This paper proposes a modular method based on the kinematics of serial manipulators to synthesize over-constrained mechanisms. Because the PPP manipulator has an unlimited work space, its end-effector can be constrained to trace a trajectory identical to those of another open-chain manipulator, including a P joint single link and an RR dyad. In doing so, two open-chain manipulators can be concatenated to form closed-loop mechanisms, including PPPP, PPPRR, or PPCR mechanisms. To design over-constrained mechanisms efficiently, the Denavit–Hartenberg convention is adopted to describe the PPP manipulator kinematically, and the Euler angles are utilized to derive geometric constraints of synthesized over-constrained mechanisms. Next, kinematic equations of the PPP manipulator can be modularized and applicable to analyze different closed-loop mechanisms. At last, by adjusting link lengths, twisted angles, and joint angles of the synthesized PPPRR and PPCR mechanisms to form other over-constrained mechanisms configurationally. The novelty of this research lies in modularizing the over-constrained mechanism into two movable serial manipulators whose end-effectors share identical trajectory and orientation. Thus, defining geometrical constraints of the over-constrained mechanism can be transformed into finding angular parameters describing the orientation of these two serial manipulators such that the end-effector coordinate system of two manipulators can properly be aligned. Angular parameters of the serial manipulators can be easily determined by means of Euler angles, which yields an advantage of easy calculation since it only involves the computation of Euler angles parameters. The presented method can be extended to the kinematic synthesis and analysis of more spatial closed-loop mechanisms.

Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

This paper presents a study on the effect of design parameters of an underactuated hand on its grasp performance. Three kinds of grasp performance characteristics are considered: grasp range, grasp strength, and immobilizing grasp range. Grasp strength is defined as the stiffness of the grasp. Immobilizing grasps are those in which the object cannot be moved for a force up to a certain threshold. In general, underactuated hands cannot produce immobilizing grasps. However, we show that immobilizing grasps can be created by including joint limits in the hand design. We consider the effect of two design parameters on the grasp performance: torque ratio and finger-base distance. Results show that an optimal finger-base distance and torque ratio exists that maximizes the grasp range and grasp strength. Also, the immobilizing grasp range is increased by decreasing the finger-base distance and increasing the torque ratio and joint limits.

In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

This paper presents an integrated optimal control framework for velocity and steering control of an autonomous pursuit vehicle, where the control objectives satisfy the requirements of collision avoidance and moving target tracking. A distinctive feature of the proposed velocity and steering control is the application of logarithmic penalty functions to both. The control barrier imposed by logarithmic function provides a unique tool in computing a balanced trajectory with optimal tracking error, control effort and safety margin. Trajectories compliant with the safety regulations for autonomous driving have been planned based on estimated intention of the target and the obstacles. Effects of the controller weights have been extensively simulated to assess the performance of the proposed strategy in a variety of dynamic situations. The controller has been validated on a real-life robot by using a shrinking horizon control policy for iterative optimisation.

Robot positioning performance is studied in the scope of a robotized X-ray computed tomography application on a ABB IRB4600 robot. The robot has the “absolute accuracy” option, that is, the manufacturer has identified the manufacturing defects and included them in the robot control. Laser-tracker measurement on a 6.5-h long linear trajectory shows thermal drift and backlash issues, affecting the positioning unidirectional repeatability and bidirectional accuracy. A thermo-geometrical model with backlash compensation is developed. Geometrical calibration improves the forwards unidirectional mean accuracy from 1.39 to 0.06 mm between theoretical and optimized geometrical parameters with a stable thermal state. Thermo-geometrical calibration reduces the positioning scattering from a maximum of 0.15 to 0.05 mm (close to the repeatability of the robot). Backlash compensation improves the bidirectional mean accuracy from 1.53 to 0.07 mm.

The design and control of a cable-driven rehabilitation robot, which can be configured easily for exercising different articulations such as elbows, shoulders, hips, knees and ankles without requiring any orthosis, are introduced. The passive, active-assisted and active-resisted exercises were designed and implemented using impedance control. The controller could switch between exercises according to the force feedback. The effectiveness of the proposed controller was demonstrated by experimental studies. The robot was tested first with a dummy extremity and then with a healthy subject mimicking various types of patients during the tests. Experimental results showed that satisfactory closed-loop performances were achieved.

Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

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.

In this paper, we conceptualize, analyze, and assemble a prototype adaptive surface system capable of morphing its geometric configuration using an array of linear actuators to impose omnidirectional movement of objects that lie on the surface. The principal focus and contribution of this paper is the derivation of feedback control protocols–for regulating the actuators’ length in order to accomplish the object conveyance task–that scale with the number of actuators and the nonlinear kinematic constraints of the morphing surface. Simulations and experimental results demonstrate the advantages of distributed manipulation over static-shaped feeders.

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.

Microrobots with their promising applications are attracting a lot of attention currently. A microrobot with a triangular mechanism was previously proposed by scientists to overcome the motion limitations in a low-Reynolds number flow; however, the control of this swimmer for performing desired manoeuvres has not been studied yet. Here, we have proposed some strategies for controlling its position. Considering the constraints on arm lengths, we proposed an optimal controller based on quadratic programming. The simulation results demonstrate that the proposed optimal controller can steer the microrobot along the desired trajectory as well as minimize fluctuations of the actuators length.

This paper presents a new feature based approach for place recognition using 2D LiDAR (Light Detection And Ranging) data. The main contribution lies in the mapping process. It includes a keypoint selection strategy to model places with persistent keypoints from concatenated LiDAR scans. Our objective is to achieve map data reduction while maintaining good place recognition performace. LiDAR scans are concatenated with a registration algorithm and keypoints are extracted from each scan. Based on a regular grid, our approach measures the occurrence of similar keypoints in each region of interest defined by a grid cell. Only keypoints with occurrences beyond a threshold, qualified as stable keypoints, are kept in the place model called submap. The environment is therefore modeled as a collection of submaps, which constitutes the global map. Place recognition consists in submap matching followed by a two steps geometric verification. In the first stage, optimal parameters are set using corrected data. Mapping parameters satisfy six criteria among which is the spatial distribution distance, which represents another contribution of our work. It gives a measure on how well keypoints are distributed in space. Place recognition optimal parameters are set through global localization. In the second stage, the approach is evaluated using raw data in the contexts of global localization, and loop closure detection in a SLAM framework. The results obtained using popular real data sets show that our approach achieves significant map data reduction (up to $92\%$ ) while maintaining good place recognition performance, comparable to state-of-the-art methods.

A classic hand-eye system involves hand-eye calibration and robot-world and hand-eye calibration. Insofar as hand-eye calibration can solve only hand-eye transformation, this study aims to determine the robot-world and hand-eye transformations simultaneously based on the robot-world and hand-eye equation. According to whether the rotation part and the translation part of the equation are decoupled, the methods can be divided into separable solutions and simultaneous solutions. The separable solutions solve the rotation part before solving the translation part, so the estimated errors of the rotation will be transferred to the translation. In this study, a method was proposed for calculation with rotation and translation coupling; a closed-form solution based on Kronecker product and an iterative solution based on the Gauss–Newton algorithm were involved. The feasibility was further tested using simulated data and real data, and the superiority was verified by comparison with the results obtained by the available method. Finally, we improved a method that can solve the singularity problem caused by the parameterization of the rotation matrix, which can be widely used in the robot-world and hand-eye calibration. The results show that the prediction errors of rotation and translation based on the proposed method be reduced to $0.26^\circ$ and $1.67$ mm, respectively.

Today, demandsin industrial manufacturing mandate humans to work with large-scale industrial robots, and this collaboration may result in dangerous conditions for humans. To deal with this situation, this work proposes a novel approach for redundant large-scale industrial robots. In the proposed approach, an admittance controller is designed to regulate the interaction between the end effector of the robot and the human. Additionally, an obstacle avoidance algorithm is implemented in the null space of the robot to prevent any possible unexpected collision between the human and the links of the robot. After safety performance of this approach is verified via simulations and experimental studies, the effect of the parameters of the admittance controller on the performance of collaboration in terms of both accuracy and total human effort is investigated. This investigation is carried out via 8 experiments by the participation of 10 test subjects in which the effect of different admittance controller parameters such as mass and damper are compared. As a result of this investigation, tuning insights for such parameters are revealed.

The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse. The proposed method combines color and depth curvature information to create a Gaussian mixture model that can segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. This helps in overcoming the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that does not require a priori knowledge about object geometry and can be implemented online with near real-time performance. The efficacy of the proposed approach is demonstrated through simulation as well as real-world experiment.

Conventional unpowered lower limb exoskeleton paid little attention to the metabolic cost of body during sit down (SD)/stand up (SU). The SD motion model and the motion characteristics of lower extremity are analyzed; then, a novel unpowered lower limb exoskeleton is proposed, and the contribution degree of muscles and stiffness of joints are used for determining the location and stiffness of energy storage element. The metabolic cost of relevant muscles in joints of the left leg is obtained based on Opensim software. The results show that metabolic cost of the gracilis, rectus femoris (RF), and long head of the biceps femoris decreased about 13%, 9%, and 68%, respectively. The total metabolic cost of body decreased about 14% during SD. However, the metabolic cost of the gracilis, RF, and long/short head of the biceps femoris increased about 22%, 33%, 208%, and 46%, respectively. And the metabolic cost of sartorius reduces about 39%, the total metabolic cost of body increased about 25.6% during SU, under the exoskeleton conditions. The results of this study can provide a theoretical basis for the optimal design of unpowered lower limb exoskeleton.

The design and development of an upper limb exoskeleton are being discussed for the tele-operation in order to control the KUKA KR5 industrial robot. When sufficient resolution is not provided by the visual feedback, feedback of haptic provides a qualitative understanding of changes in the remote conditions. This also provides tactile feedback from the virtual and real environment. Peg in a hole operation using exoskeleton works as the master for tele-operation in order to control the robot using immersive environment as visual feedback for the operator. The application of this work can be implemented as a nuclear power generation plant.

In this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

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.