In Part I a technique for the swing-up control of single inverted pendulum system is presented. The requirement is to swing-up a carriage mounted pendulum system from its natural pendent position to its inverted position. It works for all carriage balancing single inverted pendulum systems as the swing-up control algorithm does not require knowledge of the system parameters. Comparison with previous swing-up controls shows that the proposed swing-up control is simpler, eaiser. more efficient, and more robust.

In Part II the technique is extended to the case of the swing-up control of double inverted pendulum systems. Use is made of a novel selective partial-state feedback control law. The nonlinear, open-loop unstable, nonminimum-phase. and interactive MIMO pendulum system is actively linearised and decoupled about a neutrally stable equilibrium by the partial-state feedback control. This technique for swing-up control is not at all sensitive to uncertainties such as modelling error and sensor noise, and is both reliable and robust.

A new type of microrobot is described. Its simple and compact design is believed to be of promise in the microrobotics field. Stepping motion allows speeds up to 4mm/s. Resolution smaller than 10 nm is achievable. Experiments in an open-loop motion demonstrated a repeatability better than 50µm on a 10 mm displacement at an average speed of 0.25 mm/s. A position feedback based on a microvision system will be developed in order to achieve a submicron absolute position accuracy.

In this paper, a dynamic task allocation and controller design methodology for cooperative robot teams is presented. Fuzzy-logic-based utility functions are derived to quantify each robot's ability to perform a task. These utility functions are used to allocate tasks in real time through a limited lookahead control methodology partially based on the basic principles of discrete event supervisory control theory. The proposed controller design methodology accommodates flexibility in task assignments, robot coordination, and tolerance to robot failures and repairs. Implementation details of the proposed methodology are demonstrated through a warehouse patrolling case study.

In this research work, the maximum Cartesian workspace of a Tricept parallel robot with two rotational and one translational degrees of freedom was investigated. Generally, the Cartesian workspace identifies the maximum size of a work-piece, specifying its cubic x, y and z dimensions, on which the milling machine could perform operations. However, the workspace of a robot can be considered in its task space, such as ψ × θ × z for the Tricept Parallel Kinematic Mechanism (PKM). A novel homogeneous Jacobian matrix which transforms joint space velocity vector into end-effector Cartesian velocity vector has been generated named as a Cartesian Jacobian matrix. Using the indices derived from the homogeneous Cartesian Jacobian matrix, i.e. the maximum singular values and local conditioning indices, the manipulator is designed to reach the Cartesian workspace with rapid positioning rates as well as with singularity avoidance.

A predictive display method and man-virtual robot interaction based on augmented reality are applied to control a telerobot. We first discuss the process of the augmented reality environment development. Then, we present the advantages of predictive display. Simulation of virtual robot's tasks in the augmented environment improves the safety of the telerobot when it executes the planned tasks. In addition, the immediate feedback from the virtual robot avoids the exacerbation of maneuverability caused by time-delay. For a more natural operation process, we apply multi man-virtual robot interactive methods. Lastly, the experiment of pick & place is conducted to validate the system.

In this paper, we propose and implement an advanced manipulation framework that enables parametric learning of complex action trajectories along with their haptic feedback profiles. Our framework extends Dynamic Movement Primitives (DMPs) method with a new parametric nonlinear shaping function and a novel force-feedback coupling term. The nonlinear trajectories of the action control variables and the haptic feedback trajectories measured during execution are encoded with parametric temporal probabilistic models, namely parametric hidden Markov models (PHMMs). PHMMs enable autonomous segmentation of a taught skill based on the statistical information extracted from multiple demonstrations, and learning the relations between the model parameters and the properties extracted from the environment. Hidden states with high-variances in observation probabilities are interpreted as parts of the skill that could not be reliably learned and autonomously executed due to possibly uncertain or missing information about the environment. In those parts, our proposed force-feedback coupling term, which computes the deviation of the actual force feedback from the one predicted by the force-feedback PHMM, acts as a compliance term, enabling a human to scaffold the ongoing movement trajectory to accomplish the task. Our method is verified in a number of tasks including a real pick and place task that involves obstacles of different heights. Our robot, Baxter, successfully learned to generate the trajectory taking into the heights of the obstacles, move its end effector stiffly (and accurately) along the generated trajectory while passing through apertures, and allow human–robot collaboration in the autonomously detected segments of the motion, for example, when the gripper picks up the object whose position is not provided to the robot.

We have suggested a novel approach to autonomously navigate a full sized autonomous vehicle that separately treats vehicle control and obstacle detection. In this paper we discuss the vehicle control that has enabled our autonomous vehicle to travel at speeds upto 20mph. We point out the limitations of existing schemes that restrict their consideration to kinematic models and show that it is possible to obtain an increase in performance through the use of approximate dynamical models that capture first–order effects. Our approach combines such a modeling philosophy with accurate feedback in world coordinates from sensors that have only recently become available. Experimental results of our implementation on NavLab, a modified van at CMU, are presented.

The constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.

This paper presents an optimal trajectory planning method for industrial robots. The paper specially focuses on the applications of path tracking. The problem is to plan the trajectory with a specified geometric path, while allowing the position and orientation of the path to be arbitrarily selected within the specific ranges. The special contributions of the paper include (1) an optimal path tracking formulation focusing on the least time and energy consumption without violating the kinematic constraints, (2) a special mechanism to discretize a prescribed path integration for segment interpolation to fulfill the optimization requirements of a task with its constraints, (3) a novel genetic algorithm (GA) optimization approach that transforms a target path to be tracked as a curve with optimal translation and orientation with respect to the world Cartesian coordinate frame, (4) an integration of the interval analysis, piecewise planning and GA algorithm to overcome the challenges for solving the special trajectory planning and path tracking optimization problem. Simulation study shows that it is an insufficient condition to define a trajectory just based on the consideration that each point on the trajectory should be reachable. Simulation results also demonstrate that the optimal trajectory for a path tracking problem can be obtained effectively and efficiently using the proposed method. The proposed method has the properties of broad adaptability, high feasibility and capability to achieve global optimization.

This paper presents a novel time-optimal motion planning strategy for a mobile robot with kinematic constraints. The method works in environments in presence of obstacles, without needing to generate the configuration space for the robot. Further, it derives a minimum time first derivative smooth path, as opposed to a minimum distance path which is commonly given by various present solution techniques. The problem is solved in three stages: (i) A reduced visibility graph for a point object is obtained. (ii) The reduced visibility graph is converted into a feasible reduced visibility graph accounting for the size and kinematic constraints of the robot. (iii) The A* algorithm is used to search the feasible reduced visibility graph with the cost function being the time of travel, to obtain a safe, time-optimal, smooth path. The algorithm runs in polynomial time. The method has been tested in computer simulations and test results are presented

This paper presents a robust adaptive impedance controller for robot manipulators using function approximation techniques (FAT). Recently, FAT-based robust impedance controllers have been presented using Fourier series expansion for uncertainty estimation. In fact, sinusoidal functions can approximate nonlinear functions with arbitrary small approximation error based on the orthogonal functions theorem. The novelty of this paper in comparison with previous related works is that the number of required regressor matrices in this paper has been reduced. This superiority becomes more dominant when the manipulator degrees of freedom (DOFs) are increased. First, the desired signals for motor currents are calculated, and then the desired voltages are obtained. In the proposed approach, only a simple model of the actuator and manipulator dynamics is used in the controller design and all the rest dynamics are treated as external disturbance. The external disturbances can then be approximated by Fourier series expansion. The adaptation laws for Fourier series coefficients are derived from a Lyapunov-based stability analysis. Simulation results on a 2-DOF planar robot manipulator including the actuator dynamics indicate the efficiency of proposed method.

An autonomous robot must obtain information about its surroundings to accomplish multiple tasks that are greatly improved when this information is efficiently incorporated into a map. Some examples are navigation, manipulation, localization, etc. This mapping problem has been an important research area in mobile robotics during last decades. It does not have a unique solution and can be divided into multiple sub-problems. Two different aspects of the mobile robot mapping problem are addressed in this work. First, we have developed a Differential Evolution-based scan matching algorithm that operates with high accuracy in three-dimensional environments. The map obtained by an autonomous robot must be consistent after registration. It is basic to detect when the robot is navigating around a previously visited place in order to minimize the accumulated error. This phase, which is called loop detection, is the second aspect studied here. We have developed an algorithm that extracts the most important features from two different three-dimensional laser scans in order to obtain a loop indicator that is used to detect when the robot is visiting a known place. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features are values that describe several numerical properties of the measurements: volume, average range, curvature, etc. Both algorithms have been tested with real data to demonstrate that these are efficient tools to be used in mapping tasks.

A class of three-legged modular reconfigurable parallel robots is designed and constructed for precision assembly and light machining tasks by using standard active and passive joint modules in conjunction with custom designed links and mobile platforms. Since kinematic errors, especially the assembly errors, are likely to be introduced, kinematic calibration becomes particularly important to enhance the positioning accuracy of a modular reconfigurable robot. Based on the local frame representation of the Product-Of-Exponentials (Local POE) formula, a self-calibration method is proposed for these three-legged modular reconfigurable parallel robots. In this method, both revolute and prismatic joint axes can be uniformly expressed in twist coordinates by their respective local (body) frames. Since these local frames can be arbitrarily defined on their corresponding links, we are able to calibrate them, and yet retain the nominal local description of their respective joints, i.e., the nominal twist coordinates and nominal joint displacements, to reflect the actual kinematics of the robot. The kinematic calibration thus becomes a procedure of fine-tuning the locations and orientations of the local frames. Using mathematical tools from differential geometry and group theory, an explicit linear calibration model is formulated based on the leg-end distance errors. An iterative least-square algorithm is employed to identify the error parameters. A simulation example of calibrating a three-legged (RRRS) modular parallel robot shows that the robot kinematics can be fully calibrated within two to three iterations.

One of the most important specifications associated with a robot manipulator is repeatability. Unfortunately, there are no standard procedures for determining or specifying the repeatability of a robot. This situation has resulted in a good deal of confusion when attempting to use a published repeatability specification to determine if a robot will meet the requirements of an application.

The purpose of this paper, therefore, is to propose a systematic procedure for the determination and specification of robot repeatability. To accomplish this, three aspects of repeatability analysis are examined. First, a method of collecting data to study repeatability is discussed. A data acquisition system is described as well as an associated test procedure. Next, a procedure for utilizing the acquired data to develop a repeatability specification for a robot is proposed. This procedure results in a specification of the robot's ability to orient objects as well as the classical specification for positioning repeatability. A methodical approach to determining where in the workspace the repeatability test should be made is also addressed. Finally, an approach is presented whereby the data collected during the repeatability test may be used to determine which joints contribute most to the variations in robot repeatability.

This paper presents adaptive impedance controllers with adaptive sliding mode friction compensation for anthropomorphic artificial hand. A five-fingered anthropomorphic artificial hand with multi-sensory and Field-Programmable Gate Arra (FPGA)-based control hardware and software architecture is designed to fulfill the requirements of the grasping force controller. In order to improve the force-tracking precision, the indirect adaptive algorithm was applied to estimate the parameters of the environment. The generalized momentum-based disturbance observer was applied to estimate the contact force from the torque sensor. Based on the sensors of the finger, an adaptive sliding mode friction compensation algorithm was utilized to improve the accuracy of the position control. The performances of the force-tracking impedance controller and position-based joint impedance control for the five-fingered anthropomorphic artificial hand are analyzed and compared in this paper. Furthermore, the performances of the force-tracking impedance controller with environmental parameters adaptive estimation and without environmental parameters estimation are analyzed and compared. Experimental results prove that accurate force-tracking and stable torque/force response under uncertain environments of unknown stiffness and position can be achieved with the proposed adaptive force-tracking impedance controller with friction compensation on five-finger artificial hand.

This paper proposes an algorithm for managing redundant measurements provided by a stereo multi-camera system to achieve accurate visual tracking of a moving object. Selfocclusion, different visual resolution zones and optimal selection of redundant measurements are some of the problems addressed. The algorithm uses the extended Kalman filter embedded in a computational efficient pose estimation procedure based on Binary Space Partitioning Tree geometric modelling of 3D objects. Experimental results are presented for the case of an object moving in the visual space of two fixed cameras.

This paper introduces the design and the optimization of a probe holder robot for tele-echography applications. To define its kinematic architecture, an approach based on motion capture of an expert's gestures during ultrasound examinations was proposed. The medical gestures analyzed consisted of ultrasound probe movements and were used to characterize the kinematic specifications of the proposed manipulator. The selected architecture was a Spherical Parallel Mechanism (SPM) with 3 degrees of freedom (DoF) and its optimal synthesis was performed using real-coded Genetic Algorithms (GA). The optimization criteria and constraints were established thanks to the collaboration of medical experts and were successively formulated and solved using mono-objective and multi-objective functions.

Accurate contour tracking is one of the main tasks in modern manufacturing processes. By considering coupling effects among multiple axes, this paper proposes a cross-coupled proportional-integral-derivative (PID) control developed in position domain, and the controller is applied to a multi-axis computer numerical control (CNC) machine for contour tracking performance improvement. Stability analysis is conducted for the developed position domain cross-coupled PID control using the Lyapunov method, and guidelines for the selection of control gains are provided. The contour tracking performance are improved compared to an equivalent time domain controller, since the reference axis in position domain control does not contribute any error to the overall contouring error of the system. Simulation results demonstrate the effectiveness of cross-coupled PID position domain control for both linear and circular contour tracking, and prove the robustness of the controller to deal with random disturbances. It also shows that position domain cross-coupled PID control provides better contour tracking performance over position domain PID control and the equivalent time domain PID control.

This paper reports the design of sliding-mode control laws for controlling multiple small-sized autonomous helicopters in arbitrary formations. Two control schemes, which are required for defining arbitrary three-dimensional formation meshes, are discussed. In the presented leader–follower formation control schemes, each helicopter only needs to receive motion information from at most two neighboring helicopters. A nonlinear six-degree-of-freedom dynamic model has been used for each helicopter. Four control inputs, the main and the tail rotor thrusts, and the roll and pitch moments, are assumed. Parameter uncertainty in the dynamic model and wind disturbance are considered in designing the controllers. The effectiveness and robustness of these control laws in the presence of parameter uncertainty in the dynamic model and wind disturbances are demonstrated by computer simulations.

An approach for calculating the maximum possible absolute values of joint velocities or generalized reactions in a leg of a parallel mechanism has been considered in this paper. The Jacobian analysis and the Screw theory-based methods have been used to acquire the result. These values are calculated for the “worst” directions of the external load or end-effector’s velocity for each leg. The feasibility of using these parameters as the measures of closeness to different types of parallel mechanism singularity is discussed. Further, how this approach is related to the state-of-the-art methods has been illustrated. The key aspect of the discussed approach is that the normalization of vectors or screws is carried out separately for angular and linear components. One possible advantage of such an approach is that it deals only with the kinematic and statics of the mechanism while still providing physically meaningful and practically applicable measures. Case studies of a 3-Degrees Of Freedom translational parallel mechanism and a planar parallel mechanism are presented for illustration and comparison.