An observation approach is proposed for determining the poses of the active/constrained wrench and the unified statics of some limited-DOF parallel manipulators (PMs) are studied systematically. First, a general PM model is constructed, and the unified inverse displacement is analyzed. Second, various types of acceptable legs are synthesized; the poses of the active/constrained wrench exerted on the various acceptable legs are determined by the observation approach. Third, a unified 6 × 6 Jacobina matrix and a unified statics equation are derived for solving active/constrained wrench of many limited-DOF PMs. Finally, two PMs are presented to illustrate this approach.

This paper deals with dynamic dimensional synthesis of the Delta robot using the pressure/transmission angle constraints. Two types of pressure/transmission angles are defined, with which the direct and indirect singularities can be identified in a straightforward manner. Two novel global dynamic metrics are proposed for minimisation, which are associated respectively with the inertial and centrifuge/Coriolis components of the driving torque. Various geometrical and performance constraints are taken into account in terms of workspace/machine volume ratio, pressure/transmission angles, etc. The effects of pressure/transmission angle constraints on the feasible domain of design variables are investigated in depth via an example, and a set of optimised dimensional parameters is obtained for achieving a good kinematic and dynamic performance throughout the entire task workspace.

This work proposes a teleoperation architecture for mobile robots in partially unknown environments under the presence of variable time delay. The system is provided with artificial intelligence represented by a probabilistic path planner that, in combination with a prediction module, assists the operator while guaranteeing a collision-free motion. For this purpose, a certain level of autonomy is given to the system. The structure was tested in indoor environments for different kinds of operators. A maximum time delay of 2s was successfully coped with.

The localization problem in mobile robotics can be defined as the search of the robot's coordinates in a known environment. If there is no information about the initial location, we are talking about global localization. In this work, we have developed an algorithm that solves this problem in a three-dimensional (3D) environment using evolutionary computation concepts. The method has been called RELF-3D and has many features that make it very robust and reliable: thresholding and discarding mechanisms, different cost functions, effective convergence criteria, and so on. The resulting global localization module has been tested in numerous experiments and the most important improvement obtained is the accuracy of the method, allowing its application in manipulation tasks.

In this work, accuracy enhancement through backlash elimination is considered. When a nonredundantly actuated parallel manipulator is subjected to a wrench while following a trajectory, required actuator torque switching (going from positive to negative or vice versa) may occur. If backlash is present in the actuation hardware for a manipulator, torque switching compromises accuracy. When in-branch redundant actuation is added, a pseudoinverse torque solution requires smaller joint torques, but torque switching may still occur. A method is presented where concepts of exploiting a nullspace basis of the joint torques are used to ensure that single sense joint torques can be achieved for the actuated joints. The same sense torque solutions are obtained using nonlinear optimization. The methodology is applied to several examples simulating parallel manipulators in machining applications.

Human posture prediction can often be formulated as a nonlinear multiobjective optimization (MOO) problem. The joint displacement function is considered as a benchmark of human performance measures. When the joint displacement function is used as the objective function, posture prediction is a MOO problem. The weighted-sum method is commonly used to find a Pareto solution of this MOO problem. Within the joint displacement function, the relative value of the weights associated with each joint represents the relative importance of that joint. Usually, weights are determined by trial and error approaches. This paper presents a systematic approach via an inverse optimization approach to determine the weights for the joint displacement function in posture prediction. This inverse optimization problem can be formulated as a bi-level optimization problem. The design variables are joint angles and weights. The cost function is the summation of the differences between two set of joint angles (the design variables and the realistic posture). Constraints include (1) normalized weights within limits and (2) an inner optimization problem to solve for joint angles (predicted posture). Additional constraints such as weight limits and weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 24 degree of freedom human upper body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected to run the experiment. The set of weights for the general seated posture prediction is obtained by averaging all weights for all subjects and all tasks. On the basis of obtained set of weights, the predicted postures match the experimental results well.

In this paper we propose an intuitive and practical grasp quality measure for grasping 3D objects with a multi-fingered robot hand. The proposed measure takes into account the object geometries through the concept of object wrench space. Physically, the positive measure value has a meaning of the minimum single disturbance that grasp cannot resist, while the negative measure value implies the minimum necessary helping force that restores a non-force-closure grasp into a force-closure one. We show that the measure value is invariant between similar grasps and also between different torque origins. We verify the validity of the proposed measure via simulations by using computer models of a three-fingered robot hand and polygonal objects.

In this paper, a Brain–Computer Interface (BCI) control approach for the assistive robotic system FRIEND is presented. The objective of the robot is to assist elderly and persons with disabilities in their daily and professional life activities. FRIEND is presented here from an architectural point of view, that is, as an overall robotic device that includes many subareas of research, such as human–robot interaction, perception, object manipulation and path planning, robotic safety, and so on. The integration of the hardware and software components is described relative to the interconnections between the various elements of FRIEND and the approach used for human–machine interaction. Since the robotic system is intended to be used especially by patients suffering from a high degree of disability (e.g., patients which are quadriplegic, have muscle diseases or serious paralysis due to strokes, or any other diseases with similar consequences for their independence), an alternative non-invasive BCI has been investigated. The FRIEND–BCI paradigm is explained within the overall structure of the robot. The capabilities of the robotic system are demonstrated in three support scenarios, one that deals with Activities of daily living (ADL) and two that are taking place in a rehabilitation workshop. The proposed robot was clinically evaluated through different tests that directly measure task execution time and hardware performance, as well as the acceptance of robot by end-users.

This paper proposes a novel design of a reconfigurable parallel kinematic manipulator used for a machine tool. After investigating the displacement and inverse kinematics of the proposed manipulator, it is found that the parasitic motions along x-, y-, and θz-axes can be eliminated. The system stiffness of the parallel manipulator is conducted. In order to locate the highest system stiffness, single and multiobjective optimizations are performed in terms of rotation angles in x- and y-axes and translation displacement in z-axis. Finally, a case study of tool path planning is presented to demonstrate the application of stiffness mapping. Through this integrated design synthesis process, the system stiffness optimization is conducted with Genetic Algorithms. By optimizing the design variables including end-effector size, base platform size, the distance between base platform and middle moving platform, and the length of the active links, the system stiffness of the proposed parallel kinematic manipulator has been greatly improved.

This work presents the CICABOT, a novel 3-DOF translational parallel manipulator (TPM) with large workspace. The manipulator consists of two 5-bar mechanisms connected by two prismatic joints; the moving platform is on the union of these prismatic joints; each 5-bar mechanism has two legs. The mobility of the proposed mechanism, based on Gogu approach, is also presented. The inverse and direct kinematics are solved from geometric analysis. The manipulator's Jacobian is developed from the vector equation of the robot legs; the singularities can be easily derived from Jacobian matrix. The manipulator workspace is determined from analysis of a 5-bar mechanism; the resulting workspace is the intersection of two hollow cylinders that is much larger than other TPM with similar dimensions.

An adaptive computed torque (ACT) controller in the task space is proposed for the trajectory tracking of a parallel manipulator with redundant actuation. The dynamic model, including the active joint friction, is established in the task space for the parallel manipulator, and the linear parameterization expression with respect to the dynamic and friction parameters is formulated. On the basis of the dynamic model, a new control law, which contains adaptive dynamics compensation, friction compensation, and tracking error elimination terms, is designed. After defining the state-space model of the error system, the parameter adaptation law is derived by using the Lyapunov method, and the convergence of the tracking error and the error rate is proved by using the Barbalat's lemma. The ACT controller is implemented in the trajectory tracking experiments of an actual 2-DOF parallel manipulator with redundant actuation, and the experiment results are compared with the computed torque controller.

In this work, a new translational robot formed with two different parallel manipulators with a common control point is introduced. An asymmetric parallel manipulator provides three translational degrees of freedom to the proposed robot while the orientation of the end-effector platform is kept constant by means of a Delta-like manipulator. An exact solution is easily derived to solve the forward displacement analysis while a semi-closed form solution is available for solving the inverse displacement analysis. The infinitesimal kinematics of the robot is approached by applying the theory of screws. Finally, a numerical example that consists of solving the inverse/forward displacement analysis as well as the forward acceleration analysis of the end-effector platform is presented. The example also includes the computation of the workspace and the direct/inverse singularities of the example.

To achieve desired configuration, a scheme for state adjustment of a redundant robot manipulator with no end-effector task explicitly assigned and referred to as a state-adjustment scheme is proposed in this paper. Owing to the physical limits in an actual robot manipulator, both joint and joint-velocity limits are incorporated into the proposed scheme for practical purposes. In addition, the proposed state-adjustment scheme is formulated as a quadratic program and resolved at the joint-velocity level. A numerical computing algorithm based on the conversion technique of the quadratic program to linear variational inequalities is presented to address the robot state-adjustment scheme. By employing the state-adjustment scheme, the robot manipulator can automatically move to the desired configuration from any initial configuration with the movement kept within its physical limits. Computer simulation and experimental results using a practical six-link planar robot manipulator with variable joint-velocity limits further verify the realizability, effectiveness, accuracy, and flexibility of the proposed state-adjustment scheme.

This paper presents a wheel–terrain interaction model, which enables efficient modeling of wheeled locomotion in soft soil and numerical simulations of off-road mobile robots. This modular model is derived based on wheel kinematics and terramechanics and the main focus is on describing the stress distributions along the wheel–terrain interface and the reaction forces exerted on the wheel by the soil. When the wheels are steered, the shear stresses underneath the wheel were modeled based on isotropic assumptions. The forces and torques contributed by the bulldozing effect of the side surfaces is also considered in the proposed model. Furthermore, the influence of grousers, commonly used on smaller mobile robots, was modeled by (1) averaging the normal pressures contributed by the grousers and the wheel concave portion, and (2) assuming that the shear phenomenon takes places along the grouser tips. By integrating the model with multibody system code for vehicle dynamics, simulation studies of various off-road conditions in three-dimensional environments can be conducted. The model was verified by using field experiment data, both for a single-wheel vehicle and a whole vehicle.

Kinematic reconfigurable mobile robots have the ability to change their structure to increase stability and decrease the probability of tipping over on rough terrain. If stability increases without decreasing center of mass height, the robot can pass more easily through bushes and rocky terrain. In this paper, an improved sample return rover is presented. The vehicle has a redundant rolling degree of freedom. A genetic algorithm utilizes this redundancy to optimize stability. Parametric motion equations of the robot were derived by considering Iterative Kane and Lagrange's dynamic equations. In this research, an optimal reconfiguration strategy for an improved SRR mobile robot in terms of the Force–Angle stability measure was designed using a genetic algorithm. A path-tracking nonlinear controller, which maintains the robot's maximum stability, was designed and simulated in MATLAB. In the simulation, the vehicle and end-effector paths and the terrain are predefined and the vehicle has constant velocity. The controller was found to successfully keep the end-effector to the desired path and maintained optimal stability. The robot was simulated using ADAMS for optimization evaluation.