This paper illustrates means by which the techniques of compliance and electroadhesion can, using electror-heological, and other fluids, be combined to provide very effective shape adaptive robot end-effectors and similar holding surface.

This paper addresses attention to a design for a low-cost exoskeleton with fairly simple construction, lightweight, easy to wear and to adapt to human legs. The design core is focused on a cam-mechanism implementation at the ankle joint level of the leg exoskeleton. The engineering feasibility of the proposed design is characterized by numerical simulations for the design process.

A SCARA type direct-drive robot which can be used in the assembly operation of printed circuit boards was designed and manufactured with graphite fiber epoxy composite material. The optimum stacking angle of the graphite fiber which gives the minimum deflection of the composite robot arm was calculated by the energy method and the manufacturing method of the box-type cross section of the robot arm with composite materials was developed.

In order to compare the dynamic characteristics of the composite material robot arm with a conventional material robot arm, an aluminum robot arm which has the same dimensions and thickness of the composite robot arm was manufactured.

The static and dynamic characteristics of the composite material robot arm such as static deflection, natural frequency and damping as well as weight saving were considerably improved compared to those of the aluminum robot arm.

This paper presents a new program package for the generation of efficient manipulator kinematic and dynamic equations in symbolic form.

The basic algorithm belongs to the class of customized algorithms that reduce the computational burden by taking into account the specific characteristics of the manipulator to be modelled. The output of the package is high-level computer program code for evaluation of various kinematic and dynamic variables: the homogeneous transformation matrix between the hand and base coordinate frame, Jacobian matrices, driving torques and the elements of dynamic model matrices. The dynamic model is based on the recursive Newton-Euler equations. The application of recursive symbolic relations yields nearly minimal numerical complexity. Further improvement of computational efficiency is achieved by introducing different computational rates for the terms depending on joint angles, velocities and accelerations. A comparative study of numerical complexity for several typical industrial robots is presented.

The workspace of a parallel manipulator is usually smaller than the size of the robot itself. It is important to derive new structures that enjoy the advantages of parallel manipulators and also have a large workspace. In this paper we present two configurations of similar structures RRRS and RRSR with rotating links. The RRRS structure has a relatively large workspace—larger than the size of the robot itself—which is not common in parallel robots. The inverse and forward kinematics of the robots are presented. The workspaces of the robots are compared to similar and well-known structures, such as Eclipse, Alizade, Delta, and Hexa robots.

In this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.

This paper presents a new method using hierarchical optimal control for path planning and calculating maximum allowable dynamic load (MADL) of wheeled mobile manipulator (WMM). This method is useful for high degrees of freedom WMMs. First, the overall system is decoupled to a set of subsystems, and then, hierarchical optimal control is applied on them. The presented algorithm is a two-level hierarchical algorithm. In the first level, interaction terms between subsystems are fixed, and in the second level, the optimization problem for subsystems is solved. The results of second level are used for calculating new estimations of interaction variables in the first level. For calculating MADL, the load on the end effector is increased until actuators get into saturation. Given a large-scale robot, we show how the presenting in distributed hierarchy in optimal control helps to find MADL fast. Also, it enables us to treat with complicated cost functions that are generated by obstacle avoidance terms. The effectiveness of this approach on simulation case studies for different types of WMMs as well as an experiment for a mobile manipulator called Scout is shown.

This paper describes a simulation model for a multi-legged locomotion system with joints at the legs having viscous friction, flexibility and backlash. For that objective the robot prescribed motion is characterized in terms of several locomotion variables. Moreover, the robot body is divided into several segments in order to emulate the behaviour of an animal spine. The foot-ground interaction is modelled through a non-linear spring-dashpot system whose parameters are extracted from the studies on soil mechanics. To conclude, the performance of the developed simulation model is evaluated through a set of experiments while the robot leg joints are controlled using fractional order algorithms.

The design of multifunctional upper limb prosthetics has been investigated in recent years. Several areas or research need to be developed for successful implementation of dextrous upper limb prosthesis, in particular better EMG interfaces for implementing command languages.

This article introduces a novel three-bit EMG command language concept as a user interface for the multifunctional MANUS prosthesis prototype. Following the global approach proposed by MANUS, a training process and the supporting Virtual Reality training software are also presented. The last section describes the evaluation process and the results.

Posture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

We present an efficient obstacle avoidance control algorithm for redundant manipulators using a new measure called collidability measure. Considering moving directions of manipulator links, the collidability measure is defined as the sum of inverse of predicted collision distances between links and obstacles: This measure is suitable for obstacle avoidance since directions of moving links are as important as distances to obstacles. For kinematic or dynamic redundancy resolution, null space control is utilized to avoid obstacles by minimizing the collidability measure: We present a velocity-bounded kinematic control law which allows reasonably large gains to improve the system performance. Also, by clarifying decomposition in the joint acceleration level, we present a simple dynamic control law with bounded joint torques which guarantees tracking of a given end-effector trajectory and improves a kinematic cost function such as collidability measure. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

In this paper, we investigate trajectory tracking in a multi-input nonlinear system, where there is little knowledge of the system parameters and the form of the nonlinear function. An identification-based iterative learning control (ILC) scheme to repetitively estimate the linearity in a neighborhood of a desired trajectory is presented. Based on this estimation, the original nonlinear system can track the desired trajectory perfectly by the aid of a regional training scheme. Just like in adaptive control, a singularity exists in ILC when the input coupling matrix is estimated. Singularity avoidance is discussed. A new parameter modification procedure for ILC is presented such that the determinant of the estimate of the input coupling matrix is uniformly bounded from below. Compared with the scheme used for adaptive control of a MIMO system, the proposed scheme reduces the computation load greatly. It is used in a robotic visual system for manipulator trajectory tracking without any information about the camera-robot relationship. The estimated image Jacobian is updated repetitively and then its inverse is used to calculate the manipulator velocity without any singularity.

A link position tracking controller is formulated for an n-link, rigid,revolute, serially-connected robot. The controller generates torque commands tothe individual robot links based on adaptive estimates of the system parametersand measurements of only link positions. A filtering technique, based on thelink position signal, is used to alleviate the need for velocity measurements. Acomplete development of the controller is presented along with a proof ofsemiglobal asymptotic link position-velocity tracking performance. Experimentalvalidation of the proposed controller on the Integrated Motion Inc. (IMI)two-link direct drive robot is also presented. Several extensions to the basiccontroller are described that consider the use of fixed parameterestimates.

Most control algorithms for rigid-link electrically driven robots are given in joint coordinates. However, since the task to be accomplished is expressed in Cartesian coordinates, inverse kinematics has to be computed in order to implement the control law. Alternatively, one can develop the necessary theory directly in workspace coordinates. This has the disadvantage of a more complex robot model. In this paper, a robust control scheme is given to achieve exact Cartesian tracking without the knowledge of the manipulator kinematics and dynamics, actuator dynamics and nor computing inverse kinematics. The control design procedure is based on a new form of universal approximation theory and using Stone–Weierstrass theorem, to mitigate structured and unstructured uncertainties associated with external disturbances and actuated manipulator dynamics. It has been assumed that the lumped uncertainty can be modeled by linear differential equations. As the method is Model-Free, a broad range of manipulators can be controlled. Numerical case studies are developed for an industrial robot manipulator.

The need to meet demanding control requirements in increasingly complex dynamical control systems under significant uncertainties makes neural networks very attractive, because of their ability to learn, to approximate functions, to classify patterns and because of their potential for massively parallel hardware implementation. This paper proposes the use of artificial neural networks (ANN) as a novel approach to the control of robot manipulators. These are part of the general class of non-linear dynamic systems where non-linear compensators are required in the controller.

This paper addresses fixed-time trajectory tracking for a dual-arm free-floating space robot (FFSR) with the large initial errors and bounded uncertainty. A wrist-based trajectory planning method is improved by fixed-time stability to fast eliminate the error caused by singularity. Then, a novel low-chattering and global-nonsingular fixed-time terminal sliding mode control strategy is studied by state approaching angle and switching sliding mode; the practical fixed-reachable Lyapunov stability analysis is presented for a mechanical control system. In the end, the proposed trajectory planning method and controller are combined to improve the tracking accuracy of end-effector to the nanoscale. Simulation results validate the effectiveness of the proposed methodologies.

Trajectory tracking is one of the main control problems in the context of Wheeled Mobile Robots (WMRs). Control of underactuated systems has been focused by many researchers during past few years. In this paper, tracking control of a Tractor–Trailer Wheeled Mobile Robot (TTWMR) has been discussed. TTWMR includes a differential drive WMR towing a passive spherical wheeled trailer. Spherical wheels in contrast with standard wheels make the robot highly underactuated with severe non-linearities. Underactuation is due to the use of spherical wheeled trailer to increase robots' maneuverability and degrees of freedom. In fact, standard wheels are subjected to non-holonomic constraints due to pure rolling and non-slip conditions, which reduce robot maneuverability. In this paper, after introducing the robot, kinematics and kinetics models are obtained. Then, based on a physical intuition, a novel control algorithm is developed for the robot, i.e. Lyapunov-PID control algorithm. Subsequently, singularity avoidance of the proposed algorithm is discussed and the stability of the algorithm is analyzed. Finally, simulation and experimental results are presented which reveal the effectiveness of the proposed algorithm.

This paper presents a solution to the global adaptive partial state feedback control problem for rigid-link, flexible-joint (RLFJ) robots. The proposed tracking controller adapts for parametric uncertainty throughout the entire mechanical system while only requiring link and actuator position measurements. A nonlinear filter is employed to eliminate the need for link velocity measurements while a set of linear filters is utilized to eliminate the need for actuator velocity measurements. A backstepping control strategy is utilized to illustrate global asymptotic link position tracking. An output feedback controller that adapts for parametric uncertainty in the link dynamics of the robot manipulator is presented as an extension. Experimental results are provided as verification of the proposed controller.

In parallel mechanisms, singular configurations (singularities) have to be avoided during motion. All the singularities should be located in order to avoid them. Hence, relationships involving all the singular platform poses (singularity locus) and the mechanism geometric parameters are useful in the design of parallel mechanisms. This paper presents a new expression of the singularity condition of the most general mechanism (6-6 FPM) of a class of parallel mechanisms usually named fully-parallel mechanisms (FPM). The presented expression uses the mixed products of vectors that are easy to be identified on the mechanism. This approach will permit some singularities to be geometrically found. A procedure, based on this new expression, is provided to transform the singularity condition into a ninth-degree polynomial equation whose unknowns are the platform pose parameters. This singularity polynomial equation is cubic in the platform position parameters and a sixth-degree one in the platform orientation parameters. Finally, how to derive the expression of the singularity condition of a specific FPM from the presented 6-6 FPM singularity condition will be shown along with an example.

This paper approaches the design problem of suitable grippers for a robotic arm to pick up horticulture products. General considerations have been pointed out and main concepts and mechanical designs have been discussed and proposed by sketches and drawings for an easy understanding of “on-field” problems and feasible solutions. Pneumatic actuation has been proposed with suitable grasp force control by using commercial components. Specific attention has been focused on tomato horticulture for a practical design and prototyping of a device for laboratory experiments.