The use of redundant sensors brings a rich diversity of information, nevertheless fusing different sensors that run at vastly different frequencies into a proper estimate is still a challenging sensor fusion problem. Instead of using the size-varying measurements and thereby the size-varying filters during each sampling period, we propose to find a substitute of the unavailable low frequency measurements such that we can avoid using different sampling frequencies in one filter. In the gap between the sampling of two low frequency measurements, the use of these substitutes produces smoother estimates. In both the proof of concept simulation and the localization experiment performed on an indoor soccer robot, our proposed approach exhibits an improved performance compared to the size-varying Kalman filter methods.

In this paper, we consider the problem of distributed formation control for a group of unicycle robots. We propose a control algorithm that solves the formation control problem in that it ensures that robots create a desired time-varying formation shape while the formation as a whole follows a prescribed trajectory. Moreover, we show that it is also possible to obtain coordination of robots in the formation, regardless of the trajectory tracking of the formation. We illustrate the behavior of a group of robots controlled by the formation control algorithm proposed in this paper in a simulation study.

When humanoid robots are going to be used in society, they should be capable to maintain the balance. Knowing where to step appears to be crucially important to remain balanced. This paper contributes the foot placement indicator (FPI), an extension to the foot placement estimator (FPE) for planar bipeds with point feet and an arbitrary number of non-massless links. The method uses conservation of energy to determine where the planar biped needs to step to remain in balance. Simulations of the FPI show improved foot placement for balance with respect to the FPE.

Presents closed-form kinematic and dynamic models of a robot with three rotational degrees of freedom. The derivation of the models and estimation of their parameters are explained. Relevancy of the models is investigated with a writing task. Validation results, obtained by simulation and experiment, establish correctness of the models and illuminate their practical benefits.

This paper suggests and employs a method for reducing vibrations at the tip of a robot. The vibrations, caused by structural flexibility, are detected using accelerometers. The robot control system consists of two complementary sub-systems: a nominal motion controller and a vibration compensator. The former one realizes joint motions in accordance with a prescribed tip trajectory. The latter one robustly attenuates oscillations at the tip that are due to structural flexibility. The benefits of this set-up are experimentally verified for a spatial direct-drive robot with 3 revolute degrees of freedom.

Achieving human-like behavior of a robot is a key issue of the paper. Redundancy in the inverse kinematics problem is resolved using a biological analogue. It is shown that by means of "virtual fatigue" functions, it is possible to generate robot movements similar to movements of a human arm subject to muscle fatigue. Analytic method enabling control of robot motions in a human-like fashion is described. An example of an anthropomorphic robot arm performing a screw-driving task illustrates the method.

Development of skilled robotics draws clues from model based theories of human motor control. Thus, a comprehensive anthropomorphic background is given. Skills in robotics are viewed as a tool for fast and efficient real time control that can handle complexity and nonlinearity of robots, generally aiming at robot autonomy. In particular, a skill of redundancy resolution is addressed through a skill representation problem based on Function Approximator. The task of the robot is approximated by a set of parameterized motion primitives. Adopted parameters are also parameters of the function approximator, i.e., skill used. Redundancy is resolved during skill learning based on available expert knowledge, yielding parameterized joint motions. The approximation procedure (Successive Approximations), a major contribution of the paper, is used for batch compilation of parameterized examples, resulting in a parameterized skill model. Such skill enables a user, inexpert in redundancy resolution, to gain benefits from redundant robots. All properties of the Successive Approximations procedure such as accuracy in interpolation and extrapolation, acceleration in redundancy resolution and upgrading to new skill regarding the task variation, are discussed in the example of a five degrees-of-freedom planar redundant robot, performing parameterized ellipse as motion primitive.

Acceleration in redundancy resolution has been achieved by model-based learning. Parameterized joint motions generated by pseudoinverse of parameterized motion primitives in operational space are taken as learning examples. The method of successive approximations (SA) as a function approximator generalizes given examples resulting in a parameterized model, termed redundancy resolution skill. The SA method results in an analytical solution of joint motions. This particular property enables extension of the universality of skills to tasks never experienced by learning. Moreover, a proper addressing of skills output results in paths completely different from learned ones. In practice, this means that a segment as a motion primitive suffices in acquiring skill in the learning domain. The example emphasises this property of SA-based redundancy resolution. Highly compressed skill, acquired on segments only, is applied in RR along two test-trajectories-an 'H'-character and a rosette. The gained acceleration of RR recommends this procedure for online redundant robot control and easy robot programming.