Human–robot interaction (HRI) is becoming more important nowadays. In this paper, a low-cost communication system for HRI is designed and implemented on the Scout robot and a robotic face. A hidden Markov model-based voice command detection system is proposed and a non-native database has been collected by Persian speakers, which contains 10 desired English commands. The experimental results confirm that the proposed system is capable to recognize the voice commands, and properly performs the task or expresses the right answer. Comparing the system with a trained system on the Julius native database shows a better true detection (about 10%).

Cooperative manipulators have uncertainties in their structure; therefore, an optimal sliding mode control method is derived from a combination of the sliding mode control (SMC) and the state-dependent Riccati equation (SDRE) technique. This proposed combination is applied to a class of non-linear closed-loop systems. One of the distinguished features of this control method is its robustness toward uncertainty. Due to the lack of optimality in the SMC method, in this paper, a robust and optimal method is presented by considering the SDRE in design of the sliding surface. Due to the fact that cooperative manipulators have been used for carrying loads, the percentage of load distributions between each manipulator has been derived to increase the dynamic load carrying capacity (DLCC). The proposed control structure is implemented on a Scout robot with two manipulators in cooperative mode, theoretically and practically using LabVIEW software; and the results were compared by considering the uncertainty in its structure. In comparison with the SDRE, the proposed method increased the DLCC almost 10% in the Scout case.

Influences of the tip mass, excitation mode of Frequency Modulated Atomic Force Microscope (FM-AFM) on the resonance frequency shift in force modulation (FM) mode are studied. Governing equations of motion are determined based on Timoshenko beam model with concentrated end mass. Approach point and base amplitude are set such that the FM-AFM remains just in FM mode. Either the linearized and nonlinear Derjaguin-Muller-Toporov (DMT) model are investigated. Then frequency shifts are determined for various interaction force regimes. It is showed the effect of tip mass on frequency shift is significant even for small tips. Nonlinear model shows lower frequency shifts in comparison with linearized model. It is showed that the amplitude of response is increased by increasing the tip mass and order of base excitation. Deviation of frequency shift between linearized and nonlinear solution are studied. It is declared that the error between linearized and nonlinear model is complicated. A deviation index is used for explaining behavior of error while tip mass and excitation mode are changed. It is showed, this index predicts the trend of error in all excitation modes and force cases. Behavior of system is linearizing by increasing the order of excitation, generally.

The main innovation of this paper is determining the dynamic load carrying capacity (DLCC) of a flexible joint manipulator (FJM) using a closed form nonlinear optimal control approach. The proposed method is compared with two closed loop nonlinear methods that are usually applied to robotic systems. As a new idea, DLCC of the manipulator is considered as a criterion to compare how well controllers perform point to point mission for the FJMs. The proposed method is compared with feedback linearization (FL) and robust sliding mode control (SMC) methods to show better performance of proposed nonlinear optimal control approach. An optimal controller is designed by solving a nonlinear partial differential equation named the Hamilton–Jacobi–Bellman (HJB) equation. This equation is complicated to solve exactly for complex dynamics so it is solved numerically using an iterative approximation combined with the Galerkin method. In the FL method, angular position, velocity, acceleration and jerk of links are considered as new states to linearize the dynamic equations. In the case of SMC, the dynamic equations of manipulator are changed to the standard form then the Slotine method is used to design the sliding mode controller. Two simulations are performed for a planar and a spatial Puma manipulator and performances of controllers are compared. Finally an experimental test is done on 6R manipulator and the Stereo vision method is used to determine the position and orientation of the end-effector.

Nowadays, with the growing use of atomic force microscope (AFM) nanorobots in the fabrication of nanostructures, research in this area has proliferated. A major limiting factor in the utilization of AFM nanorobots is the lack of real-time monitoring. Computer simulations have been widely used to improve the feasibility of nanoparticles pushing by means of the AFM-based nanorobots. In order to prevent damage to a cantilever during the nanoparticle displacement, it is necessary to come up with an algorithm that can check the feasibility of the manipulation operation with regards to the geometrical constraints of the cantilever and the considered path. The existing models for predicting the feasibility of manipulation processes are limited to 2D manipulation models (spherical nanoparticles), which are incapable of predicting the feasibility of 3D operations (lack of a comprehensive model of 3D cases). In this paper, to predict the feasibility of displacing cylindrical nanoparticles and to consider the path of motion (rough surface), a model has been proposed that can ensure the feasibility of the manipulation operation in advance. Finite element simulation has been employed to obtain the maximum load that can be withstood by a cantilever of a specific geometry. Also, the effect of surface roughness on the critical manipulation force has been explored. Ultimately, an algorithm has been presented for determining the feasibility of the manipulation operation by considering the particle motion path and the cantilever geometry.

In this paper, the process of pushing rough cylindrical micro/nanoparticles on a surface with an atomic force microscope (AFM) probe is investigated. For this purpose, the mechanics of contact involving adhesion are studied first. Then, a method is presented for estimating the real area of contact between a rough cylindrical particle (whose surface roughness is described by the Rumpf and Rabinovich models) and a smooth surface. A dynamic model is then obtained for the pushing of rough cylindrical particles on a surface with an AFM probe. Afterwards, the process is simulated for different particle sizes and various roughness dimensions. Finally, by reducing the length of the cylindrical particle, the simulation condition is brought closer to the manipulation condition of a smooth spherical particle on a rough substrate, and the simulation results of the two cases are compared. Based on the simulation results, the critical force and time of manipulation diminish for rough particles relative to smooth ones. Reduction in the aspect ratio at a constant cross-section radius and the radius of asperities (height of asperities based on the Rabinovich model) results in an increase in critical force and time of manipulation.

Optimal path planning of a closed loop cable robot, between two predefined points in presence of obstacles is the goal of this paper. This target is met by proposing a new method of optimal regulation for non linear systems while Dynamic Load Carrying Capacity (DLCC) of the robot is supposed as the related cost function. Feedback linearization is used to linearize the system while Linear Quadratic Regulator (LQR) is employed to optimize the DLCC of the system based on torque and error constraints. Obstacle avoidance for both the end-effector and cables is also considered by the aid of designing an adaptive local obstacle avoidance controller. As a result of linearized nature of the proposed optimal regulation and obstacle avoidance, fast calculation for real time applications is possible. Therefore, formulation of the optimal feedback linearization, together with calculating the DLCC of the robot based on the presented constraints is derived. Finally, a simulation study is performed to study the optimal dynamics and also the maximum DLCC of the cable robot in presence of obstacles. Simulation results are eventually compared with experimental tests conducted on IUST Cable Suspended Robot (ICaSbot) to verify the validity and efficiency of the proposed optimal controllers.

A sliding mode controller is designed to compensate for the flexibility uncertainties of a cable robot and improve its tracking performance. Of the most significant sources of these uncertainties are the elasticity of the cables and the flexibility of the joints. A favorable approach to improve the accuracy of the system is first to model the cable and joint flexibilities and then convert the model uncertainties into parametric uncertainties. Parametric uncertainties are the product of imprecise flexibility coefficients and are finally neutralized by a sliding mode controller. The flexibility in cables is modeled by considering the longitudinal vibration of the time-varying length cables. A simulation study is carried out to confirm the presented model and the positive effect of the designed controller. Then the impact of these uncertainties on the dynamic load carrying capacity (DLCC) of the robot is examined and compared for different cases. Finally, experimental tests are conducted on the IUST (Iran University of Science and Technology) cable-suspended robot to validate the presented theories and simulation results.

Due to the growing use of Atomic Force Microscope (AFM) nanorobots in the moving and manipulation of cylindrical nanoparticles (carbon nanotubes and nanowires) and the fact that these processes cannot be simultaneously observed, a computer simulation of the involved forces for the purpose of predicting the outcome of the process becomes highly important. So far, no dynamic 3D model that shows changes in these forces in the course of process implementation has been presented. An algorithm is used in this paper to show in 3D, the manner by which the dynamic forces vary in the mentioned process. The presented model can simulate the forces exerted on the probe tip during the manipulation process in three directions. Because of the nonlinearity of the presented dynamic model, the effective parameters have been also studied. To evaluate the results, the parameters of the 3D case (cylindrical model) are gradually reduced and it is transformed into a 2D model (disk model); and we can observe a good agreement between the results of the two simulations. Next, the simulation results are compared with the experimental results, indicating changes in lateral force. With the help of the offered dynamic model, the cantilever deformation and the forces interacting between probe tip and particle can be determined from the moment the probe tip contacts the nanoparticle to when the nanoparticle dislodges from the substrate surface.

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.

This paper presents a solution for optimal trajectory planning problem of robotic manipulators with complicated dynamic equations. The main goal is to find the optimal path with maximum dynamic load carrying capacity (DLCC). Proposed method can be implemented to problems of both motion along a specified path and point-to-point motion. Dynamic Programming (DP) approach is applied to solve optimization problem and find the positions and velocities that minimize a pre-defined performance index. Unlike previous attempts, proposed method increases the speed of convergence by using the sequential quadratic programming (SQP) formulation. This formulation is used for solving problems with nonlinear constraints. Also, this paper proposes a new algorithm to design optimal trajectory with maximum DLCC for both fixed and mobile base mechanical manipulators. Algorithms for DLCC calculations in previous works were based on indirect optimization method or linear programming approach. The proposed trajectory planning method is applied to a linear tracked Puma and the mobile manipulator named Scout. Application of this algorithm is confirmed and simulation results are compared with experimental results for Scout robot. In experimental test, results are obtained using a new stereo vision system to determine the position of the robot end-effector.

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.

In this paper, the combination of neural network (NN), proportional derivative (PD), and robust controller are used for determining the maximum load-carrying capacity (MLCC) of articulated robots, subject to both actuator and end-effector deflection constraints. The proposed technique is then applied to articulated robots, and MLCC is obtained for a given trajectory. In the practical simulations, it's impossible to determine the parameters of robot model exactly, so the trajectory tracking performance of the proportional integral derivative (PID) and computed torque methods significantly decrease. The PD control of robot has major problem, it cannot guarantee zero steady state error. For this reason, the NN controller with PD and robust controller are used. The multilayer neural network is also used to compensate gravity and friction effects. By using Lyapunov Direct Method it is shown that the stability of closed loop system would be guaranteed, if the weights of multilayer had certain learning rules. Standard back propagation algorithm is used as a learning algorithm to update the connection weights of the NN controller. The simulation results of the proposed adaptive robust neural network (ARNN) controller are compred with sliding mode and feedback linearization methods for flexible joint robot, and compared with open loop controller for 3D industrial robot. The obtained results assured the robustness and improvement in MLCC in the presence of uncertainties in dynamic model of the robot arm and external disturbances. In fact, adaptive robust NN controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.

Maximum load carrying capacity (MLCC) of flexible robot manipulators is computed based on closed-loop approach. In open-loop approach, controller is not considered, so the end effector deviation from the predefined path is significant and the accuracy constraint restrains the maximum payload before actuators go into saturation mode. In order to improve the MLCC, a method based on closed-loop strategy is presented. Since in this case the accuracy is improved the actuators constraint is not a major concern and full power of actuators can be used. Since controller can play an important role in improving the maximum payload, a sliding mode based partial feedback linearization controller is designed. Furthermore, a fuzzy variable layer is used in sliding mode design to boost the performance of the controller. However, the control strategy required measurements of elastic variables velocity that are not conveniently measurable. So a nonlinear observer is designed to estimate these variables. Stability analysis of the proposed controller and state observer are performed on the basis of Lyapunov's direct method. In order to verify the effectiveness of the presented method, simulation is done for a two-link flexible manipulator. The obtained maximum payload in open-loop and closed-loop cases is compared and the superiority of the method is illustrated and the results are discussed.

In this paper, finding the maximum load carrying capacity of mobile manipulators for a given two-end-point task is formulated as an optimal control problem. The solution methods of this problem are broadly classified as indirect and direct. This work is based on the indirect solution which solves the optimization problem explicitly. In fixed-base manipulators, the maximum allowable load is limited mainly by their joint actuator capacity constraints. But when the manipulators are mounted on the mobile bases, the redundancy resolution and nonholonomic constraints are added to the problem. The concept of holonomic and nonholonomic constraints is described, and the extended Jacobian matrix and additional kinematic constraints are used to solve the extra DOFs of the system. Using the Pontryagin's minimum principle, optimality conditions for carrying the maximum payload in point-to-point motion are obtained which leads to the bang-bang control. There are some difficulties in satisfying the obtained optimality conditions, so an approach is presented to improve the formulation which leads to the two-point boundary value problem (TPBVP) solvable with available commands in different softwares. Then, an algorithm is developed to find the maximum payload and corresponding optimal path on the basis of the solution of TPBVP. One advantage of the proposed method is obtaining the maximum payload trajectory for every considered objective function. It means that other objectives can be achieved in addition to maximize the payload. For the sake of comparison with previous results in the literature, simulation tests are performed for a two-link wheeled mobile manipulator. The reasonable agreement is observed between the results, and the superiority of the method is illustrated. Then, simulations are performed for a PUMA arm mounted on a linear tracked base and the results are discussed. Finally, the effect of final time on the maximum payload is investigated, and it is shown that the approach presented is also able to solve the time-optimal control problem successfully.

The aim of this paper is to determine the optimal trajectory and maximum payload of flexible link manipulators in point-to-point motion. The method starts with deriving the dynamic equations of flexible manipulators using combined Euler–Lagrange formulation and assumed modes method. Then the trajectory planning problem is defined as a general form of optimal control problem. The computational methods to solve this problem are classified as indirect and direct techniques. This work is based on the indirect solution of open-loop optimal control problem. Because of the offline nature of the method, many difficulties like system nonlinearities and all types of constraints can be catered for and implemented easily. By using the Pontryagin's minimum principle, the obtained optimality conditions lead to a standard form of a two-point boundary value problem solved by the available command in MATLAB®. In order to determine the optimal trajectory a computational algorithm is presented for a known payload and the other one is then developed to find the maximum payload trajectory. The optimal trajectory and corresponding input control obtained from this method can be used as a reference signal and feedforward command in control structure of flexible manipulators. In order to clarify the method, derivation of the equations for a planar two-link manipulator is presented in detail. A number of simulation tests are performed and optimal paths with minimum effort, minimum effort-speed, maximum payload, and minimum vibration are obtained. The obtained results illustrate the power and efficiency of the method to solve the different path planning problems and overcome the high nonlinearity nature of the problems.

A computational algorithm is developed to find a dynamic motion trajectory of a mobile manipulator with flexible links and joints that will allow the robot to carry a maximum load between two specified end positions. A compact form of the linearized state space dynamic equations is organized as well as constraint equations. Then, the problem of finding a maximum load carrying capacity on flexible mobile manipulators is formulated as a trajectory optimization problem.