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.

In this paper, an experimental verification of antagonistic stiffness planning is presented for a 2-DOF parallel mechanism with four actuators. With 2-DOF force redundancy, the magnitude and direction of the stiffness enhancement can be controlled by the additional actuators, where the internal torques of the mechanism exist on the two-dimensional null space. In the experiments, the passive and active stiffness are measured, respectively, during endowing the external force at the end-effector. Two stiffness assignment methods for a given pathway are suggested and are verified by the experiments.

The real-time balance PD control of an eight-link biped robot using a zero-moment point (ZMP) dynamic model is implemented using two alternative intelligent computing control techniques that were compared: one based on support vector regression (SVR) and another based on a first order Takagi–Sugeno–Kang (TSK) -type neural-fuzzy (NF). Both methods use the ZMP error, and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data, and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods.

The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is based on ankle and hip human trajectories that were acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs at 0.2 ms, about 50 times faster than the NF controller and much faster than a controller based on full ZMP dynamic model equations.

This paper proposes a novel shuffle turning method for a humanoid robot that controls the load distribution of the soles of the robot's feet. Turning motions of a humanoid robot are conventionally performed through a repeated foot stepping motion. However, this motion is inefficient and time-consuming. In our method, the feet are slid along the floor without a stepping movement. In order to reduce the friction with the floor and to achieve the correct shuffle turning motion, a non-uniform load distribution of the soles is controlled. Experiments using a humanoid robot were conducted on two floors with differing friction amounts, and the validity of the proposed method was verified.

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.

Estimating robot performance in human robot teams is a vital problem in human robot interaction community. In a previous work, we presented extended neglect tolerance model for estimation of robot performance, where the human operator switches control between robots sequentially based on acceptable performance levels, taking into account any false alarms in human robot interactions. Task complexity is a key parameter that directly impacts the robot performance as well as the false alarms occurrences. In this paper, we validate the extended neglect tolerance model for two robot tasks of varying complexity levels. We also present the impact of task complexity on robot performance estimations and false alarms demands. Experiments were performed with real and virtual humanoid soccer robots across tele-operated and semi-autonomous modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the extended neglect tolerance model predictions for both real and virtual robot experiments. Experiments also showed that the task complexity is directly proportional to false alarm demands and inversely proportional to robot performance.

Mobile robot systems are being used more often in tasks that protect human operators from dangerous environments, but these benefits can be easily lost if the robots spend much of their time being repaired. This implies that any increment in their reliability will also improve their benefits. One way to achieve this is by adding redundant elements to the robot, but this adds complexity and cost to the design. On the other hand, cooperative mobile robots formed by members with the same basic structure provide a natural redundancy of elements, which may be used for reliability improvement. This work presents an architecture that takes advantage of the analytical and sensor redundancy present in groups of cooperative mobile robots in order to increase the reliability of the whole system. First, the design of the architecture is portrayed and the faults to be detected are described. The different layers of the system are then explained and analyzed using several simulations to test their capabilities and limitations. Finally, the experimental results on a group of small mobile robots are shown, validating the results delivered by simulations. These results show that the proposed architecture is able to detect and isolate correctly most of the faults tested.

The proper transmission of wave variables rather than power variables in teleoperation with time delays ensures system passivity – rendering the entire system stable, but the introduction of wave variables leads to distortion between the velocities/positions of the master and slave, and the performance deteriorates significantly with the increase of time delays. This paper presents a new compensating scheme implemented at the slave side to remove or cancel partially the distortion. The system passivity can still be maintained by tuning a properly designed low-pass filter. Compared with previous work, the main contribution of this work exists in two points: first, it is the actual velocity/trajectory of the slave rather than the reference velocity/trajectory of the slave that tracks the master one, so the quality of tracking is better and converges faster; second, the proposed compensator does not require any additional signal from the master side, minimizing the amount of data transmitted over the channel, which is very important from the practical point of view. The simulation and experiment results show that the velocity (or trajectory) tracking is significantly improved under the condition of stability, and the force presented to the user is close to the environment one, meaning a high degree of system transparency is achieved.

This paper considers planning and control algorithms that enable a programmable sheet to realize different shapes by autonomous folding. Prior work on self-reconfiguring machines has considered modular systems in which independent units coordinate with their neighbors to realize a desired shape. A key limitation in these prior systems is the typically many operations to make and break connections with neighbors, which lead to brittle performance. We seek to mitigate these difficulties through the unique concept of self-folding origami with a universal fixed set of hinges. This approach exploits a single sheet composed of interconnected triangular sections. The sheet is able to fold into a set of predetermined shapes using embedded actuation.

We describe the planning algorithms underlying these self-folding sheets, forming a new family of reconfigurable robots that fold themselves into origami by actuating edges to fold by desired angles at desired times. Given a flat sheet, the set of hinges, and a desired folded state for the sheet, the algorithms (1) plan a continuous folding motion into the desired state, (2) discretize this motion into a practicable sequence of phases, (3) overlay these patterns and factor the steps into a minimum set of groups, and (4) automatically plan the location of actuators and threads on the sheet for implementing the shape-formation control.

This paper presents a control architecture, which has the potential to monitor the task and safety issues, to provide assessment of the progress and alter the task parameters, and to incorporate patient's feedback in order to make the necessary modifications to impart effective therapy during the execution of the task in an automated manner. Experimental results are presented to demonstrate the efficacy of the proposed control architecture.

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.

Based on the terminal constraints system (TCS) and reciprocal screw theory, a novel method is presented to determine the isotropic configurations of limited degree-of-freedom (DOF) parallel manipulators. From the available physical meaning of isotropy, the criteria to determine the isotropic configurations can be transformed to investigate whether the TCS acting on the moving platform works equally well in all directions. From the TCS study, the simplest form of constraints system matrix can be obtained. Then the constraint condition number is defined to measure the isotropy of spatial parallel manipulator based on the TCS. This method not only avoids solving the Jacobian matrix for some complex structural parallel manipulators but also points out the physical meaning of isotropy, which indicates that the TCS acting on the moving platform works equally well in all directions. Three examples are employed to illustrate this method.

Dexterity and workspace are two of the most important design criteria in developing manipulators. This paper presents algorithms for developing contour surfaces with specified dexterities and evaluating the area of the surfaces or the volume of the enclosed regions. The obtained results can be utilized to evaluate the dexterity and the rate of change of dexterity. Any closed curve or surface can be used to determine the singularity-free workspace of a manipulator with better dexterity. The proposed algorithms can be employed to study the dexterity and singularity-free workspace of 3-DOF manipulators and 4-DOF redundant serial manipulators. The contour surfaces in some subspaces of 6-DOF manipulators can also be investigated.

A mechanical design and dynamics walking simulation of a novel tripod walking robot are presented in this paper. The tripod walking robot consists of three 1-degree-of-freedom (DOF) Chebyshev–Pantograph leg mechanisms with linkage architecture. A balancing mechanism is mounted on the body of the tripod walking robot to adjust its center of gravity (COG) during walking for balancing purpose. A statically stable tripod walking gait is performed by synchronizing the motions of the three leg mechanisms and the balancing mechanism. A three-dimensional model has been elaborated in SolidWorks® engineering software environment for a characterization of a feasible mechanical design. Dynamics simulation has been carried out in the MSC.ADAMS® environment with the aim to characterize and to evaluate the dynamic walking performances of the proposed design with low-cost easy-operation features. Simulation results show that the proposed tripod walking robot with proper input torques, gives limited reaction forces at the linkage joints, and a practical feasible walking ability on a flatten ground.

The fields of modular reconfigurable robotics and programmable matter study how to compose functionally useful systems from configurations of modules. In addition to the external shape of a module configuration, the internal arrangement of modules and bonds between them can greatly impact functionally relevant mechanical properties such as load bearing ability. A fast method to evaluate the mechanical property aids the search for an arrangement of modules achieving a desired mechanical property as the space of possible configurations grows combinatorially. We present a fast approximate method where the bonds between modules are represented with stiffness matrices that are general enough to represent a wide variety of systems and follows the natural modular decomposition of the system. The method includes nonlinear modeling such as anisotropic bonds and properties that vary as components flex. We show that the arrangement of two types of bonds within a programmable matter systems enables programming the apparent elasticity of the structure. We also present a method to experimentally determine the stiffness matrix for chain style reconfigurable robots. The efficacy of applying the method is demonstrated on the CKBot modular robot and two programmable matter systems: the Rubik's snake folding chain toy and a right angle tetrahedron chain called RATChET7mm. By allowing the design space to be rapidly explored we open the door to optimizing modular structures for desired mechanical properties such as enhanced load bearing and robustness.

This paper presents the teleoperation of a parallel robot based on the Stewart–Gough platform. The robot is an experimental prototype capable of sliding along electrical lines in order to carry out different inspection and maintenance tasks. The paper also describes the kinematic equations of the parallel robot and their solution. The teleoperation of the prototype took place in a testing bank developed in the laboratory and containing the basic elements of electrical lines. The operator who performs the task uses a six degrees-of-freedom master-arm with force reflection, which also has an image-viewing system providing the operator with stereoscopic vision and allowing him/her to calibrate the information of the local station simulator with the real environment through the blending of images. The paper also presents the results of the laboratory experiments of the robot sliding along a line.

The present paper aims at generating three-dimensional cyclic gait of a biped with a locomotion system having anthropomorphic characteristics. Kinematic and dynamic models of both single and double support phases are extensively developed with a special attention devoted to the double support phase. A variety of gait constraints defining a feasible walk is taken into account. Joint trajectories are approximated by cubic spline functions connected at uniformly distributed knots. Joint coordinates at knots, walking phase durations, and independent parameters at phase transitions are the design parameters of a parametric optimization problem. Therefore, only the pattern organization and the gait speed are explicitly specified. The effectiveness of the proposed method is verified and discussed through some simulation results.

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.

We present two visual servoing controllers (pose-based and image-based) enabling mobile robots with a fixed pinhole camera to reach and follow a continuous path drawn on the ground. The first contribution is the theoretical and experimental comparison between pose-based and image-based techniques for a nonholonomic robot task. Moreover, our controllers are appropriate not only for path following, but also for path reaching, a problem that has been rarely tackled in the past. Finally, in contrast with most works, which require the path geometric model, only two path features are necessary in our image-based scheme and three in the pose-based scheme. For both controllers, a convergence analysis is carried out, and the performance is validated by simulations, and outdoor experiments on a car-like robot.

In this work a novel spatial hyper-redundant manipulator inspired in the motions of the worms is introduced. The displacement analysis is presented in a semi-closed form solution, whereas the velocity and acceleration analyses are carried out by means of the theory of screws. Among typical applications of most hyper-redundant manipulators, interesting biomechanical applications such as the simulation of the motion of the spine are available for this new artificial worm.