively actuated joints and provides active control of metacarpophalangeal and proximal interphalangeal joints. Forward and inverse kinematics for both position and velocity have been solved using closed loop vector analysis by including human finger as an integral part of the system. For accurate force control, a cascaded control structure has been presented. Force controlled trajectories have been proposed to guide the finger along preprogrammed virtual paths. Such trajectories serve to gently guide the finger towards the correct rehabilitation protocol, thus acting as an effective replacement of intervention by a human therapist. Extensive computer simulations have been performed before fabricating a prototype and performing experimental validation. Results show accurate modeling and control of the proposed design.

This paper proposes an iterative algorithm to solve the inverse displacement for a hyper-redundant elephant’s trunk robot (HRETR). In this algorithm, each parallel module is regarded as a geometric line segment and point model. According to the forward approximation and inverse pose adjustment principles, the iteration process can be divided into forward and backward iteration. This iterative algorithm transforms the inverse displacement problem of the HRETR into the parallel module’s inverse displacement problem. Considering the mechanical joint constraints, multiple iterations are carried out to ensure that the robot satisfies the required position error. Simulation results show that the algorithm is effective in solving the inverse displacement problem of HRETR.

The use of swimming robots has increased widely in recent years due to the need of using them in situations where human intervention is difficult or not allowed. Exploring depths of seas, military interventions, or entering areas where the amount of water pollution is high that may threaten the lives of divers. In such cases, the best alternative for humans is to use swimming robots. This paper presents a swimming robot based on Labriform swimming mode. First, it starts with an analytical study of the effect of the fins shape on the performance of a robotic fish. The suggested design of the pectoral fins is concave. The effect of such a design would help largely in achieving the highest thrust in comparison to flat designs provided in the literature. Secondly, a variation in the velocity between the power and recovery strokes is accomplished and a maximum thrust can be obtained when the velocity of the power stroke is three times the velocity of recovery stroke. Thirdly, the kinematics and dynamics of the swimming robot are derived and an evaluation of the total hydrodynamic forces that are exerted on the robot’s body is studied via the computational fluid dynamics method from SOLIDWORKS® platform. Finally, the obtained results are compared to other designs in the literature in terms of some dimensionless numbers of biological fish to examine the efficiency. The proposed design has been validated theoretically and examined experimentally. The results of the simulation and practical experiments confirmed the validity of the design.

Electromagnetic actuation systems (EMA) have excelled themselves in microscale manipulation. Yet, the fastened structure of the current systems tethers the controlled workspace. In this paper, a new electromagnetic actuation principle is investigated. The actuator structure consists of a pair of coaxially movable electromagnets integrated to a robotic manipulator. The pair induces a coaxial homogeneous magnetic field or gradient to control the magnitude of the magnetic torque or force by changing the distance between the electromagnets asymmetrically. The robotic manipulator, on the other hand, transports the pair at five degrees of freedom to manipulate a microrobot in 3D space by closed-loop control with integrated vision feedback system. Numerical analyses are performed to investigate the induced electromagnetic field at the symmetrical/asymmetrical configuration of the coaxial pair. Accordingly, a correlation between the magnitude of the magnetic force and the asymmetric distance is obtained for flexible force control. A proof of concept prototype is constructed to validate the proposed actuation principle and evaluate its performance experimentally. The experimental results verify the numerical analysis and show the system applicability of inducing controlled forces on a micro-object in 2D and 3D workspaces at a velocity range of 65 to 157 $\mu$ m/s. Moreover, micromanipulation on a helical route is also demonstrated with an absolute error mean from the reference path of 191 $\mu$ m.

This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.

Parallel manipulators are increasingly utilized in extensive industrial applications due to their high accuracy, compact structure, and significant stiffness characteristics. However, most of the time, massive actuators are involved in constructing and controlling a parallel manipulator, which burdens the structure design and controller development. In this paper, a novel underactuated positioning system been built by different sets of linear motion units (defined as the positioning lines) is proposed, enabling to actuate the multiple degree-of-freedom manipulators with one motor. To achieve this, a smart shape memory alloy (SMA) clutch is presented to obtain the positioning function of each positioning line. Further, to get the decoupled motion regulation of the positioning lines, a new thermal kinematic model of the SMA clutch, which considers the heat dissipation influence on the metal components, was built and validated by the physical prototypes. The experimental results show that the constitutive model of the SMA clutch developed in this paper can be validated within the error of 5.3%. It can also be found that the heat dissipation of the metal component has a significant influence on the model accuracy of the SMA clutch (i.e., 2.6% of the model accuracy). The experiments on the underactuated positioning system produce the following results: the single positioning line can achieve high positioning (i.e., average error: 1.01%) and tracking (i.e., average error $\leq$ 1 mm) abilities; the underactuated positioning system can perform decoupled motions in the three positioning lines with high accuracy (i.e., ±2 mm within the stroke of 180 mm).

This paper investigates the trajectory tracking problem of uncertain robot manipulators with input saturation and output constraints. Uncertainty and disturbance estimator (UDE) is used to tackle the model uncertainties and external disturbances. Different from most existing methods, UDE only needs the bandwidth of the unknown plant model for design, which makes it easy to be implemented. Nonlinear state-dependent function is employed to cope with output constraints and a second order auxiliary system is constructed to solve the input saturation. Finally, an UDE-based tracking controller is proposed based on the backstepping method. With the proposed control scheme, the input saturation and the output constraints are not violated, and all signals in the closed-loop system are bounded. The comparative simulation results of a two-link robot manipulator are utilized to validate the effectiveness and superiority of the proposed control method.

As one of the research hotspots in the field of rehabilitation robotics, the upper limb exoskeleton robot has been widely used in the field of rehabilitation. However, the existing methods cannot comprehensively and accurately reflect the motion state of patients, which may lead to overtraining and secondary injury of patients in the process of rehabilitation training. In this paper, an upper limb exoskeleton control method based on mixed perception model of motion intention and intensity is proposed, which is based on the 6 degree-of-freedom upper limb rehabilitation exoskeleton in the laboratory. First, the kinematic information and heart rate information in the rehabilitation process of patients are collected, corresponding to patients’ motion intention and motion intensity, and fused to obtain the mixed perception vector. Second, the motion perception model based on long short-term memory neural network is established to realize the prediction of upper limb motion trajectory of patients and compared with back-propagation neural network to prove its effectiveness. Finally, the control system is built, and both offline and online test of the control method proposed are implemented. The experimental results show that the method can achieve comprehensive motion state perception of patients, realize real-time and accurate prediction trajectory according to human motion intention and intensity. The average prediction accuracy is 95.3%, and predicted joint angle error is less than 5 degrees. Therefore, the control method based on mixed perception model has good robustness and universality, which provides a new method for the active control of upper limb exoskeleton.

Climbing robots that use bionic claws can climb vertical or even inverted rough surfaces. However, wall-climbing robots with unidirectional spiny feet cannot crawl horizontally or downward on vertical rough surfaces. In this paper, a pair of gripping spiny feet is used to give a robot the capacity to crawl in any direction on a rough wall. On the basis of observations of the gecko’s method for grasping onto a vertical rough surface, a multilevel interlocking structure is proposed. A spherical contact model of the claw tip on a vertical rough surface is established, and the influences of the contact angle, friction coefficient, and other factors on the grappling claw action are analyzed. Moreover, the optimal structure of the grappling claws is proposed. The force during the grasping and detachment of the mechanism and the influence of the number of feet on grasping performance are determined through experiments. Furthermore, a six-legged wall-climbing robot is designed and evaluated in terms of crawling on a vertical rough surface at various angles. The feasibility of using an opposed gripping mechanism to allow a robot to crawl in any direction on a vertical rough surface is also verified.

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.

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 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.