We present a visual feedback method for closed loop control of automated microassembly. A CAD model based multi-camera visual tracking system that is well suited for flexible automation and assembly of complex 3D geometries was developed. The system is capable of providing six degree-of-freedom pose feedback on the observed micro-components in real-time (30 Hz). Using CAD models of the observed objects, a complete description of the observed scene, including the effect of occlusions, is available and dependence on distinctive visual features such as fudicial marks is avoided.

A new approach to the control of indirect simultaneous positioning of deformable objects is presented. Many manufacturing processes that deal with deformable objects such as clothes and rubber sheets involve a positioning of multiple points on a deformable object. The multiple points should be guided simultaneously to the desired locations. Moreover, these positioned points cannot be manipulated directly. This operation is referred to as indirect simultaneous positioning. In this article, we will propose a new control law for indirect simultaneous positioning of a deformable object based on its uncertain model and will show the robustness of the proposed control law. First, a simplified physical model of a deformable object is developed for its positioning operation. Second, indirect simultaneous positioning of an extensible object is formulated. Based on a linearized model of an extensible object, we will propose a novel control law for indirect simultaneous positioning. Next, we will prove the robustness of the proposed control law theoretically. Finally, experimental results will show the robustness of our proposed control law against the discrepancy between a real fabric and its uncertain model.

Psychophysiological measurements, which serve as objective indicators of psychological state, have recently been introduced into human–robot interaction. However, their usefulness in haptic interaction is uncertain, since they are influenced by physical workload. This study analyses psychophysiological responses to a haptic task with three different difficulty levels and two different levels of physical load. Four physiological responses were recorded: heart rate, skin conductance, respiratory rate and skin temperature. Results show that mean respiratory rate, respiratory rate variability and skin temperature show significant differences between difficulty levels regardless of physical load and can be used to estimate cognitive workload in haptic interaction.

Recent development of sensor knowledge representation by the use of a certainty grid has been extensive and shown the usefulness of the grid-based concept for robot navigation.

Yet the methodology was not perfect. This paper introduces the Bayesian formula into the certainty grid representation to overcome some difficulties of ad hoc formula that has been the only way of updating the grids. The complete derivation of the proposed updating formula is given and proved to be able to accurately identify the simulated world. Also, the paper suggests two updating models: context-sensitive and context-free. Both of them were shown to be usable through simulation in real world modeling.

In recent years hexagonal hexapod robots gained the interest of international research community. The aim of this paper is twofold. First, after summarizing all known gaits of such robots, we introduce some improvements both for normal conditions and for fault tolerance. Then we show the advantages of hexagonal hexapod robots over rectangular ones by comparing different gaits from theoretical and experimental points of view. Stability, fault tolerance, turning ability, and terrain adaptability are analyzed. For reaching these aims we also introduce a robot kinematics that considers at the same time supporting and transferring legs. The trajectories of feet are described as well. Finally, single leg stride selection is studied for side wave and for kick-off gaits to optimize walking ability and energy management.

The theoretical results presented herein have been validated with experiments conducted on a prototype of the Novel Robotics System for Planetary Exploration (Rovetta et al., “New Robot Concepts for Mars Soil Exploration: Mechanics and Functionality,” ASTRA 2004, Eighth ESA Workshop on Advanced Space Technologies for Robotics and Automatian, Nordwijk, The Netherlands Nov. 2–4, 2004) (NOROS), developed by Politecnico di Milano and Beijing University of Astronautics and Aeronautics, and the results are summarized in this paper.

With the increasing demands for versatile robotic platforms capable of performing a variety of tasks in diverse and uncertain environments, the needs for adaptable robotic structures have been on the rise. These requirements have led to the development of modular reconfigurable robotic systems that are composed of a numerous self-sufficient modules. Each module is capable of establishing rigid connections between multiple modules to form new structures that enable new functionalities. This allows the system to adapt to unknown tasks and environments. In such structures, coupling between modules is of crucial importance to the overall functionality of the system. Over the last two decades, researchers in the field of modular reconfigurable robotics have developed novel coupling mechanisms intended to establish rigid and robust connections, while enhancing system autonomy and reconfigurability. In this paper, we review research contributions related to robotic coupling mechanism designs, with the aim of outlining current progress and identifying key challenges and opportunities that lay ahead. By presenting notable design approaches to coupling mechanisms and the most relevant efforts at addressing the challenges of sensorization, misalignment tolerance, and autonomous reconfiguration, we hope to provide a useful starting point for further research into the field of modular reconfigurable robotics and other applications of robotic coupling.

In this paper, an adaptive control scheme is proposed for an n-link rigid robot manipulator without using the regressor. The robot is firstly modeled as a set of second-order nonlinear differential equations with the assumption that all of the matrices in that model are unavailable. Since these matrices are time-varying and their variation bounds are not given, traditional adaptive or robust designs do not apply. The function approximation technique (FAT) is used here to represent uncertainties in some finite linear combinations of orthonormal basis. The dynamics of the output tracking can thus be proved to be a stable first order filter driven by function approximation errors. Using the Lyapunov stability theory, a set of update laws is derived to give closed loop stability with proper tracking performance. Experiments are also performed on a 2-D robot to test the efficacy of the proposed scheme.

The Rehabilitation Technologies Division of Applied Resources Corp. (RTD-ARC) has engaged in a Phase I effort to commercialize a robotic bi-manual therapy machine for use in stroke rehabilitation, in cooperation with the VA Rehabilitation R&D Center in Palo Alto. The robotic therapy device, called ARCMIME here in order to differentiate it from its clinical predecessor, has the potential to improve rehabilitation outcomes significantly for individuals who have upper limb impairments due to stroke and other brain injuries.

This paper describes design considerations and clinical outcomes with regards to the Phase I system. It was found that the kinematically simpler system adequately replicated the data outcomes of the more sophisticated PUMA-based experimental test rig.

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.

As researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative aspects of the group as a whole, in addition to electromechanical considerations of individual robots. This paper reviews the state-of-the-art of mobile multi-robotic system research, with an emphasis on the confluence of mapping, localization and motion control of robotic system. Methods that compose these three topics are presented, including areas of overlap, such as integrated exploration and simultaneous localization and mapping. From these methods, an analysis of benefits, challenges and tradeoffs associated with multi-robotic system design and use are presented. Finally, specific applications of multi-robotic systems are also addressed in various contexts.

In this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.

In this paper, a multilayer feedforward neural network (NN) is designed and trained, for human–robot collisions detection, using only the intrinsic joint position sensors of a manipulator. The topology of one NN is designed considering the coupled dynamics of the robot and trained, with and without external contacts, by Levenberg–Marquardt algorithm to detect unwanted collisions of the human operator with the manipulator and the link that is collided. The proposed approach could be applied to any industrial robot, where only the joint position signals are available. The designed NN is compared quantitatively and qualitatively with an NN, where both the intrinsic joint position and the torque sensors of the manipulator are used. The proposed method is evaluated experimentally with the KUKA LWR manipulator, which is considered as an example of the collaborative robots, using two of its joints in a planar horizontal motion. The results illustrate that the developed system is efficient and fast to detect the collisions and identify the collided link.

In this paper, the optimization of architectural parameters for a class of translational parallel kinematic machine (PKM) is performed with the particle swarm optimization (PSO) to achieve the best accuracy characteristics. The conventional error transformation matrix (ETM) is derived based on the differentiation of kinematic equations, and a new error amplification index (EAI) over a usable workspace is proposed as an error performance index for the optimization. To validate the efficiency of the PSO method, both the traditional direct search method and the genetic algorithm (GA) are implemented as well. The simulation results not only show the advantages of PSO method for the architectural optimization, but also reveal the necessity to introduce the EAI for the optimal design. And the results are valuable for architectural design of the PKM for machine tool applications.

This paper introduces a three degree of freedom XYZ Micromanipulator (XYZM) that is fabricated in the $x$-$y$ plane and positions components in the $x$, $y$, and $z$ directions using three independent linear inputs. The mechanism positions components on a platform using three legs, each composed of a slider mechanism and a parallelogram mechanism.

Three versions of the XYZM were fabricated and tested using surface micromachining processes: the rigid-body, offset, and compliant XYZM. Slider displacements of 45 micrometers result in a predicted out-of-plane displacement of 188 micrometers for the rigid-body XYZM, 205 micrometers for the offset XYZM, and 262 micrometers for the compliant XYZM.

A novel method of Zero-Moment-Point (ZMP) compensation is proposed to improve the stability of locomotion of a biped, which is subjected to disturbances. A compensating torque is injected into the ankle-joint of the foot of the robot to improve stability. The value of the compensating torque is computed from the reading of the force sensors located at the four corners of each foot. The effectiveness of the method is verified on a humanoid robot, MANUS-I. With the compensation technique, the robot successfully rejected disturbances in different forms. It carried an additional weight of 390 gm (17% of body weight) while walking. Also, it walked up a 10° slope and walked down a 3° slope.

In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

A brand-new paradigm of robots–quantum robots–is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O($N\sqrt N)$. Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.

A prototype robot for picking citrus is described which utilized real-time, color machine vision to vision-servo the robot on a targeted fruit. A programming technique is presented which simplified development of the task-level, robot control program. An economic evaluation of robotic harvesting in Florida determined that robotic harvesting would be approximately 50 percent more expensive than conventional hand harvesting. Harvest inefficiency was identified as the most influential factor affecting robotic harvesting economics. Field trial performance is summarized and the potential of commercial robotic harvesting discussed.

This paper presents a novel scheme for achieving attitude control of a tumbling combination system in the post-capture phase of a tethered space robot (TSR). Given the combination rotation characteristics, tether force is applied to provide greater control torques for stabilising the attitude. The proposed control scheme involves two attitude controllers, which coordinate the controller of the tether force and thruster force and the controller of single thruster force. The numerical simulations include a comparison between this coordinated control and the traditional thruster control and a sensitivity analysis on initial values of parameters. Simulation results validate the feasibility of the attitude control scheme for a tumbling combination system, and fuel consumption of the attitude control is efficiently reduced using the coordinated control strategies.

This paper presents the kinematics and dynamics of asix-degree-of-freedom platform-type parallel manipulator with six revolute legs,i.e. each leg consists of two links that are connected by a revolute joint.Moreover, each leg is connected, in turn, to the base and moving platforms bymeans of universal and spherical joints, respectively. We first introduce akinematic model for the manipulator under study. Then, this model is used toderive the kinematics relations of the manipulator at the displacement, velocityand acceleration levels. Based on the proposed model, we develop the dynamicsequations of the manipulator using the method of the natural orthogonalcomplement. The implementation of the model is illustrated by computersimulation and numerical results are presented for a sample trajectory in theCartesian space.