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.

A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.

A high-level robot programming language constitutes a general purpose interface for accessing the basic functional capabilities of a robot. On the other hand, CAD facilities give the possibility of using a subset of these capabilities in an easier fashion. In this paper, we show how a robot programming language and CAD facilities can be combined to obtain a robot programming system satisfying the need for generality, and allowing an easy connection with the basic robot programming functions. Such a connection is based on a “complete” simulator providing facilities for executing robot control programs on a graphic display, for describing manipulation tasks using interactive graphic tools, for simulating the physical world and its perception through sensors, and for displaying three-dimensional scenes as shaded pictures.

Vibrations in robot action, both in the arm control system and arm itself, and in the transported load, are a serious problem in the development of high speed systems. The paper discusses the typical causes of vibration resulting from the motion command in path controlled robots and those due to the failure of a system to meet the specified command through power and performance limitations. Experimental results illustrating such phenomena are presented. Methods of reducing the magnitude or occurrence of such vibrations through the improved design of motion commands, which take account of the vibratory nature of the system and load, are presented.

An algorithm for the inference of the external behaviour model of an automaton is given. It uses a sequential learning procedure based on induction-contradiction-correction concepts. The induction is a generalization of relationships between automaton state properties, and the correction consists in a more and more accurate discrimination of the automaton state properties. These properties are defined from the input/output contradictory sequences which are discovered after the observed contradictions between successive predictions and observations.

The previous paper by the same authors (Robotica 2, Part 2, pp. 93–103 1984) was concerned with the application of logic programming to the generation of paths for robots. This paper is dealing with the PIGS system for robot problem-solving in generating plans for robots. The system avoids the overhead of generating a new set of facts for each state considered. A computer-type control is adopted not only to generate a simple sequence of actions to be performed by the robot, but also to enable the robot to follow an accurately defined path. A suitable plan of action of a robot problem-solving system is generated. The system is written in the PROLOG language and acts as a robot plan generator that is integrated with a path finding procedure in order to avoid collisions whilst plans are being implemented.

A prototype robot system for automated handling of flexible electrical wires of variable length is described. The handling process involves the selection of a single wire from a tray of many, grasping the wire close to its end with a robot manipulator, and either placing the end in a crimping press or, for tinning applications, dipping the end in a bath of molten solder. This system relies exclusively on the use of vision to identify the position and orientation of the wires prior to their being grasped by the robot end-effector. Two distinct vision algorithms are presented. The first approach utilises binary imaging techniques and involves object segmentation by thresholding followed by thinning and image analysis. An alternative general-purpose approach, based on more robust grey-scale processing techniques, is also described. This approach relies in the analysis of object boundaries generated using a dynamic contour-following algorithm. A simple Robot Control Language (RCL) is described which facilitates robot control in a Cartesian frame of reference and object description using frames (homogeneous transformations). The integration of this language with the robot vision system is detailed, and, in particular, a camera model which compensates for both photometric distortion and manipulator inaccuracies is presented. The system has been implemented using conventional computer architectures; average sensing cycle times of two and six seconds have been achieved for the grey-scale and binary vision algorithms, respectively.

Increasing productivity in manufacturing industry leads to a continuing shift in the balance between employment in ‘productive’ and in ‘service’ industries, the latter providing more employment at present in a ratio of about 70:30. This paper is primarily concerned with employment in manufacturing industry. Some have seen automation as a means of de-skilling jobs, but there is evidence of a gradual ‘upward’ shift of skill requirements in manufacturing industry: the unskilled workers are being eliminated and the skilled manual workers replaced by technicians with mental skills. This introduces the need for re-training and raises the question whether one can have a society in which there are no unskilled persons.

In this paper an optimal path planning method based on a new evolutionary algorithm is presented for higher order robotic systems. It is a combination of immune system and wavelet mutation. By increasing the system's dimensions, the complexity of algorithm grows linearly. The obtained results have been compared with other optimal path producing algorithms, and its excellence in terms of optimality has been proved. Strengths of this method are simplicity in large-scale path planning, being free of most of the common deadlocks in usual method, and ability to obtain more optimized results than other similar methods. The effectiveness of this approach on simulation case studies for a three-link planar robot and 5 degrees of freedom mobile manipulators as well as an experiment for a mobile robot called K-joniour is shown.

This paper presents an adaptation of Lowe's numerical model-based camera localisation algorithm to the domain of indoor mobile robotics. While the original method is straightforward and even elegant, it nonetheless exhibits certain weaknesses. First, due to an affine approximation, the method is not consistent with perspective projection especially when the dimensions of objects seen are large in comparison with their distances to the camera. Next, the non-linearity of equations makes convergence properties sensitive both to the initial solution estimate and to noise. By taking the specificity and exigency of the mobile robotics domain into account, a new formulation of this method is proposed in order to improve efficiency, accuracy and robustness in the presence of noisy data and variable initial conditions. According to this formulation, line correspondences are used rather than points, the number of degrees of freedom is reduced, the affine approximation is removed and rotation is uncoupled from translation. Test results with both synthetic and real images illustrate the improvements expected from theoretical modifications.

Robotic technology has been used in urological surgery since the late 1980's with the invention of robotic endoscopic prostate surgery. The major technological landmark has been the widespread introduction of the da Vinci master-slave manipulator which is now used in up to 60% of radical prostatectomy operations in the USA. These machines offer a number of tools to facilitate laparoscopic surgery though they require both a human console operator and a surgical team to connect the device to laparoscopic ports in the patient's abdomen. We outline the improved outcomes and future development of prostatectomy, cystectomy and pyeloplasty associated with robotic technology.

Assembly robots that use an active camera system for visual feedback can achieve greater flexibility, including the ability to operate in an uncertain and changing environment. Incorporating active vision into a robot control loop involves some inherent difficulties, including calibration, and the need for redefining the servoing goal as the camera configuration changes. In this paper, we propose a novel self-organizing neural network that learns a calibration-free spatial representation of 3D point targets in a manner that is invariant to changing camera configurations. This representation is used to develop a new framework for robot control with active vision. The salient feature of this framework is that it decouples active camera control from robot control. The feasibility of this approach is established with the help of computer simulations and experiments with the University of Illinois Active Vision System (UIAVS).

Assume that the end-effector of a serial mechanism with n-degrees of freedom arrives at the position p0, where it can change arbitrarily the direction of its movement. The physical conditions imply that this change in direction also alters its velocity. The kinematic performance ellipse represents the velocities according to the new direction of the system, and thus solving the problem of which direction will correspond to a maximum or minimum (principal directions) magnitude of the corresponding velocity. In this paper, a new procedure to calculate these principal directions is presented and contrasted with two of the most common procedures employed in the field. All three procedures are considered in some detail, in order to understand their underlying concepts and, therefore, gain a deeper understanding of the physical situation. They are all proved in standard examples.

Cladding through laser metal deposition is a promising application of additive manufacturing. On the one hand, industrial robots are increasingly used in cladding because they provide wide wrist reorientation, which enables manufacturing of complex geometries. On the other hand, limitations in robot dynamics may prevent cladding of sharp edges and large objects. To overcome these issues, this paper aims at exploiting the residual degrees of freedom granted by the cladding process for the optimization of the deposition orientation. The proposed method optimizes the robot head orientation along a predefined path while coping with kino-dynamic constraints as well as process constraints. Experimental tests and results are reported and used to validate the approach.

In this paper, design, dynamic, and control of the motors of a spatial cable robot are presented considering flexibility of the joints. End-effector control in order to control all six spatial degrees of freedom (DOFs) of the system and motor control in order to control the joints flexibility are proposed here. Corresponding programing of its operation is done by formulating the kinematics and dynamics and also control of the robot. Considering the existence of gearboxes, flexibility of the joints is modeled in the feed-forward term of its controller to achieve better accuracy. A two sequential closed-loop strategy consisting of proportional derivative (PD) for linear actuators in joint space and computed torque method for nonlinear end-effector in Cartesian space is presented for further accuracy. Flexibility is estimated using modeling and simulation by MATLAB and SimDesigner. A prototype has been built and experimental tests have been done to verify the efficiency of the proposed modeling and controller as well as the effect of flexibility of the joints. The ICaSbot (IUST Cable-Suspended robot) is an under-constrained six-DOF parallel robot actuated by the aid of six suspended cables. An experimental test is conducted for the manufactured flexible joint cable robot of ICaSbot and the outputs of sensors are compared with simulation. The efficiency of the proposed schemes is demonstrated.

The current contribution presents a new sinkage sensor specified for an unmanned ground vehicle to find the exact sinkage zone of a wheel interacting with the soil particles. This sensor will be wrapped around the wheel, and consequently, contact analog outputs will be used in soil deposition and bulldozing effect prediction. Furthermore, the new sensor will be used for a novel soil flow calculation estimating the total mass variation of the control volume of soil particles beneath the wheel. Accordingly, the spiral model simulating the displacement of the particle is implemented to calculate the soil deposition.

Gripper is one of the most important parts of robot because of contacting with workpieces directly and has attracted lots of research interests. However, the existing grippers are either simple in function or complex in structure. In this paper, we will propose a one-DOF gripper based on a compliant mechanism with four identical twofold-symmetric Bricard linkages. A mobile network with four identical twofold-symmetric Bricard linkages with particular design parameters is constructed at first. Kinematics, such as mobility, singularity, and folding performance, is then analyzed to show the potential of realizing the function of grasping. The result is demonstrated with a physical prototype. To simplify the fabricating process, a compliant mechanism of the network is designed and fabricated with a single polypropylene board, and the grasping function is realized by a cable-driven scheme. Some grasping experiments are carried out on different types of objects which shows that the proposed and fabricated four-figure gripper is simple in structure and has a great grasping function. The work provides a new idea for the design of grippers with low cost, simple structure, and rich functions.