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For efficient trajectory control of industrial robots, a cumbersome computation for inverse kinematics and inverse dynamics is needed, which is usually developed using spatial transformation using Denavit–Hartenberg principle and Lagrangian or Newton–Euler methods, respectively. The model is highly non-linear and needs to deal with uncertainties because of lack of accurate measurement of mechanical parameters, noise and non-inclusion of joint friction, which results in some inaccuracies in predicting accurate torque trajectories. To get a guaranteed closed form solution, the robot designers normally follow Pieper’s recommendation and compromise with the mechanical design. While this may be acceptable for the industrial robots where the aesthetic look is not that important, it is not for humanoid and social robots. To help solve this problem, this study aims to propose an alternative machine learning-based computational approach based on a multi-gated sequence model for finding appropriate mapping between Cartesian space to joint space and motion space to joint torque space.

First, the authors generate sufficient data required for the sequence model, using forward kinematics and forward dynamics by running N number of nested loops, where N is the number of joints of the robot. Subsequently, to develop a learning-based model based on sequence analysis, the authors propose to use long short-term memory (LSTM) and hence, train an LSTM model, the architecture details of which have been discussed in the paper. To make LSTM learning algorithms perform efficiently, the authors need to detect and eliminate redundant features from the data set, which the authors propose to do using an elegant statistical tool called Pearson coefficient.

To validate the proposed model, the authors have performed rigorous experiments using both hardware and simulation robots (Baxter/Anukul robot) available in their laboratory and KUKA simulation robot data set made available from Neural Learning for Robotics Laboratory. Through several characteristic plots, it has been shown that a sequence-based LSTM model of deep learning architecture with non-redundant features could help the robots to learn smooth and accurate trajectories more quickly compared to data sets having redundancy. Such data-driven modeling techniques can change the future course of direction of robotics research for solving the classical problems such as trajectory planning and motion planning for manipulating industrial as well as social humanoid robots.

The present investigation involves development of deep learning-based computation model, statistical analyses to eliminate redundant features, data creation from one hardware robot (Anukul) and one simulation robot model (KUKA), rigorously training and testing separately two computational models (specially configured two LSTM models) – one for learning inverse kinematics and one for learning inverse dynamics problem – and comparison of the inverse dynamics model with the state-of-the-art model. Hence, the authors strongly believe that the present paper is compact and complete to get published in a reputed journal so that dissemination of new ideas can benefit the researchers in the area of robotics.

The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique.

Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution).

The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude.

This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

This paper aims to describe the topic of robot kinematics and provide a modern machine.

The paper examines, in brief, kinematics and robot kinematics, classes, constraints and chains to provide an introduction. An example shows how robot kinematics can benefit the design of advanced machines for industry.

Robot kinematics, in conjunction with mathematics and other disciplines, lead to a greater understanding of robotics design and control.

This paper discusses robot kinematics in brief as a robot design topic in its own right, as well as presenting the Gantry‐Tau robot as a new and interesting kinematic development. As such, the article should be of general interest to robotics engineers and designers.

Planning and control of humanoid biped walking has been an active research topic for many years. But, there is no definite answer to the question of how to practicre‐examinedally achieve speedy and stable walking in real‐time and in a changing environment. The purpose of this paper is to re‐examine the issue of planning and controlling humanoid biped walking, then to propose two new ideas.

The first idea is to treat the supporting foot of a biped to be part of the ground. In this way, there is a foot reaction force acting at a fixed virtual joint, which can be at, or below, the ankle joint. And, a new concept is come our that is named as in‐foot ZMP in contrast to the existing concept of on‐ground ZMP. The unique benefit with this new concept of in‐foot ZMP is that the ZMP control is no longer an issue because the in‐foot ZMP can be controlled so as to to be at a fixed virtual joint during a stable walking. Such a fixed virtual joint can be called a ZMP joint.

The second idea is to focus on hip's trajectory (instead of on‐ground ZMP's trajectory) and to split a hip's dynamic response into two independent parts: one is the steady‐state response contributing to the stability of walking (or standing), and the other is the transient response contributing to the speed of walking. This idea allows us to explicitly postulate the necessary and sufficient condition for achieving leg stability as well as the necessary and sufficient condition for achieving foot stability. The paper shows that the implementation of these two new ideas help realize a unified framework for task‐guided, intention‐guided, and sensor‐guided, planning and control of humanoid biped walking.

This paper first re‐examines the issue of planning and controlling humanoid biped walking, then proposes two new ideas. The first idea is to treat the supporting foot of a biped to be part of the ground. The second idea is to focus on hip's trajectory (instead of on‐ground ZMP's trajectory) and to split a hip's dynamic response into two independent parts: one is the steady‐state response contributing to the stability of walking (or standing), and the other is the transient response contributing to the speed of walking.

In the current teleoperation system of humanoid robots, the control between arms and the control between the waist and arms are individual and lack coordinated motion. This paper aims to solve the above problem and proposes a teleoperation control approach for a humanoid robot based on waist–arm coordination (WAC).

The teleoperation approach based on WAC comprises dual-arm coordination (DAC) and WAC. The DAC method realizes the coordinated motion of both arms through one hand by establishing a mapping relationship between a single hand controller and the manipulated object; the WAC method realizes the coordinated motion of both arms and waist by calculating the inverse kinematic input of robotic arms based on the desired velocity of the waist and the end of both arms. An integrated teleoperation control framework provides interfaces for the above methods, and users can switch control modes online to adapt to different tasks.

After conducting experiments on the dual-arm humanoid robot through the teleoperation control framework, it was found that the DAC method can save 27.2% of the operation time and reduce 99.9% of the posture change of the manipulated object compared with the commonly used individual control. The WAC method can accomplish a task that cannot be done by individual control. The experiments proved the improvement of both methods in terms of operation efficiency, operation stability and operation capability compared with individual control.

The DAC method better maintains the constraints of both arms and the manipulated object. The WAC method better maintains the constraints of the manipulated object itself. Meanwhile, the teleoperation framework integrates the proposed methods and enriches the teleoperation modes and control means.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

Humanoid robots have complicated dynamics, and they lack dynamic stability. Despite having similarities in kinematic structure, developing a humanoid robot with robust walking is quite difficult. In this paper, an attempt to produce a robust and expected walking gait is made by using an ALO (ant lion optimization) tuned linear inverted pendulum model plus flywheel (LIPM plus flywheel).

The LIPM plus flywheel provides the stabilized dynamic walking, which is further optimized by ALO during interaction with obstacles. It gives an ultimate turning angle, which makes the robot come closer to the obstacle and provide a turning angle that optimizes the travel length. This enhancement releases the constraint on the height of the COM (center of mass) and provides a larger stride. The framework of a sequential locomotion planer has been discussed to get the expected gait. The proposed method has been successfully tested on a simulated model and validated on the real NAO humanoid robot.

The convergence curve defends the selection of the proposed controller, and the deviation under 5% between simulation and experimental results in regards to travel length and travel time proves its robustness and efficacy. The trajectory of various joints obtained using the proposed controller is compared with the joint trajectory obtained using the default controller. The comparison shows the stable walking behavior generated by the proposed controller.

Humanoid robots are preferred over mobile robots because they can easily imitate the behaviors of humans and can result in higher output with higher efficiency for repetitive tasks. A controller has been developed using tuning the parameters of LIPM plus flywheel by the ALO approach and implementing it in a humanoid robot. Simulations and experiments have been performed, and joint angles for various joints are calculated and compared with the default controller. The tuned controller can be implemented in various other humanoid robots

This paper aims to introduce the human arm movement primitive (HAMP) to express and plan the motions of anthropomorphic arms. The task planning method is established for the minimum task cost and a novel human-like motion planning method based on the HAMPs is proposed to help humans better understand and plan the motions of anthropomorphic arms.

The HAMPs are extracted based on the structure and motion expression of the human arm. A method to slice the complex tasks into simple subtasks and sort subtasks is proposed. Then, a novel human-like motion planning method is built through the selection, sequencing and quantification of HAMPs. Finally, the HAMPs are mapped to the traditional joint angles of a robot by an analytical inverse kinematics method to control the anthropomorphic arms.

For the exploration of the motion laws of the human arm, the human arm motion capture experiments on 12 subjects are performed. The results show that the motion laws of human arm are reflected in the selection, sequencing and quantification of HAMPs. These motion laws can facilitate the human-like motion planning of anthropomorphic arms.

This study presents the HAMPs and a method for selecting, sequencing and quantifying them in human-like style, which leads to a new motion planning method for the anthropomorphic arms. A similar methodology is suitable for robots with anthropomorphic arms such as service robots, upper extremity exoskeleton robots and humanoid robots.

The purpose of this paper is to design and implement a team of kid size humanoid soccer robots conforming to RoboCup Humanoid league.

The project is described in two main parts: hardware and software. The hardware section consists of the mechanical structure and the driver circuit board enabling each robot to walk, fast walk, autonomously get up, kick and dribble when it catches the ball. The software is developed as a robot application which consists of motion controller, autonomous motion robot, self localization based on vision system, AI, trajectory planning and network.

This year, the authors' developments for the humanoid robot include: the design and construction of our new humanoid robots structure and implementation of a new recurrent hybrid neural network for walking control. The control system consists of two neural network controllers, two standard PD controllers and a robot walking planar. The proposed neural network controller has three layers, which are input, hidden, and output layers.

This paper presents results of research work in the field of autonomous robot‐middle size soccer robot, supported by IAU‐ Isfahan Branch (Khorasgan).

The purpose of this paper is to present a dual-mode online optimization method (OOM) for trajectory tracking of the redundant manipulators. This method could be used to resolve the problem of the kinematics redundancy effectively when the manipulator moves in a limited space or its movements go through a singular point.

In the proposed method, the physical limits of the manipulator in the torque level is considered as inequality constraints for the optimal scheme. Besides, a dual-mode optimal scheme is developed to yield a feasible input in each control period during the path tracking task of the manipulator, especially when it moves under the limited space or around the singular point. Then, the scheme is formulated as a quadratic programming; the computationally efficient quadratic programming solver based on interior method is formulated to solve the kinematic redundancy problem.

The traditional pseudo inverse method (PIM) for the kinematic resolution to the redundant manipulator has some limitations, such as slow computation speed, unable to take joint physical limits into consideration, etc. Relatively, the OOM could be used to conquer the deficits of the PIM method. Combining with the dual-mode optimal scheme and considering the physical constraints in the torque level, the online method proposed in this paper is more robust and efficient than the existing method.

In this paper, dual-mode OOM is first proposed for the resolution of the kinematics redundancy problem. Specific design of its model and the discussion of its performance are also presented in this paper.