In this paper, accuracy and stiffness analysis of a 3-RRP spherical parallel manipulator (SPM) (Enferadi and Tootoonchi, A novel spherical parallel manipulator: Forward position problem, singularity analysis and isotropy design, Robotica, vol. 27, 2009, pp. 663–676) with symmetrical geometry is investigated. At first, the 3-RRP SPM is introduced and its inverse kinematics analysis is performed. Isotropic design, because of its design superiority, is selected and workspace of the manipulator is obtained. The kinematics conditioning index (KCI) is evaluated on the workspace. Global conditioning index (GCI) of the manipulator is calculated and compared with another SPM. Unlike traditional stiffness analysis, the moving platform is assumed to be flexible. A continuous method is used for obtaining mathematical model of the manipulator stiffness matrix. This method is based on strain energy and Castigliano's theorem. The mathematical model is verified by finite element model. Finally, using mathematical model, kinematics stiffness index (KSI), and global stiffness index (GSI) are evaluated.

The behavior of mechanical manipulators with backlash is analyzed. In order to acquire and study the signals an experimental setup is implemented. The signal processing capabilities of the wavelets are used for de-noising the experimental signals and the energy of the obtained components is analyzed. To evaluate the backlash effect upon the robotic system, it is proposed an index based on the pseudo phase plane representation. Several tests are developed that demonstrate the coherence of the results.

This study offers the solution of the end-effector trajectory tracking problem subject to state constraints, suitably transformed into control-dependent ones, for mobile manipulators. Based on the Lyapunov stability theory, a class of controllers fulfilling the above constraints and generating the mobile manipulator trajectory with (instantaneous) minimal energy, is proposed. The problem of manipulability enforcement is solved here based on an exterior penalty function approach which results in continuous mobile manipulator controls even near boundaries of state constraints. The numerical simulation results carried out for a mobile manipulator consisting of a non-holonomic unicycle and a holonomic manipulator of two revolute kinematic pairs, operating in a two-dimensional task space, illustrate the performance of the proposed controllers.

In this paper, a new balancing approach called “optimal balancing” is presented for open-chain robot manipulators based on open-loop optimal control. In fact, an optimal trajectory planning problem is outlined in which states, controls and the values of counterweights must be determined simultaneously in order to minimize the given performance index for a predefined point-to-point task. Optimal balancing method can be propounded beside the other methods such as unbalancing, static balancing and adaptive balancing, with this superiority that the objective criterion value obtained of proposed method is very lower than the objective criterion value obtained of other methods. For this purpose, the optimal control problem is extended to the case where the performance index, the differential constraints and the prescribed final conditions contain parameters. Using the fundamental theorem of calculus of variations, the necessary conditions for optimality are derived which lead to the optimality conditions associated with the Pontryagin's minimum principle and an additional condition associated with the constant parameters. By developing the obtained optimality conditions for the two-link manipulator, a two-point boundary value problem is achieved which can be solved with bvp4c command in MATLAB®. The obtained results show that optimal balancing in comparison with the previous methods can reduce the performance index significantly. This method can be easily applied to the more complicated manipulator such as a three degrees of freedom articulated manipulator.

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.

In this work, a novel hybrid control strategy is proposed for robust trajectory tracking control of robotic systems. The main interest of using hybrid design is to reduce the controller gains so as to reduce control efforts from the single model certainty equivalence principle- based adaptive controllers. For this purpose, we allow the parameter estimate of conventional adaptive control design to be switched into a model that best approximates the plant among a finite set of models. First, we uniformly divide the compact set of unknown parameters into a finite number of smaller compact subsets. Then we construct a finite set of candidate controller for each of these smaller compact subsets. The derivative of the Lyapunov function candidate is employed to identify a controller that closely approximates the plant at each instant of time. The idea of introducing hybrid approach in adaptive control framework is to achieve good transient tracking performance with smaller values of controller gains in the presence of large-scale parametric uncertainties. The proposed method is implemented and evaluated on two 3 degree-of-freedom Phantom Premimum™ 1.5 telerobotic systems to demonstrate the effectiveness of the theoretical development.

This paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only those features that contribute the most to the decreasing of the uncertainty ellipsoid volume of the SLAM system state will be chosen for the correction stage of the algorithm. The proposed features selection procedure restricts the number of features to be updated during the SLAM process, thus allowing real time implementations with non-reactive mobile robot navigation controllers. In addition, a Monte Carlo experiment is carried out in order to show the map reconstruction precision according to the Kullback–Leibler divergence curves. Consistency analysis of the proposed SLAM algorithm and experimental results in real environments are also shown in this work.

This paper addresses the problems of barrier coverage and sweep coverage in a corridor environment with a network of self-deployed mobile autonomous robotic sensors. Using the ideas of nearest neighbor rules and information consensus, we propose a decentralized control law for the robotic sensors to solve the coverage problems. Numerical simulations illustrate the effectiveness of the proposed algorithm. The results in this paper demonstrate that such simple motion coordination rules can play a significant role in addressing the issue of coverage in a mobile robotic sensor network.

Collision-free path planning for static robots is a demanding manifold of contemporary robotics research, vastly due to the growing industrial applications. In this paper, a novel ‘visibility map’-based heuristic algorithm is used to generate near-optimal safe path for a three-dimensional congested robot workspace. The final path is obtainable in terms of joint configurations, by considering the Configuration Space of the task space. The developed algorithm has been verified initially by considering representative 2D workspaces, cluttered with different obstacles with regular geometries and then after with the spatial endeavour. A case study reveals the effectiveness of the developed modules of the configuration space mapping, pertaining to a five degrees-of-freedom low payload articulated robot.

Hybrid actuation systems consist of two types of motors: constant velocity (CV) motor and servo (SV) motor. The CV motor can produce a large power but with a poor task flexibility. On the other hand, the SV motor has an excellent task flexibility but with a small power capacity. Combination of these two types of motors into a coherent driver architecture for machine systems is extremely promising, because they complement each other. Existing studies on the hybrid actuation or machine system usually employ two servo motors, one of which substitutes the CV motor. This treatment compromises the control accuracy for the trajectory tracking at the end-effector. This paper presents a study on a new controller for the hybrid machine that considers one SV motor and one CV motor and for trajectory tracking at the end-effector level. A comparison of this new controller with the controller we developed previously is provided. A five-bar mechanism with two degrees of freedom is employed for the illustration purpose.

This paper presents a method to generate humanoid gaits from a human locomotion pattern recorded by a motion capture system. Thirty seven reflective markers were fixed on the human subject skin in order to get the subject whole body motion. To reproduce the human gait, especially the toes and heel contacts, the front and back edges of the robot's feet are used as support at the start and the end of the double support phase. The balance of the robot is respected using the zero moment point (ZMP) criterion and confirmed by the simulation software OPENHRP (General Robotics, Inc®). First, the feet trajectory as well as the ZMP reference trajectory are defined from the motion of the robot controlled as a marionette with the measured human joint angles. Then a specific inverse kinematic (IK) algorithm is proposed to find the humanoid robot's joint trajectories respecting the constraints of balance, floor contacts, and joint limits. The studied motion presented in this paper is a human walking trajectory containing a start, a movement in a straight line, a stop, and a quarter turn. The method was developed to be easily used for human-like robots of different sizes, masses, and structures and has been tested on the robot HRP-2 (AIST, Kawada Industries, Inc®) and on the small-sized humanoid robot HOAP-3 (Fujitsu Automation Ltd®).