Feedback systems: Input-output properties

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

/pdf/model-learning-for-robot-control-a-survey-yltgqr6pis.pdf

Model learning for robot control: a survey

We show that for an adaptive controller that uses recorded and instantaneous data concurrently for adaptation, a verifiable condition on linear independence of the recorded data is sufficient to guarantee exponential tracking error and parameter error convergence. This condition is found to be less restrictive and easier to monitor than a condition on persistently exciting exogenous input signal required by traditional adaptive laws that use only instantaneous data for adaptation.

Concurrent learning for convergence in adaptive control without persistency of excitation

Neural network-based sliding mode adaptive control for robot manipulators

Thank you very much for downloading aided navigation gps with high rate sensors. As you may know, people have look numerous times for their favorite readings like this aided navigation gps with high rate sensors, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their laptop. aided navigation gps with high rate sensors is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the aided navigation gps with high rate sensors is universally compatible with any devices to read.

Aided Navigation Gps With High Rate Sensors

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.

Neural networks for advanced control of robot manipulators

Robot localization is a fundamental problem in mobile robotics. This paper proposes the use of a new type of sensor that permits accurate localization in indoor environments. An Impulse Radio (IR) Ultra Wide-Band (UWB) sensor uses very short baseband pulses for transmission. This sensor facilitates the development of indoor location and tracking applications based on time of arrival estimation with great accuracy. There are many different sensors and techniques that have been proposed for indoor localization; however they usually have large errors in Non Line of Sight (NLOS) conditions or with certain environment conditions. The objective of this paper is to propose a self-localization system based on Time of Arrival (TOA) estimation algorithm that permits precise localization in indoor environments with obstructed line of sight. This work proposes a new wavelet cyclic cross correlation strategy for time of arrival estimation based on non-coherent receiver structure and sliding correlation techniques for multiple user differentiation. The simulation results show that the propose localization system overcome the problems of NLOS conditions and makes possible the mobile robot localization with small number of base stations.

Mobile robot self-localization system using IR-UWB sensor in indoor environments

In this work, a simulation framework for low-cost integrated navigation systems called NaveGo is presented. NaveGo simulates measurements from inertial sensors and a GPS receiver. It also executes a loosely-coupled navigation algorithm. Complete mathematical models are shown. As example, simulation results for a particular scenario are exhibited. NaveGo is developed in MATLAB and freely distributed under an open source license. It is the authors' believe that this article is the most complete work about a simulation environment for low-cost integrated navigation systems. NaveGo is validated by using a practical approach, which main goal is to evaluate how close in performance is a simulated navigation system to a real one. Thus, simulated sensors are generated based upon a real trajectory, during which real sensors were logged. Then, simulated sensors are corrupted with noises according to real sensors error profiles. Both simulated and real navigation systems are processed. It is found that absolute differences between real and simulated systems are under 0.6 degrees for attitude, under 23 centimetres for 2D position, and under 10 centimetres for vertical position. As a result, it is verified that NaveGo is a suitable simulation tool for the design and analysis of low-cost integrated navigation systems.

/pdf/navego-a-simulation-framework-for-low-cost-integrated-30eu5jpgck.pdf

NaveGo: a simulation framework for low-cost integrated navigation systems

New solutions to the navigation problem related to low-cost integrated navigation systems (INS) are often published. Since these new solutions are generally compared with ad hoc mathematical models that are not fully exposed, one cannot be sure of the relative improvements. In this work, complete mathematical model for a low-cost INS is suggested to be used as a benchmarking. As far as the authors’ knowledge, a benchmarking for low-cost INS has not been previously reported. Shown INS comprises a strapdown inertial navigation system, loosely coupled to a GPS receiver. The INS mathematical model is based upon classical navigation equations and classical sensor models, both from recognized authors. The algorithm that details the INS operation is also presented. The benchmarking is provided as an open-source toolbox for MATLAB. Additionally, this work can be taken as a starting point for new practitioners in the INS field. To validate the INS mathematical model, real-world data sets from three different Micro...

Hector Daniel Patino

Papers

Neural networks for advanced control of robot manipulators

Mobile robot self-localization system using IR-UWB sensor in indoor environments

NaveGo: a simulation framework for low-cost integrated navigation systems

An approach to benchmarking of loosely coupled low-cost navigation systems

Optimal greenhouse control of tomato-seedling crops