In this paper, the use of algebraic and trigonometric splines for the trajectory planning of robot manipulators is discussed. First, the two methods are analyzed and compared in detail; then, a strategy, which involves a combined use of the two schemes to perform sudden changes in a predefined trajectory (e.g. in case of obstacle avoidance) is proposed. Results show that the main interest in using trigonometric splines lies especially in the task of connecting two separate pieces of cubic splines, as overshoots are significantly reduced, although the continuity of velocity, acceleration and (in case) jerk is guaranteed.

This paper presents a novel systematic approach to identify the dynamic parameters of robotic manipulators. A sequential identification procedure is first proposed to deal with the difficulties usually encountered with standard approaches. An all-accelerometer inertial measurement unit (IMU) is also suggested to estimate the joint velocities and accelerations, which are traditionally obtained by differentiating the joint positions. The IMU kinematics and the computation method for estimation joint motion from IMUs are provided. The proposed method yields promising results in improving the identification precision compared to conventional methods. Finally, practical experiments are conducted to validate the theoretical results.

When computer vision technique is used in robotics, robotic hand–eye calibration is a very important research task. Many algorithms have been proposed for hand–eye calibration. Based on these algorithms, we introduce a new hand–eye calibration algorithm in this paper, which employs the screw motion theory to establish a hand–eye matrix equation by using quaternion and gets a simultaneous result for rotation and translation by solving linear equations. The algorithm proposed in this paper has high accuracy and stable computational efficiency and can be understood easily. Both simulations and real experiments show the superiority of our algorithm over the comparative algorithms.

This paper treats, in a general way, the problem of mobile robot navigation in a totally unknown environment. The different aspects of this problem are dealt with one by one. We begin by introducing a simple method for perceiving and analyzing the robot's local environment based on a limited amount of distance information. Using this analysis as our base, we present a navigation algorithm containing different action modules; some of these actions use Fuzzy Logic. The results presented whether experimental or simulation show that our method is well adapted to this type of problem.

Assembly problems require that a robot with fewer actuated degrees of freedom manipulate an environment containing a greater number of unactuated degrees of freedom. From the perspective of control theory, these problems hold considerable interest because they are characterized by the presence of non-holonomic constraints that preclude the possibility of feedback stabilization. In this sense they necessitate the introduction of a hierarchical controller. This paper explores these issues in the simple instance when all of the pieces to be assembled are constrained to lie on a line. A hierarchical controller is devised for this problem and is shown to be correct: the closed loop system achieves any desired final assembly from all initial configurations that lie in its connected component in configuration space; the generated sequence of motions never causes collisions between two pieces. Further examination of this approach interprets the controller's mediation of conflicting subgoals as promoting an M-player game amongst the pieces to be assembled.

This paper proposes a new family of spatial 3-DOF (degree of freedom) parallel manipulators with two translational and one rotational DOFs. The manipulators in this family are the variations of the parallel manipulators, which are capable of very high rotational capability, introduced by X.-J. Liu, J. Wang, and G. Pritschow (“A new family of spatial 3-DoF fully parallel manipulators with high rotational capability,” Mech. Mach. Theory40(4), 475–494, 2005). However, compared with those old manipulators, the new parallel manipulators proposed here have the advantages of simpler kinematics and structure, easier manufacturing, and energy saving.

In this paper we develop a compilant system that permits robotic assembly of chamferless pieces. The idea is to absorb the positioning error between parts to be inserted by giving one of them a planar random movement. An actuator consisting of two axes (X and y) operated by an electromagnetic System is fitted to the work table; when its inputs are pseudo-random binary signais (P.R.B.S.) random motion is obtained. The trajectories of the actuator are analysed depending upon the P.R.B.S. parameters and a peg-in-a-hole assembly task is carried out. Experimental results show that large positioning errors can be compensated for chamferless insertions.

A stochastic ellipsoid modelling of repeatability is proposed for industrial manipulator robots. The covariance matrix of angular position is determined introducing the jump process, which reveals to be a first and second order stationary Gaussian process.

From this accurate covariance matrix, the stochastic ellipsoid theory gives the density of position in the workspace around the mean position. Hence the pose repeatability index can be computed in different locations. Computed and experimental repeatability are compared. Experimental repeatability variability is studied. A new “intrinsic repeatability index” is proposed. In conclusion, the modelling reflects well the location and load influence on the repeatability.

This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

Programming robotic assembly for industrial small-batch production is challenging; hence, it is vital to increase robustness and reduce development effort in order to achieve flexible robotic automation. A human who has made an assembly error will often simply undo the process until the error is undone and then restart the assembly. Conceptually, robots could do the same. This paper introduces a programming model that enables robot assembly programs to be executed in reverse. We investigate the challenges in running robot programs backwards and present a classification of reversibility characteristics. We demonstrate how temporarily switching the direction of program execution can be an efficient error recovery mechanism. Moreover, we demonstrate additional benefits arising from supporting reversibility in an assembly language, such as increased code reuse and automatically derived disassembly sequences. As a default approach to reversibility, we use program inversion and statement-level inversion of commands, but with a novel override option providing alternative sequences for asymmetric reverse actions. To efficiently program for this model, this paper introduces a new domain-specific language, SCP-RASQ (Simple C++ Reversible Assembly SeQuences). In initial experiments, where 200 consecutive assemblies of two industrial cases were performed, 18 of 22 errors were corrected automatically using only the trial-and-error capabilities that come from reverse execution.

We present AssistOn-Finger, a novel under-actuated active exoskeleton for robot-assisted tendon therapy of human fingers. The primary use for the exoskeleton is to assist flexion/extension motions of a finger within its full range, while decreasing voluntary muscle contractions helping to keep the tendon tension levels to stay within acceptable limits, avoiding gap formation or rupture of the suture. The device can also be employed to administer range of motion (RoM)/strengthening exercises. AssistOn-Finger is designed to be passively back-driveable, can cover the whole RoM of patients, and can do so in a natural and coordinated manner. In particular, the device employs human finger as an integral part of its kinematics and when coupled to a human operator, the parallel kinematic structure of exoskeleton supports three independent degrees of freedom, dictated by the kinematics of the human finger. Automatically aligning its joint axes to match finger joint axes, AssistOn-Finger can guarantee ergonomy and comfort throughout the therapy. The self-aligning feature also significantly shortens the setup time required to attach the patient to the exoskeleton. We present the kinematic type selection for the exoskeleton to satisfy the design requirements for tendon therapy applications, detail optimal dimensional synthesis of the device considering trade-offs between multiple design criteria and discuss implementation details of the exoskeleton. We also present feasibility studies conducted on healthy volunteers and provide statistical evidence on the efficacy of exoskeleton driven exercises in keeping the average muscle recruitment and the maximum tendon tension levels as low as human guided therapies.

This paper deals with the design and implementation of a visual kinematic control scheme for a redundant manipulator. The inverse kinematic map for a redundant manipulator is a one-to-many relation problem; i.e. for each Cartesian position, multiple joint angle vectors are associated. When this inverse kinematic relation is learnt using existing learning schemes, a single inverse kinematic solution is achieved, although the manipulator is redundant. Thus a new redundancy preserving network based on the self-organizing map (SOM) has been proposed to learn the one-to-many relation using sub-clustering in joint angle space. The SOM network resolves redundancy using three criteria, namely lazy arm movement, minimum angle norm and minimum condition number of image Jacobian matrix. The proposed scheme is able to guide the manipulator end-effector towards the desired target within 1-mm positioning accuracy without exceeding physical joint angle limits. A new concept of neighbourhood has been introduced to enable the manipulator to follow any continuous trajectory. The proposed scheme has been implemented on a seven-degree-of-freedom (7DOF) PowerCube robot manipulator successfully with visual position feedback only. The positioning accuracy of the redundant manipulator using the proposed scheme outperforms existing SOM-based algorithms.

Observer-based two-time scale robust control is proposed for free-flying flexible-joint space manipulators with unknown payload parameters and bounded disturbances. The dynamic equations of a free-flying space manipulator with two flexible revolute joints were derived by the momentum conservation law and the Lagrange equations. A flexibility compensator was introduced to make the equivalent joint stiffness large enough, which made traditional singular perturbation approach applicable. Then, a singular perturbation model was formulated and a reduced-order controller is proposed. This controller consisted of a slow sub-controller and a fast flexible-joint sub-controller. To the slow subsystem, a sliding observer based robust slow sub-controller was proposed. By optimal linear quadratic regulator method, the fast sub-controller was designed with the estimated velocity by linear observer. This fast sub-controller could stabilize the fast subsystem around the equilibrium trajectory created by the slow subsystem under the effect of the slow control. Finally the numerical simulations were carried out, which showed that elastic joint vibrations had been stabilized effectively and good tracking performances had been achieved.

This paper is organized in two parts. In Part I, the wrench polytope concept is presented and wrench performance indices are introduced for planar parallel manipulators (PPMs). In Part II, the concept of wrench capabilities is extended to redundant manipulators and the wrench workspace of different PPMs is analyzed. The end-effector of a PPM is subject to the interaction of forces and moments. Wrench capabilities represent the maximum forces and moments that can be applied or sustained by the manipulator. The wrench capabilities of PPMs are determined by a linear mapping of the actuator output capabilities from the joint space to the task space. The analysis is based upon properly adjusting the actuator outputs to their extreme capabilities. The linear mapping results in a wrench polytope. It is shown that for non-redundant PPMs, one actuator output capability constrains the maximum wrench that can be applied (or sustained) with a plane in the wrench space yielding a facet of the polytope. Herein, the determination of wrench performance indices is presented without the expensive task of generating polytopes. Six study cases are presented and performance indices are derived for each study case.

In this paper we consider the problem of coordinating multiple motion devices for welding. We focus on the problem of coordinating a three-axis positioning table and a six-axis manipulator. The problem is complex as there are nine axes involved and a number of permutations are possible which achieve the same movements of the weld torch. The system is redundant and the robot has singular configurations. As a result, manual programming of the robot system is rather difficult to complete.

Our approach to the coordination problem is based on a subdivision of tasks. The welding table is coordinated to align the weld point surface to be anti-parallel to the gravity direction. The six-axis robot is constrained to move the weld torch along the weld trajectory. Robot coordination is achieved by placing the positioning table in a good maneuverability position, i.e. far from its singular configurations and far from the motion limits of the six-axis arm and the motion limits of the track. While considering multiple criteria, including the welding orientation, a Genetic Algorithm was employed to globally optimize six relevant redundant degrees of the multiple robotic system for welding. The joint angles of the arm were generated by inverse kinematics. Our method of redundancy coordination is superior to pseudo-inverse techniques, for it is more global and accurate.

The robotic intervention has great potential in the rehabilitation of post-stroke patients to regain their lost mobility. In this paper, firstly, we present a design of a novel, 7 degree-of-freedom (DOF) upper limb robotic exoskeleton (u-Rob) that features shoulder scapulohumeral rhythm with a wide range of motions (ROM) compared to other existing exoskeletons. An ergonomic shoulder mechanism with two passive DOF was included in the proposed exoskeleton to provide scapulohumeral motion with corresponding full ROM. Also, the joints of u-Rob have more range of motions compared to its existing counterparts. Secondly, we propose a fractional sliding mode control (FSMC) to control u-Rob. Applying the Lyapunov theory to the proposed control algorithm, we showed the stability of it. To control u-Rob, FSMC has shown effectiveness to handle unmodeled dynamics (e.g. friction, disturbance, etc.) in terms of better tracking and chatter compared to traditional SMC.

The article proposes a nonlinear optimal control method for the model of the wheeled inverted pendulum (WIP). This is a difficult control and robotics problem due to the system’s strong nonlinearities and due to its underactuation. First, the dynamic model of the WIP undergoes approximate linearization around a temporary operating point which is recomputed at each time step of the control method. The linearization procedure makes use of Taylor series expansion and of the computation of the associated Jacobian matrices. For the linearized model of the wheeled pendulum, an optimal (H-infinity) feedback controller is developed. The controller’s gain is computed through the repetitive solution of an algebraic Riccati equation at each iteration of the control algorithm. The global asymptotic stability properties of the control method are proven through Lyapunov analysis. Finally, by using the H-infinity Kalman Filter as a robust state estimator, the implementation of a state estimation-based control scheme becomes also possible.

In this paper, a closed-form dynamic model of flexible manipulators is developed, based on the Newton–Euler formulation of motion equations of flexible links and on the adoption of the spatial vector notation. The proposed model accounts for two main innovations with respect to the state of the art: it is obtained in closed form with respect to the joints and modal coordinates (including the quadratic velocity terms) and motion equations of the whole manipulator can be computed for any arbitrary shape of the links and any possible link cardinality starting from the output of several commercial (finite element analysis) FEA codes. The Newton–Euler formulation of motion equations in terms of the joint and elastic variables greatly improves the simulation performances and makes the model suitable for real-time control and active vibration damping. The model has been compared with literature benchmarks obtained by the classical multibody approach and further validated by comparison with experiments collected on an experimental manipulator.

A new neural network (NN) control technique for robot manipulators is introduced in this paper. The fundamental robot control technique is the model-based computed-torque control which is subjected to performance degradation due. to model uncertainty. NN controllers have been traditionally used to generate a compensating joint torque to account for the effects of the uncertainties. The proposed NN control approach is conceptually different in that it is aimed at prefiltering the desired joint trajectories before they are used to command the computed-torque-controlled robot system (the plant) to counteract performance degradation due to plant uncertainties. In this framework, the NN controller serves as the inverse model of the plant, which can be trained on-line using joint tracking error. Several variations of this basic technique is introduced; Back-propagation training algorithms for the NN controller have been developed. Simulation results have demonstrated the excellent tracking performance of the proposed control technique.

In this paper, we develop a model predictive control (MPC) scheme for robots to follow a virtual leader. The stability of this control scheme is guaranteed by adding a terminal state penalty to the cost function and a terminal state region to the optimization constraints. The terminal state region is found by analyzing the stability. Also a terminal state controller is defined for this control scheme. The terminal state controller is a virtual controller and is never used in the control process. Two virtual leader-following formation models are studied. Simulations on different formation patterns are provided to verify the proposed control strategy.