In this paper, an iterative learning control algorithm is adopted to solve the high-precision trajectory tracking issue of a wheeled mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties. The designed iterative learning control law adopts predictive, current and past learning items to drive the state variables, and input variables, and outputs variables converge to the bounded scope of their desired values. The algorithm can enhance the control performance, stability and robust characteristics. The rigorous mathematical proof of the convergence character of the proposed iterative learning control algorithm is given. The feasibility, effectiveness, and robustness of the proposed algorithm are illustrated by quantitative experiments and comparative analysis. The experimental results show that the proposed iterative learning control algorithm has an outstanding control effect on the trajectory tracking issue of wheeled mobile robots.

The positioning of a wheeled robot is an imperative manipulation problem in mobile robotics. Odometry is a familiar method for determining the relative position of a mobile robot. It comprises the detection of a set of kinematic parameters that permit reconstructing the robot's absolute position and orientation starting from the wheels' encoder measurements. This paper deals with the determination of better relative localization of a mobile robot by means of odometry by considering the influence of parameters namely total weight, speed, diameter of wheel, and width of wheel. Experiments have been conducted based on L9 orthogonal array suggested in Taguchi method to obtain the optimum condition. A mathematical model has also been developed for the mobile robot with the help of MINITAB software.

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.

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.

In this paper, an adaptive force reflection scheme is proposed for bilateral teleoperation. In order to achieve an ideal telepresence performance while keeping the system stable, the force reflection algorithm needs to take both the human force and the contact force into consideration. An observer based on the feature of the human operator is designed to estimate the force applied on the master device. The reflected force is calculated by performing the orthogonal decomposition of the contact force, and is adjusted adaptively according to the estimated human force. The direction of the reflected force becomes a key consideration in the design process, so the proposed approach has an advantage in the guiding contact task. Based on the small gain theorem, the master device with the force reflection scheme is proved to be input-to-output stable, and the derivation for stability criterion of the closed-loop teleoperation system is also given. The results of simulations and experiments on a 6-degree of freedom teleoperation system demonstrate the effectiveness of the proposed scheme.

A crucial part of probabilistic roadmap planners is the nearest neighbor search, which is typically done by exact methods. Unfortunately, searching the neighbors can become a major bottleneck for the performance. This can occur when the roadmap size grows especially in high-dimensional spaces. In this paper, we investigate how well the approximate nearest neighbor searching works with probabilistic roadmap planners. We propose a method that is based on the locality-sensitive hashing and show that it can speed up the construction of the roadmap considerably without reducing the quality of the produced roadmap.

We propose a distributed algorithm for estimating the 3D pose (position and orientation) of multiple robots with respect to a common frame of reference when Global Positioning System is not available. This algorithm does not rely on the use of any maps, or the ability to recognize landmarks in the environment. Instead, we assume that noisy relative measurements between pairs of robots are intermittently available, which can be any one, or combination, of the following: relative pose, relative orientation, relative position, relative bearing, and relative distance. The additional information about each robot's pose provided by these measurements are used to improve over self-localization estimates. The proposed method is similar to a pose-graph optimization algorithm in spirit: pose estimates are obtained by solving an optimization problem in the underlying Riemannian manifold $(SO(3)\times{\mathcal R}^3)^{n(k)}$. The proposed algorithm is directly applicable to 3D pose estimation, can fuse heterogeneous measurement types, and can handle arbitrary time variation in the neighbor relationships among robots. Simulations show that the errors in the pose estimates obtained using this algorithm are significantly lower than what is achieved when robots estimate their pose without cooperation. Results from experiments with a pair of ground robots with vision-based sensors reinforce these findings. Further, simulations comparing the proposed algorithm with two state-of-the-art existing collaborative localization algorithms identify under what circumstances the proposed algorithm performs better than the existing methods. In addition, the question of trade-offs between cost (of obtaining a certain type of relative measurement) and benefit (improvement in localization accuracy) for various types of relative measurements is considered.

The safety analysis of human–robot collisions has recently drawn significant attention, as robots are increasingly used in human environments. In order to understand the potential injury a robot could cause in case of an impact, such incidents should be evaluated before designing a robot arm based on biomechanical safety criteria. In recent literature, such incidents have been investigated mostly by experimental crash-testing. However, experimental methods are expensive, and the design parameters of the robot arm are difficult to change instantly. In order to solve this issue, we propose a novel robot-human collision model consisting of a 6-degree-of-freedom mass-spring-damper system for impact analysis. Since the proposed robot-human consists of a head, neck, chest, and torso, the relative motion among these body parts can be analyzed. In this study, collision analysis of impacts to the head, neck, and chest at various collision speeds are conducted using the proposed collision model. Then, the degree of injury is estimated by using various biomechanical severity indices. The reliability of the proposed collision model is verified by comparing the obtained simulation results with experimental results from literature. Furthermore, the basic requirements for the design of safer robots are determined.

In this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.

This paper presents a new biped mechanism with low-cost easy-operation features. The mechanism is designed with functions for straight walking, changing direction, overcoming obstacle, and climbing stairs with only 7 DOFs (degrees of freedom). Dynamics of the biped mechanism are analyzed by means of simulations in the MSC.ADAMS environment. Simulation results in terms of motion torque, joint force, contact force, parts displacement, velocity, and acceleration are reported and analyzed to show the feasibility and efficiency of the proposed solution. In addition, with the simulation results, dynamical motion of the biped mechanism is investigated and its operation performances are characterized as well.