This paper proposes control algorithms for heterogeneous teleoperation systems to guarantee stability and tracking performance in the presence of time-varying communication delays. Because robotic manipulators, in most applications of bilateral teleoperation systems, interact with a human operator and remote environment on the end-effector, the control system is developed in the task-space. When the dynamic parameters of the robots are unknown and the communication network is subject to time-varying delay, the developed controller can ensure stability and task-space position tracking. Additionally, if the robotic systems are influenced by human and environmental forces, the presented teleoperation control system is demonstrated to be stable and all signals are proven to be ultimately bounded. By employing the redundancy of the slave robot for sub-task control, the proposed teleoperation system can autonomously achieve additional missions in the remote environment. Numerical examples utilizing a redundant planar robot are addressed to validate the proposed task-space teleoperators with time-varying delay.

This paper presents a new monocular SLAM algorithm that uses straight lines extracted from images to represent the environment. A line is parametrized by two pairs of azimuth and elevation angles together with the two corresponding camera centres as anchors making the feature initialization relatively straightforward. There is no redundancy in the state vector as this is a minimal representation. A bundle adjustment (BA) algorithm that minimizes the reprojection error of the line features is developed for solving the monocular SLAM problem with only line features. A new map joining algorithm which can automatically optimize the relative scales of the local maps is used to combine the local maps generated using BA. Results from both simulations and experimental datasets are used to demonstrate the accuracy and consistency of the proposed BA and map joining algorithms.

This paper addresses the trajectory tracking control problem of robotic wheelchairs in the presence of modeling uncertainties. The controller has been designed using position and angular measurements. A global ultra-model, or simplified model achieved from flatness considerations is proposed first. This model highly reduces the design complexity of the state estimation and the output feedback control tasks since it groups, as an unknown time-varying disturbance, both the combined effects of all uncertain state-dependent (i.e., endogenous) nonlinearities and those of external (i.e., exogenous) perturbation inputs which are present in the input-to-flat output model of the system. An extended linear high-gain observer, or Generalized Proportional Integral (GPI) observer, is then developed for the simultaneous, though approximate, state and disturbance estimation. The proposed feedback controller combines the global ultra-model and the GPI observers to conform an active disturbance rejection, or disturbance accommodation, control scheme. The simulation results presented in the paper show that the proposed method has a very good tracking performance and robustness in the presence of system uncertainties, external disturbances and noisy corruptions.

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot.

This paper studies the effect of joint flexibility on the dynamic performance of a serial spatial robot arm of rigid links. Three models are developed in this paper. The first and the third models are developed using the multibody dynamics approach, while the second using the classical robotics approach. A numerical algorithm and an experimental test-rig are developed to test the final model. The links' inertial parameters are estimated numerically. Empirical formulae with assumption models are used to estimate the flexibility coefficients. The simulation results show that the joint damping is a major source of inaccuracies, causing trajectory error without a proper feedback controller.

In this paper, the robust, optimal, output control problem is dealt with for a 3-degree-of-freedom laboratory helicopter. The control goal is to achieve the practical tracking of the desired elevation and pitch angles without the angular velocity feedback. A nominal linear time-invariant system is introduced and the real system is considered as the nominal one with uncertainties, including parameter perturbations, nonlinear time-varying uncertainties, and external disturbances. An observer is first used to estimate angular velocity. Then a nominal controller based on the optimal control method is designed for the nominal system to achieve the desired tracking properties. Lastly, a robust output compensator is added to restrain the effects of uncertainties in the real system. It is shown that asymptotic tracking properties and robust stability can be achieved. Experimental results on the laboratory helicopter are shown to verify the effectiveness of the proposed control method.

In SLAM (simultaneous localization and mapping), the topological paradigm provides a more natural and compact solution that scales better with the size of the environment. Computer vision has always been regarded as the ideal sensor technology for topological feature extraction and description and several methods have been proposed in the literature, but they are either time-consuming, require plenty of different sensors, or are very sensitive to perceptual aliasing, all of which limit their application scope.

This paper presents a fast-to-compute collection of features extracted from monocular images, and an adaptive matching procedure for location identification in structured indoor environments inspired by the natural language processing field. Although only dominant vertical lines, color histograms, and a reduced number of keypoints are employed in this paper, the matching framework introduced allows for the incorporation of almost any other type of feature. The results of the experiments carried out in a home and an office environment suggest that the proposed method could be used for real-time topological scene recognition even if the environment changes moderately over time. Due to the combination of complementary features, high precision can be achieved within reasonable computation time by using weaker but faster descriptors.

This paper presents the design of a controller that allows a four-rotor helicopter to track a desired trajectory in 3D space. To this aim, a dynamic model obtained from Euler-Lagrange equations describes the robot. This model is represented by numerical methods, with which the control actions for the operation of the system are obtained. The proposed controller is simple and presents good performance in face of uncertainties in the model of the system to be controlled. Zero-convergence proof is included, and simulation results show a good performance of the control system.

Odometric error modelling for mobile robots is the basis of pose tracking. Without bounds the odometric accumulative error decreases localisation precision after long-range movement, which is often not capable of being compensated for in real time. Therefore, an efficient approach to odometric error modelling is proposed in regard to different drive type mobile robots. This method presents a hypothesis that the motion path approximates a circular arc. The approximate functional expressions between the control input of odometry and non-systematic error as well as systematic error derived from odometric error propagation law. Further an efficient algorithm of pose tracking is proposed for mobile robots, which is able to compensate for the non-systematic and systematic error in real time. These experiments denote that the odometric error modelling reduces the accumulative error of odometry efficiently and improves the specific localisation process significantly during autonomous navigation.

This paper proposes an analytical method of solving the inverse kinematic problem for a humanoid manipulator with five degrees-of-freedom (DOF) under the condition that the target orientation of the manipulator's end-effector is not constrained around an axis fixed with respect to the environment. Since the number of the joints is less than six, the inverse kinematic problem cannot be solved for arbitrarily specified position and orientation of the end-effector. To cope with the problem, a generalized unconstrained orientation is introduced in this paper. In addition, this paper conducts the singularity analysis to identify all singular conditions.

This paper presents analysis of the dynamics and vibration of an orientation motion platform utilizing a sphere actuated by omnidirectional wheels. The purpose of the analysis is to serve as a design tool for the construction of a six-degree-of-freedom motion platform with unlimited rotational motion. The equations of motion are presented taking flexibility of the system into account. The behaviour of the system is illustrated by sample configurations with a range of omnidirectional wheel types and geometries. Vibration analysis follows, and sensitivity to various parameters is investigated. It is determined that the geometry of omnidirectional wheels has a significant effect on the behaviour of the system.

For cable-driven parallel manipulators (CDPMs), it is known that maintaining positive cable tension is critical in constraining the moving platform. Hence, the force-closure workspace of CDPMs represents a set of poses where the cable tensions can balance arbitrary external wrench applied on the moving platform, proposed by researchers. A new computation method for the force-closure workspace of CDPMs is developed in this paper, and the new method is realized by calculating the null space of the structure matrix and solving the linear matrix inequalities. The detailed calculation procedures of the force-closure workspace for the incompletely restrained, completely restrained, and redundantly restrained CDPMs are given, respectively, and the advantages of the new method are analyzed according to the time complexity. The simulation experiments of the force-closure workspace computation are implemented on a six-degree of freedom (6-DOF) CDPM with eight cables, and then the superiority of the new method over the existing algorithm is studied.

Undulatory fin propulsion, inspired by the locomotion of aquatic species such as electric eels and cuttlefish, holds considerable potential for endowing underwater vehicles with enhanced propulsion and maneuvering abilities, to address the needs of a growing number of applications. However, there are still gaps in our understanding of the effect of the fin undulations' characteristics on the generated thrust, particularly within the context of developing propulsion control strategies for such robotic systems. Towards this end, we present the design and experimental evaluation of a robotic fin prototype, comprised of eight individually-actuated fin rays. An artificial central pattern generator (CPG) is used to produce the rays' undulatory motion pattern. Experiments are performed inside a water tank, with the robotic fin suspended from a carriage, whose motion is constrained via a linear guide. The results from a series of detailed parametric investigations reveal several important findings regarding the effect of the undulatory wave kinematics on the propulsion speed and efficiency. Based on these findings, two alternative strategies for propulsion control of the robotic fin are proposed. In the first one, the speed is varied through changes in the undulation amplitude, while the second one involves simultaneous adjustment of the undulation frequency and number of waves. These two strategies are evaluated via experiments demonstrating open-loop velocity control, as well as closed-loop position control of the prototype.

We propose a fuzzy weighted subtask controller for a redundant robot manipulator. To expand the feasibility of the inverse kinematic solution, we introduce a weighted pseudo-inverse that changes the null-space of the Jacobian. The weights of elements in the pseudo-inverse are obtained using fuzzy rules that are related to the null-space velocity tracking error. With the pseudo-inverse, we develop a task space controller to track a desired task space trajectory and subtask control input. We propose a weighted subtask controller for multiple subtasks. The results of a simulation and experiment using a seven-degree-of-freedom whole arm manipulator robot show the effectiveness of the proposed controller with multiple subtasks.

This paper proposes a new technique based on a Parameter-Optimal Iterative Learning Control (POILC) to track a command pitch rate of a high-speed supercavitating vehicle (HSSV). The pitch rate of a supercavitating vehicle has non-minimum phase behavior. Thus, tracking is fundamentally limited to poor performance. To solve this problem, a feed-forward control can be used while using the cavitator as a control input in the feed-forward path to modify the slow response caused by non-minimum phase behavior. The main idea of this paper is to apply the cavitator input with high precision as a feed-forward control to improve tracking performance. The exact value of the feed-forward control is achieved using POILC. However, in the presence of uncertainty, zero convergence of POILC algorithms is threatened. It will be shown that applying adaptive weight in the performance index, the convergence is guaranteed in the presence of uncertainty and also when the system is sign-indefinite. The proposed technique includes an optimal planning to make the error norm monotonically convergent to zero. The convergence and perfect tracking will be guaranteed through a Lyapunov candidate. Performance and significance of the proposed supercavitating vehicle control will be verified by simulation.

In this work, we investigate a quaternion-based formulation of 3D Simultaneous Localization and Mapping with Extended Kalman Filter (EKF-SLAM) using relative pose measurements. We introduce a discrete-time derivation that avoids the normalization problem that often arises when using unit quaternions in Kalman filter and we study its observability properties. The consistency of the estimation errors with the corresponding covariance matrices is also evaluated. The approach is further tested on real data from the Rawseeds dataset and it is applied within a delayed-state EKF architecture for estimating a dense 3D map of an unknown environment. The contribution is motivated by the possibility of abstracting multi-sensorial information in terms of relative pose measurements and for its straightforward extensions to the multi robot case.

In this paper, an analytical formula for the determination of the center of mass position in humanoid platforms is proposed and tested in a real humanoid robot. The formula uses the force-torque values obtained by the two force-torque sensors applied on the feet of the robot and the measured currents required from the motors to maintain balance as inputs. The proposed formula outputs the real center of mass position that minimizes the errors between real humanoid robots and virtual models. Data related to the Zero Moment Point positions and to the joint movements are compared with the target values, showing how the application of the proposed formula enables achieving better repeatability and predictability of the static and dynamic robot behaviour.

This paper presents a nonlinear distributed control strategy for flexible-link manipulators to solve the tracking control problem in the joint space and cancel vibrations of the links. First, the dynamic of an n-flexible-link manipulator is decomposed into n subsystems. Each subsystem has a pair of one joint and one link. The distributed control strategy is applied to each subsystem starting from the last subsystem. The strategy of control consists in controlling the nth joint and stabilizing the nth link by assuming that the remaining subsystems are stable. Then, going backward to the (n − 1)th subsystem, the same control strategy is applied to each corresponding joint-link subsystem until the first. Sliding mode technique is used to develop the control law of each subsystem and the global stability of the resulting tracking errors is proved using the Lyapunov technique. This algorithm was tested on a two-flexible-link manipulator and gave effective results, a good tracking performance, and capability to eliminate the links' vibrations.

We propose an appearance-based approach for topological visual mapping and localization using local invariant features. To optimize running times, matchings between the current image and previously visited places are determined using an index based on a set of randomized kd-trees. We use a discrete Bayes filter for predicting loop candidates, whose observation model is a novel approach based on an efficient matching scheme between features. In order to avoid redundant information in the resulting maps, we also present a map refinement framework, which takes into account the visual information stored in the map for refining the final topology of the environment. These refined maps save storage space and improve the execution times of localizations tasks. The approach is validated using image sequences from several environments and compared with the state-of-the-art FAB-MAP 2.0 algorithm.

A novel nonlinear teleoperation algorithm for simultaneous inertial parameters and force estimation at the master and slave sides of the teleoperation system is proposed. The scheme, called Extended Active Observer (EAOB), is an extension of the existing active observer. It provides effective force tracking at the master side with accurate position tracking at the slave side in the presence of inertial parameter variation and measurement noise. The proposed method only requires the measurement of robot position, and as a result significantly reduces the difficulty and cost of implementing bilateral teleoperation systems. The approach is described and its stability is analytically verified. The performance of the method is validated through computer simulation and compared with the Nicosia observer-based controller. According to the results, EAOB outperforms the Nicosia observer method and effectively rejects noise.