For robot manipulators, there are two types of disturbances. One is model parametric uncertainty; the other is unmodelled parameters such as joint friction forces and external disturbances. Unmodelled joint frictions and external disturbances reduce performance in terms of positioning accuracy and repeatability. In order to compensate for unmodelled parameters, the design of a new controller is considered. First, the modelled and unmodelled parameters are included in a dynamic model. Then, based on the dynamic model, a new Lyapunov function is developed. After that, new nonlinear joint friction and external disturbance estimation laws are derived as an analytic solution from the Lyapunov function; thus, the stability of the closed system is guaranteed. Better values of the adaptive dynamic compensators can be extracted by fuzzy rules according to the tracking error. Limitations and knowledge about friction and external disturbances are not required for the design of the controller. The controller compensates for all possible model parameter uncertainties, all possible unknown joint frictions and external disturbances.

This article is concerned with remote monitoring and control of the 2-degrees of freedom (DoF) robotic manipulators, which have nonlinear dynamics over the packet erasure channel, which is an abstract model for communication over the Internet, WiFi, or Zigbee modules. This type of communication is subject to imperfections, such as random packet dropout and rate distortion. These imperfections cause a significant challenge for monitoring and control of robotic manipulators in the industrial environments because sensitive data, such as sensor data and control commands may not ever reach to their destination resulting in significant performance degradation. Therefore, the effects of these imperfections must be compensated. In this article, we apply two coding and control techniques previously developed for the telepresence ad teleoperation of autonomous vehicles to compensate the effects of the above communication imperfections for remote monitoring and control of the 2-DoF robotic manipulators controlled over the packet erasure channel. To achieve this goal, we design a new linear controller and a new nonlinear controller for the 2-DoF robotic manipulators over the packet erasure channel. The first technique is based on the linearization method and the second technique uses a nonlinear controller. The performances of these two techniques for remote monitoring and control of robotic manipulators are evaluated and compared with each other in this paper. We illustrate their satisfactory performances in the presence of severe communication imperfections.

This article designs a frog-inspired swimming robot based on pneumatic muscles. The musculoskeletal characteristics of the frog are refined and used as the basis for the design of the robot joint structure and movement mode. The posture adjustment module, joint water seal, and power system are designed to meet the robot’s motion requirements, and the structure optimization design of the robot is completed by combining simulation analysis. The body length of the robot is about 710 mm, and the overall mass is 10 kg. Combining the structural characteristics of the robot, the control system is built to realize the frog-like motion. The robot’s propulsion speed is about 0.6 m/s, the propulsion distance reaches 2.4 m, the turning angle is 30°, and the turning radius is 0.6 m. The prototype experiment verifies the rationality of the frog-inspired swimming robot structure design and the reliability of the control system and water seal.

In order to assist patients with lower limb disabilities in normal walking, a new trajectory learning scheme of limb exoskeleton robot based on dynamic movement primitives (DMP) combined with reinforcement learning (RL) was proposed. The developed exoskeleton robot has six degrees of freedom (DOFs). The hip and knee of each artificial leg can provide two electric-powered DOFs for flexion/extension. And two passive-installed DOFs of the ankle were used to achieve the motion of inversion/eversion and plantarflexion/dorsiflexion. The five-point segmented gait planning strategy is proposed to generate gait trajectories. The gait Zero Moment Point stability margin is used as a parameter to construct a stability criteria to ensure the stability of human-exoskeleton system. Based on the segmented gait trajectory planning formation strategy, the multiple-DMP sequences were proposed to model the generation trajectories. Meanwhile, in order to eliminate the effect of uncertainties in joint space, the RL was adopted to learn the trajectories. The experiment demonstrated that the proposed scheme can effectively remove interferences and uncertainties.

The principal objective of the paper is to show the importance of the Hamiltonian in control theory. Instead of using the Lagrangian formulation of electromechanical or robotic systems, our work is focused on robot dynamics by its Hamiltonian. Using the iterative Newton–Euler, we generate the local Hamiltonians and the derivative of the moments at each joint of the robot manipulator. Thus, we can apply decentralized controllers at each joint. We compare and discuss the efficiency of the controllers. We show that the performance of the sliding modes controller is more robust than that of the PD or Bang–Bang controllers.

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.

In this study, several joint axis orientations on equilateral platforms and the limbs of 3-UPU parallel manipulators (PMs) are examined. The generated joint layouts for the platforms were matched with each other to generate and enumerate manipulator architectures based on certain assumptions. The structures of thus obtained manipulators are examined and limb types were determined. These limb types were analyzed using screw theory. The instantaneous mobility of the manipulators and the motion characteristics of the moving platforms are tabulated. The finite mobility analysis of one of the manipulators is performed using a software package as an example. Among several different 3-UPU PM architectures, 118 novel 3-UPU PMs with non-parasitic 3-degrees-of-freedom are significantly important. The classified 3-UPU PMs with determined motion characteristics can be used by researchers as a design alternative for their specific design task.

For an underactuated anthropomorphic hand, apart from replicating the geometry, biomimicry of human counterpart requires design based on functional biomimesis. Optimization based on grasping capabilities to generate a stable grasp for underactuated anthropomorphic hands has been a recent focus. In this article, we optimize the actuation parameters of the underactuated mechanism based on grasp quality measures. Optimization of the hand design parameters like pulley radii, spring stiffnesses, and pre-load angles was undertaken based upon the grasp robustness metric. A quasi-static model based on a soft synergistic compliant grasp of the underactuated hand is formalized. Numerical simulations based on evolutionary strategies are applied to the grasp model to optimize the underactuated parameters. Finally, validation of the results based on the grasp wrench space is presented. The results show that an anthropomorphic hand with an optimized underactuated mechanism performs better grasps.

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.

At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

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.

Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

In dynamically switched systems with unknown switching signal, the control system is conventionally designed based on the worst switching scenario to ensure system stability. Such conservative design demands excessive control effort in less critical switching configurations. In the case of continuum mechanics systems, such excessive control inputs result in increased structural deformations and resultant modeling uncertainties. These deformations alter differential equations of motion which cripple the task of control. In this paper, a new approach for tracking control of uncertain continuum mechanics multivariable systems undergoing switching dynamics and unknown time delay has been proposed. Control algorithm is constructed based on the mathematical rigid model of the plant and a Common Lyapunov Function (CLF) is proposed upon sliding hyperplane regarding all switching configurations. Considering the model-based nature of sliding mode control (SMC) and inevitable uncertainties induced from modeling simplifications of continuum system or parameter evaluation errors, Finite Element Analysis (FEA) is utilized to approximate total model uncertainties. To obtain robust stability, instead of conventional switching functions in the construction of control law, the control inputs are selected such that system dynamics reside within stability bounds which are calculated based on the Lyapunov asymptotic stability criterion. Therefore, the unwanted chattering issue caused by continuous switching is not observed in control input signals. Eventually, the accuracy of the proposed method has been verified through the student version of ANSYS® mechanical APDL-based simulations and its effectiveness has been demonstrated in multiple operating conditions.

A spring-assisted modular and reconfigurable robot (SA-MRR) has been recently developed at our laboratory to reinforce its performance and enable safe and dexterous operation in human–robot environments. Multiple working mode (MWM) control enables each SA-MRR joint module to switch independently between working in a primary actuation mode and a secondary, spring-assisted mode that may improve task-specific energy performance measures and safety in a variety of manipulation tasks. The spring-assisted mode is characterized by synergy of spring and motor energy and may be summoned to offset motor energy demands or to safeguard a reconfigurable set of secondary joint limits. In this research work, two spring-assisted working mode strategies are proposed, and their characteristics have been investigated for SA-MRR actuation energy advantages while safe robot segregation in collaboration tasks is maintained. One MWM strategy has been designed to safeguard task-specific joint limits and is able to decrease motor energy consumption in some tasks. Another MWM strategy has been designed for energy efficiency and was able to reduce motor energy per cycle by $ \text{72}$ % in a simulated manipulation task while maintaining spatial safety constraints. Numerical simulations have demonstrated the effectiveness of the proposed spring-assisted working mode strategies for energy-aware safe manipulation applications.

In this paper, we propose a sparse point-plane odometry used in structured environments. Compared to a point-based odometry, we add additional planar constraints into the process of optimization, making the results more reliable. A novel grid-based plane detection algorithm is proposed to cluster sparse points in the same planes. Then, the planes are parameterized by inverse normal and take part in the windowed optimization. By reducing the size of Hessian Matrix, the process of optimization converges faster. Compared to the original point-based odometry, the proposed method performs better on both robustness and efficiency in structured environments.

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 efficient novel method for tracking the linear deformable objects (LDOs) in real time is proposed. The method is developed based on recursively slicing a pointcloud into smaller pointclouds with sufficiently small variance. The performance of this method is investigated through a series of experiments with various camera resolutions in simulation when a robot end effector tracking an LDO using an RGBD camera, and in real word when the camera tracks a rope during a swing. The performance of the proposed method is compared with another state-of-the-art technique and the outcome is reported here.

To improve mobile robot positioning accuracy in building environments and construct structural three-dimensional (3D) maps, this paper proposes a stereo visual-inertial odometry (VIO) system based on structural lines and points belonging to lines. The 2-degree-of-freedom (DoF) spatial structural lines based on the Manhattan world assumption are used to establish visual measurement constraints. The property of point belonging to a line (PPBL) is used to initialize the structural lines and establish spatial distance-residual constraints between point and line landmarks in the reconstructed 3D map. Compared with the 4-DoF spatial straight line, the 2-DoF structural line reduces the variables to be estimated and introduces the orientation information of scenes to the VIO system. The utilization of PPBL makes the proposed system fully exploit the prior geometric information of environments and then achieves better performance. Tests on public data sets and real-world experiments show that the proposed system can achieve higher positioning accuracy and construct 3D maps that better reflect the structure of scenes than existing VIO approaches.

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.

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.