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.

One alternative to overcome the presence of singularities within Parallel Manipulators’ workspace is kinematic redundancy. This design alternative can be realized by adding an extra active joint to a kinematic chain. Due to this addition, the IKM presents an infinite number of solutions requiring a redundancy resolution scheme. Moreover, Parallel Manipulators’ control may require complex strategies due to their coupled and complex dynamic and kinematic relations. In this work, a model-free, a joint space computed torque, and a hybrid joint-task-space computed torque control strategies are experimentally compared for a kinematically redundant parallel manipulator. The latter is a novel strategy that requires the measurement of the end-effector’s pose, which is performed by an eye-to-hand limited frame rate camera. The impact of up to three kinematic redundancy levels is also experimentally evaluated using prepositioning and ongoing positioning redundancy resolution schemes. The data are assessed by evaluating a prescribed trajectory executed using a planar kinematically redundant parallel manipulator. These results indicate that kinematic redundancy can not only be used as an alternative design for reducing the presence of singular regions, as claimed in the literature, but also be used along with model-based control strategies for improving dynamic performance and accuracy of parallel manipulators.

Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions. During these sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at this stage, often cannot control their muscles, and neurological disorders may occur and lead to undesirable movements. Thus, the therapist should train the joint gently to handle any sudden involuntary movements. Otherwise, the joint may undergo excessive torques, which may injure it. In this paper, we address this case and develop a clinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirable movements. The developed system has an innovative mechanism to measure interaction torques exerted by involuntary movements. Then, we introduce a new control approach consisting of an admittance controller and a proportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBF guides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torques and updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirable movement occurs the robot gets along with this movement smoothly, then it gets back to the predefined trajectory. To validate the performance of the proposed admittance PD-RBF controller, we consider two controllers, an admittance adaptive sliding mode control and an admittance conventional PD one. Then, a compatarive study is conducted on these controllers via real-world experiments. The obtained results verify the efficiency of the admittance PD-RBF and prove its superiority over the other aforementioned controllers.

Based on the characteristics of high-frequency swing during fast swimming of fish, this paper designs a bionic fish-driven joint based on electromagnetic drive to achieve high-frequency swing. Aiming at the characteristic parameters of high-frequency swing control, the Fourier transform is used to separate the characteristic parameters and then compared the driving accuracy of the joints in open-loop and closed-loop. The comparison results show that the closed-loop control is performed after Fourier transform. Under the same driving conditions, the closed-loop control method can improve the joint driving accuracy. Then a bionic fish robot composed of three joints is designed according to this method and Kane method is used to model it dynamically and combined with the central pattern generator control method to complete model simulation and related experiments. The experimental results show that the bionic fish prototype can swim faster under high-frequency swing under electromagnetically driven joints.

In recent years, using autonomous underwater vehicles (AUVs) for submarine missions has increased substantially. One of the problems in controlling these nonlinear devices is the possibility of a fault in the system operators. Failure causes increased operating costs and reduced vehicle performance. Therefore, the use of fault tolerance control is essential to ensure the stability and ability of the device to continue its activity. The focus of this article is on the trajectory tracking control for an underactuated AUV with actuator faults using kinematics and dynamics modeling. An adaptive rule is used as an online estimation to compensate for malfunctions in robot performance. In this regard, the adaptive fault-tolerant control plan is proposed, so that the closed-loop system is stable, and all control objectives are achievable. At first, the dynamic model of AUV with actuator fault and disturbance is described. Next, the control algorithm is designed for trajectory tracking in the presence of time-varying disturbances and actuator faults. The proposed adaptive rules will overcome disturbances and actuator faults. Finally, to illustrate the effectiveness of the proposed method, the provided controller is compared with other common control methods.

This paper presents a hierarchical framework that allows online point-to-point dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot and the zero moment point stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot’s quasi-static path is then used as an initial guess to warm start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint. Qualitative and quantitative comparisons with existing methods are also presented.

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.

In this work, a vision-based guidance algorithm for interception of an aerial manoeuvring target is proposed. Vision-based aerial engagement in 3D space in an outdoor environment is challenging due to uncertainty in estimating the target location. The vision-based guidance algorithm proposed here generates the desired velocity commands for the interceptor drone based on the orientation of the target pixel in the image plane. The desired velocity at the centre of gravity of the interceptor is calculated such that the velocity at the camera centre is aligned towards the line joining the target pixel and the camera centre. It is shown that the proposed guidance strategy forces the target pixel towards the centre of the image plane and leads to low miss distance. The algorithm developed is first tested in the ROS-Gazebo platform and then implemented in field experiments.

Robotics and computer vision problems commonly require handling rigid-body motions comprising translation and rotation – together referred to as pose. In some situations, a vectorial parameterization of pose can be useful, where elements of a vector space are surjectively mapped to a matrix Lie group. For example, these vectorial representations can be employed for optimization as well as uncertainty representation on groups. The most common mapping is the matrix exponential, which maps elements of a Lie algebra onto the associated Lie group. However, this choice is not unique. It has been previously shown how to characterize all such vectorial parameterizations for SO(3), the group of rotations. Some results are also known for the group of poses, where it is possible to build a family of vectorial mappings that includes the matrix exponential as well as the Cayley transformation. We extend what is known for these pose mappings to the $4 \times 4$ representation common in robotics and also demonstrate three different examples of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem. In the pointcloud alignment problem, our results lead to a new algorithm based on the Cayley transformation, which we call CayPer.

Thispaper addresses the problem of carrying an unknown nonuniform payload by multiple quadrotor agents. The load is modeled as a rigid body with unknown weight and position of Center of Gravity (CG) for the agents, and is included in their dynamic equations of motion. The agents and the load are assumed to be connected to each other by taut ropes. The Udwadia–Kalaba equation is used to calculate the constraint forces on the ropes acting on each quadrotor. Inner-loop and outer-loop controllers for quadrotors position and attitude control are presented. For the outer loop, an estimation algorithm based on the invariance and immersion adaptive control is utilized to estimate the unknown physical parameters of the payload including mass and CG position without using multi-axes force/torque sensors. The inner-loop controller employs an adaptive controller. Simulation results, for two and four agents carrying a nonuniform rod and cubic payload, show the effectiveness of the proposed algorithm. A case study is also performed to investigate the effect of quadrotors positioning on flight endurance of the cooperative aerial team carrying a nonuniform payload.

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.

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.

This paper first uses a decoupling modeling method to model legged robots. The method groups all the degrees of freedom according to the number of limbs, regarding each limb as a manipulator with serial structure, which greatly reduces the number of dynamic parameters that need to be identified simultaneously. On this basis, a step-by-step identification method from the end-effector link to the base link, referred to as “E-B” identification method, is proposed. Simulation verification is carried out on a quadruped robot with 16 degrees of freedom in Gazebo, and the validity of the method proposed is discussed.

We present a method to drastically reduce the required number of degrees-of-freedom (DOFs) needed for walking for each leg of bipedal robots and lower-limb exoskeletons. This approach releases more legs DOFs in the null space to do other tasks, instead of unnecessarily constraining them. It uses relative reference frames to control relative motion between the two feet, instead of the usual method of controlling foot movement with respect to fixed reference frames. In its basic form, it controls the bipedal walking holistically using two controllers: (1) world space control using relative feet motion and (2) null-space control of the legs posture.

The recent development of new offshore projects in pre-salt deepwater fields has placed offshore loading operations as the main production outflow alternative, increasing the operational complexity and risks. Numerous dangerous situations are associated with oil offloading, such as the messenger line transfer during the mooring stage. Nowadays, this critical task is realized by launching a thin messenger cable using the pneumatic line throwing apparatus. This is a complex and slow process since the operation usually occurs with the ship opposite to the wind. This work proposes a hybrid flight methodology based on computer vision and sensor fusion techniques for autonomous unmanned aerial vehicles (UAVs). The UAV takes off from an oil rig and precisely reaches a specific point in the shuttle tanker without using expensive positioning devices and augmenting UAV’s orientation (yaw) precision since the compass can suffer from severe interference due to naval metallic structures near the vehicle. The proposed framework was tested in a realistic simulated environment considering several practical operational constraints. The results demonstrated both the robustness and efficiency of the methodology.

Optimal mechanical design, model-based control, and robot dynamic calibration mainly rely on the analytical formulation of robot dynamics. In this paper, the kinematics equations of a general 3-UPU translational parallel manipulator (TPM) are derived, and then, by using the principle of the virtual work theorem, the full implicit dynamic model is derived. Furthermore, by making some modifications, the explicit dynamic formulation of the robot is attained, which is the basis of a wide range of advanced model-based controllers. To validate the proposed formulation, a prototype of the 3-UPU TPM is modeled in MSC-ADAMS® software, and the results of the dynamic formulation are validated using this model. The results show the high accuracy of the proposed dynamic formulation presented in this article.

Body posture determination methods have many applications, including product design, ergonomic workplace design, human body simulation, virtual reality, and animation industry. Initiated in robotics, inverse kinematic (IK) method has been widely applied to proactive human body posture estimation. The analytic inverse kinematic (AIK) method is a convenient and time-saving type of IK methods. It is also indicated that, based on AIK methods, a specific body posture can be determined by the optimization of an arbitrary objective function. The objective of this paper is to predict the postures of human arms during reaching tasks. In this research, a human body model is established in MATLAB, where the middle rotation axis analytic kinematic method is accomplished, based on this model. The joint displacement function and joint discomfort function are selected to be initially applied in this AIK method. Results show that neither the joint displacement function nor the joint discomfort function predicts postures that are close enough to natural upper limb postures of human being, during reaching tasks. Therefore, a bi-criterion objective function is proposed by integrating the joint displacement function and joint discomfort function. The accuracy of the arm postures, predicted by the proposed objective function, is the most satisfactory, while the optimal value of the coefficient, in the proposed objective function, is determined by golden section search.

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.

Inverted pendulum systems (IPSs) are mostly used to demonstrate the control rules for keeping the pendulum at a balanced upright position due to a slight force applied to the cart system. This paper presents an application for nonlinear control of an x-z type IPS by using a proportional-integral-derivative (PID) controller with newly established evolutionary tuning method Lightning Search Algorithm (LSA). A single, double and triple PID controller system is tested with the conventional and the self-tuning controllers to get a better understanding of the performance of the given system. The mathematical modelling of the x-z type IPS, the theoretical explanation of the LSA and the simulation analysis of the x-z type IPS is put forward entirely. The LSA algorithm solves the optimization problem of PID controller in an evolutionary way. The most effective way of making comparisons is evaluating the performance results with a well-known optimization technique or with the previous accepted results. We have compared the system’s performance with particle swarm optimization and with a classical control study in the literature. According to the simulation results, LSA-tuned PID controller has the ability to decrease the overshoot better than the conventional-tuned controllers. Finally, it can be concluded that the LSA-supported PID can better stabilize the pendulum angle and track the reference better for non-disturbed and the slightly disturbed systems.

This paper presents an investigation of the effectiveness of different inverse kinematics strategies in a context of physical human-robot interaction in which passive articulated shells are mounted on the links of a serial robot for manual guidance. The concept of passive link shells is first recalled. Then, inverse kinematics strategies that are designed to plan the trajectory of the robot according to the motion sensed at the link shells are presented and formulated. The different approaches presented all aim at interpreting the motion of the shells and provide an intuitive interaction to the human user. Damped Jacobian based methods are introduced in order to alleviate singularities. A serial 5-dof robot used in previous work is briefly introduced and is used as a test case for the proposed inverse kinematics schemes. The robot includes two link shells for interaction. Simulation results based on the different inverse kinematic strategies are then presented and compared. Finally, general observations and recommendations are discussed.