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.

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.

This study presents a novel context awareness multihuman–robot interaction (MHRI) system that allows multiple operators to interact with a robot. In the system, a monocular multihuman 3D pose estimator is first developed with the convolutional neural network. The estimator first regresses a set of 2D joints representations of body parts and then restores the 3D joints positions based on these 2D representations. Further, the 3D joints are assigned to the corresponding individual with a priority–redundancy association algorithm. The whole 3D pose of each person is reconstructed in real time, even in crowded scenes containing both self-occlusion of the body and inter-person occlusion. Then, the identities of multiple persons are recognized with action context and 3D skeleton tracking to improve interactive efficiency. For context-awareness multitask interaction, the robot control strategy is designed based on target goal generation and correction. The generated goal is taken as a reference to the model predictive controller (MPC) to generate motion trajectory. Different interactive requirements are adapted by adjusting the weight parameters of the energy function of the MPC controller. Multihuman–robot interactive experiments, including dynamic obstacle avoidance (human–robot safety) and cooperative handling, demonstrate the feasibility and effectiveness of the MHRI, and the safety and collaborative efficiency of the system are evaluated with HRI metrics.

The knee joint plays a significant role in ground clearness, which is a crucial subtask of normal walking and avoiding falls. While post-stroke survivors are often faced with muscle weakness during walking, which leads to inadequate knee flexion. The lack of ground clearance caused by inadequate knee flexion will severely impede walking, increase metabolic exertion, and increase the risk of falls. A compliant exoskeleton robot possesses more favorable edges than other rigid ones in lightweight, safety, sense of comfort, and so on. We developed a new type of soft exoskeleton robot to assist the knee joint to achieve desired movements with Bowden cable transmitting force and torque. With the agonist–antagonist driving method, like a group of muscles working, we have explored dual-motors structure to realize the knee flexion function. It has built a standard dynamic model to analyze stability and realize the control law. We have conducted simulation and prototype experiments to verify the feasibility and usefulness of our method. The results show that the device can compensate for the lack of the knee joint driving force and realize the reference movement. Finally, we concluded that our method is a desirable way, and the scheme could improve the knee flexion ability and clearing ground.

An important goal for humanoid robots is to achieve fast, flexible and stable walking. In previous research, the structure and walking algorithms evolved separately, resulting in a slow evolution speed and lack of an initial design basis. This paper proposes comprehensively considering body morphology and walking patterns, exploring the relationship between them and their influence on the motion ability. The method parameterizes the body morphology and walking patterns. Then a response surface model is established to describe the complex relationship between these parameters and finally obtain the optimized parameters, which provides a reference for the structural design and gait generation.

PU- and P*U*-equivalent parallel mechanisms (PMs) are critical families of PMs with one translational and two rotational (1T2R) degree of freedoms and have always been a research hotspot among lower mobility PMs. However, researches on these two families of PMs remain to be inadequate, and existing types are few and uncomprehensive. In this study, first, general wrench systems of PU-equivalent are derived based on virtual-chain approach and screw theory, revealing its constraint characteristics under general configuration. Cause of one parasitic motion is put forward and general wrench systems of P*U*-equivalent PMs with one parasitic motion are obtained. In addition, constraint analysis has been carried out to figure out constraint characteristics of P*U*-equivalent PMs with three parasitic motions. Second, branch chains are divided by generating constraints, and their structures are synthesized based on the presented rules. Then, the process for type synthesis of PU-equivalent PMs and P*U*-equivalent PMs and a series of novel 1T2R PMs are attained based on this. Finally, a novel PU-equivalent PM, 2PRU-PRUPc, and a novel P*U*-equivalent PM with one parasitic motion, 2PRU-PUU PM, are analyzed, demonstrating the effectiveness of the proposed type synthesis method.

Fault-tolerant control systems are vital in many industrial systems. Actuator redundancy is employed in advanced control strategies to increase system maneuverability, flexibility, safety, and fault tolerability. In this paper, a fault-tolerant control scheme is proposed to make an over-actuated octorotor robust, against actuators fault and saturation. A sliding mode observer is employed to determine the actuators condition. Then, a fault-tolerant control based on the control allocation methodology is proposed to distribute the control signals between the actuators by considering their condition. In a nonlinear system, an actuator fault can lead to the saturation of other actuators and steady-state errors that can cause closed-loop instability. Hence, the proposed control scheme corrects the actuator signals in a way that their limitations are considered. Finally, experimental studies are carried out and a comparison study is provided.

The traditional identification methods of industrial robots are based on Inverse Dynamic Identification Model (IDIM). Based on the model, input torque, motor-side and link-side motion data are necessary when joint flexibilities are considered. However, it is often unavailable or expensive to general robots which are not equipped with link-side sensors. To solve the problem, a novel dynamic parameter identification method, which only employ input torque and motor-side motion data, is proposed in this report. Based on motor-side dynamics, link-side dynamics are modified as high-order nonlinear functions of input torque and motor-side motion. Then, through different trajectory-load groups and high-order observers, the nonlinear equations can be solved, and dynamic parameters can be estimated with short operation time. The selection rules of the trajectory-load groups are then discussed based on simulation results, so as to promote estimation results. Finally, experiments are conducted to verify the proposed method and exhibit the selection rules of observer gains. As shown in the report, except viscous friction parameters, identification difference between the IDM-based methods and the proposed one is less than 9%.

The paper deals with the workspace-based optimization of a novel humanoid robotic arm. The eight-degree-of-freedom hybrid manipulator that conforms to the kinematics characteristics of the human arm is briefly introduced. According to the structural features of this mechanism and the requirements of tasks in the complex environment, the workspace is divided into three parts, the orientation space of the humanoid shoulder joint, the position space of the humanoid elbow joint, and the active orientation space of the end-moving platform. Moreover, a multi-parameter planar model is proposed for the optimization problem with multidimensional parameters and highly nonlinear constraints. Based on the visualized optimization result, the coupling effect of each parameter on the corresponding workspace is clearly presented. Considering the compactness and the processing and assembling technology of this mechanism, a set of structural parameters satisfying the workspace-based optimization objective is obtained. Simulation results show that the corresponding workspace of the three parts has increased significantly by the factor of 1.45, 1.68, and 1.3, respectively.

The main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.

The quaternion is a powerful and common tool to avoid singularity in rotational dynamics in three-dimensional (3D) space. Here it has been particularly used as an alternative to Euler angles and rotation matrix. The application of the quaternion is exercised in quadrotor modeling and control. It changes the dynamics and represents a singularity-free attitude model. Here for the first time (for the best knowledge of authors), the state-dependent differential Riccati equation (SDDRE) control has been implemented on the quaternion-based model of a quadcopter. The proposed control structure is capable of aerobatic flight, and the Pugachev’s Cobra maneuver is chosen to assess the capability of the quaternion-based SDDRE approach. The introduced control simulator is validated by comparison with conventional dynamics based on Euler angles, controlled using a proportional-derivative (PD) controller on a normal regulation flight. The simulator successfully performed the Cobra maneuver and also validated the proposed structure. The more precision in regulation along with lower energy consumption demonstrated the superiority of the introduced approach.

Reaction null space (RNS) planning and control of a planar three-link space manipulator’s pregrasping a spinning target are studied. First, the Lagrange dynamic model of the manipulator was established. Second, the RNS motion planning algorithm was derived, and the vector norm constraint algorithm of RNS planning was addressed to ensure certain joint angular acceleration constraints were satisfied. Furthermore, an augmented adaptive sliding mode controller based on time delay estimation (TDE) was proposed. This controller estimated the unknowns of the system by TDE technology, in which accurate and complete dynamics were not required, and an adaptive TDE was introduced to decrease the estimation errors and avoid serious chattering. Finally, numerical simulations were carried out to verify the effectiveness of the proposed RNS planning and control algorithm.

In this study, we present a new approach to improving vocal fold access to perform phonomicrosurgery. It is done by shooting the laser through a mirror to reach the vocal fold hidden parts. A geometrical study of laser shooting path was conducted for vocal fold anatomical constraints, followed by devising a laser-shooting system conceptual design. Control laws were developed and tested by simulation and validated experimentally on a test bench in a monocular and stereoscopic configuration. Simulation and experimental results are provided to demonstrate the effectiveness of the developed approach.

This paper considers the problem of formation control for a team of nonholonomic wheeled mobile robots considering obstacle avoidance. A new control algorithm based on the model predictive control (MPC) and the nonlinear dynamics of the system is presented here. The control algorithm is applied to the nonlinear system using two different controllers including linear MPC and nonlinear MPC. The virtual structure formation approach and artificial potential field method are employed to determine the reference trajectories of the robots and to solve the problem of obstacle avoidance. A control algorithm consisting of two parts is proposed to track the trajectories and maintain the team’s formation. Two advantages of using MPC techniques are the ability to consider control and state constraints which are of high importance in practical applications. The main contribution of this paper is the design of two robust control systems to disturbance with respect to actuator saturation limits. Simulation results demonstrate the effectiveness and robustness of the proposed control algorithm in trajectory tracking and formation maintenance in the presence of disturbance and actuator limits.

This article investigates the fixed-time trajectory tracking control of a free-flying rigid space manipulator perturbed by model uncertainties and external disturbances. A novel robust fixed-time integrated controller is developed by integrating a nominal fixed-time proportional–differential-like controller with a fixed-time disturbance observer. It is strictly proved that the proposed controller can ensure the position and velocity tracking errors regulate to zero in fixed time even subject to lumped disturbance. Benefiting from the feedforward compensation, the proposed controller has the strong robustness and excellent disturbance attenuation capability. The effectiveness and advantages of the proposed control approach are validated through simulations and comparisons.

The capabilities of teleoperated robots can be enhanced with the ability to recognise and reproduce human-like behaviour. The proposed framework presents motion recognition for a Kinect-based NAO teleoperation. It allows the NAO robot to recognise the human motions and act as a human motion imitator. A stable whole-body imitation is still a challenging issue because of the difficulty in dynamic balancing of centre of mass (CoM). In this paper, a novel adaptive balancing technique for NAO (ABTN) is proposed to control the whole body in single as well as double supporting phases. It targets dynamic balancing of the humanoid robot by solving forward kinematics and applying a weighted average of mass with the CoMs of individual links with respect to the previous joint frames, which provides us with the dynamic CoM of the whole body. Our novel approach uses this dynamic CoM and calculates joint angles using proposed pitch and roll control algorithm to keep the dynamic CoM inside the stable region. Additionally, the NAO robot is capable of recognising human motions using the proposed 7-layer one-dimensional convolutional neural network (1D-CNN). To solve the problem of variable length of time sequences, Zero padding is introduced with 1D-CNN. It attains a recognition accuracy of 95% as compared to the hidden Markov model and neural network. The experimental results demonstrate that the developed teleoperation framework is robust and serves as potential support for the development and application of teleoperated robots.

This paper presents a geometrical alternative to estimate the accuracy of a deployable mechanism equipped on the Synthetic Aperture Radar (SAR) space satellite. The deployable mechanism is simplified into a planar mechanism, and the error space of the outer panel in its deployed state is modeled concerning multiple revolute joint clearances and link length tolerances. Compared with the existing methods, the advance of the proposed geometrical approach lies in that it gives expressions of the complete error mobility that the outer panel may have. After deducing the expressions, the final error space is visualized and evaluated numerically with discrete sampling points. Finally, based on the error space and the computed maximum errors, effects of tolerances on accuracy are studied and the optimal accuracy design of tolerances is obtained. The result reveals that, for the deployable mechanism discussed in this paper, effects of tolerances on the final accuracy can be eliminated without increasing the manufacturing cost.

Recent improvements in robotic arms have increased their interest in many areas such as the industry and biomedical sectors. Path planning is an essential part of the robotic arm, since most automated factories seek to move things from one place to another with obstacles providing the shortest route. This paper presents a novel optimal path planning algorithm based on the 3D cubic Bézier curve with three shape parameters and its geometric properties and hierarchical clustering. The proposed method utilizes a feature vector which is obtained from curvature, torsion, and path length of candidate curves. A hierarchical clustering is applied to determine curve pairs. Then, a multi-objective function is used to determine the best curve pair, which gives the best curve for the robotic arm. Besides forming the optimal 3D cubic Bézier path, the optimal ruled and developable path surfaces are obtained. In addition to presenting theoretical results, this work also demonstrates the proposed method on several Kinova Gen3 robotic arm cases.

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.

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.