The cubic root unscented kalman filter to estimate the position and orientation of mobile robot trajectory

In this paper we introduce a Cubic Root Unscented Kalman Filter (CRUKF) compared to the Unscented Kalman Filter (UKF) for calculating the covariance cubic matrix and covariance matrix within a sensor fusion algorithm to estimate the measurements of an omnidirectional mobile robot trajectory. We study the fusion of the data obtained by the position and orientation with a good precision to localize the robot in an external medium; we apply the techniques of Kalman Filter (KF) to the estimation of the trajectory. We suppose a movement of mobile robot on a plan in two dimensions. The sensor approach is based on the Cubic Root Unscented Kalman Filter (CRUKF) and too on the standard Unscented Kalman Filter (UKF) which are modified to handle measurements from the position and orientation. A real-time implementation is done on a three-wheeled omnidirectional mobile robot, using a dynamic model with trajectories. The algorithm is analyzed and validated with simulations

Provably Safe Navigation for Mobile Robots with Limited Field-of-Views in Unknown Dynamic Environments

Technical session - Conf. website: http://icra2012.orgInternational audienceThis paper addresses the problem of navigating a mobile robot with a limited field-of-view in a unknown dynamic environment. In such a situation, absolute motion safety, i.e. such that no collision will ever take place whatever happens, is impossible to guarantee. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest. Passive motion safety is tackled using a variant of the Inevitable Collision State (ICS) concept called Braking ICS, i.e. states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Passive motion safety is readily obtained by avoiding Braking ICS at all times. Building upon an existing Braking ICS-Checker, i.e. an algorithm that checks if a given state is a Braking ICS or not, this paper presents a reactive collision avoidance scheme called PASSAVOID. The main contribution of this paper is the formal proof of PASSAVOID's passive motion safety. Experiments in simulation demonstrates how PASSAVOID operates

A PI/Backstepping Approach for Induction Motor Drives Robust Control

International audienceThis paper presents a robust control design procedure for induction motor drives in case of modeling errors and unknown load torque. The control law is based on the combination of nonlinear PI controllers and a backstepping methodology. More precisely, the controllers are determined by imposing flux-speed tracking in two steps and by using appropriate PI gains that are nonlinear functions of the system state. A comparative study between the proposed PI/Backstepping approach and the feedback linearizing control is made by realistic simulations including load torque changes, parameter variations and measurement noises. Flux-speed tracking results show the proposed method effectiveness in presence of strong disturbances

Passively Safe Partial Motion Planning for Mobile Robots with Limited Field-of-Views in Unknown Dynamic Environments

International audienceThis paper addresses the problem of planning the motion of a mobile robot with a limited sensory field-of-view in an unknown dynamic environment. In such a situation, the upper-bounded planning time prevents from computing a complete motion to the goal, partial motion planning is in order. Besides the presence of moving obstacles whose future behaviour is unknown precludes \textit{absolute motion safety} (in the sense that no collision will ever take place whatever happens) is impossible to guarantee. The stance taken herein is to settle for a weaker level of motion safety called \textit{passive motion safety}: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is {\passivepmp}, a partial motion planner enforcing passive motion safety. {\passivepmp} periodically computes a passively safe partial trajectory designed to drive the robot towards its goal state. Passive motion safety is handled using a variant of the Inevitable Collision State (ICS) concept called \textit{Braking ICS}, {\ie} states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Simulation results demonstrate how {\passivepmp} operates and handles limited sensory field-of-views, occlusions and moving obstacles with unknown future behaviour. More importantly, {\passivepmp} is provably passively safe

Analysis and Control of a Class of Stiff Linear Distributed Systems

182 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1983.This thesis examines a class of systems whose models are described by linear partial differential equations that depend on a small parameter (epsilon). First, the spectral decomposition of the so-called "stiff" operators (using the terminology of {24}) is investigated, including the convergence of their eigenvalue-eigenvector pairs as (epsilon) (--->) 0, with the objective of clarifying their singular behavior. Second, asymptotic approximation of the solution boundary value problems involving stiff operators are constructed, using the weak limits of their eigenvectors. This approach leads to a decomposition into "regular" approximation and "internal layer" approximation, which are found separately and then combined to provide an approximation to the original problem. This methodology is not complicated. Moreover, it alleviates the inherent stiffness when numerical algorithms are employed. Third, the same approach is applied to some control problems. In this case, similar results are obtained, provided additional requirements are satisfied, due to the type of control, which may drastically alter the system behavior.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Provably Safe Navigation for Mobile Robots with Limited Field-of-Views in Dynamic Environments

International audienceThis paper addresses the problem of navigating in a provably safe manner a mobile robot with a limited field-of-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is the concept of Braking Inevitable Collision States (ICS), i.e. a version of the ICS corresponding to passive motion safety. Braking ICS are defined as states such that, whatever the future braking trajectory followed by the robot, a collision occurs before it is at rest. Passive motion safety is obtained by avoiding Braking ICS at all times. It is shown that Braking ICS verify properties that allow the design of an efficient Braking ICS-Checking algorithm, i.e. an algorithm that determines whether a given state is a Braking ICS or not. To validate the Braking ICS concept and demonstrate its usefulness, the Braking ICS-Checking algorithm is integrated in a reactive navigation scheme called PassAvoid. It is formally established that PassAvoid is provably passively safe in the sense that it is guaranteed that the robot will always stay away from Braking ICS no matter what happens in the environment

Relaxing the Inevitable Collision State Concept to Address Provably Safe Mobile Robot Navigation with Limited Field-of-Views in Unknown Dynamic Environments

This paper addresses the problem of provably safe navigation for a mobile robot with a limited field-ofview placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision is inevitable, the robot will be at rest. The primary contribution of this paper is a relaxation of the Inevitable Collision State (ICS) concept called Braking ICS. A Braking ICS is a state for which, no matter what the future trajectory of the robot is, it is impossible to stop before a collision takes place. Braking ICS are designed with a passive motion safety perspective for robots with a limited field-of-view in unknown dynamic environments. Braking ICS are formally defined and a number of important properties are established. These properties are then used to design a Braking ICS checker, i.e. an algorithm that checks whether a given state is a Braking ICS or not. In a companion paper, it is shown how the Braking ICS checker can be integrated into a reactive navigation scheme whose passive motion safety is provably guaranteed

MEMS IMU/ZUPT Based Cubature Kalman Filter applied to Pedestrian Navigation System

In most related work with Pedestrian navigation, indirect filtering approach is used based on linear error based Kalman Filter (KF). In this research, it is proposed to outperform this approach by the use of direct filtering approach. Based on only MEMS IMU, we propose the use of modern algorithms developed in the last decade; Sigma Point Kalman Filters (SPKF), and recently developed Cubature Kalman Filter (CKF) as a superior alternative to all previous filters. The CKF improves the mean and covariance propagation as compared with EKF and previous SPKF (UKF, CDKF). Although the CKF provides a better estimate of the orientation, velocity and position with Zero velocity UPdaTes (ZUPT) and Zero Angular Rate UpdaTes (ZARUT) measurements. Cubature Information Filter has been also implemented for sake of completeness

Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system

In this paper, a suitable adaptive neuro-fuzzy inference system (ANFIS) model is presented for estimating sequences of mean monthly clearness index () and total solar radiation data in isolated sites based on geographical coordinates. The magnitude of solar radiation is the most important parameter for sizing photovoltaic (PV) systems. The ANFIS model is trained by using a multi-layer perceptron (MLP) based on fuzzy logic (FL) rules. The inputs of the ANFIS are the latitude, longitude, and altitude, while the outputs are the 12-values of mean monthly clearness index . These data have been collected from 60 locations in Algeria. The results show that the performance of the proposed approach in the prediction of mean monthly clearness index is favorably compared to the measured values. The root mean square error (RMSE) between measured and estimated values varies between 0.0215 and 0.0235 and the mean absolute percentage error (MAPE) is less than 2.2%. In addition, a comparison between the results obtained by the ANFIS model and artificial neural network (ANN) models, is presented in order to show the advantage of the proposed method. An example for sizing a stand-alone PV system is also presented. This technique has been applied to Algerian locations, but it can be generalized for any geographical position. It can also be used for estimating other meteorological parameters such as temperature, humidity and wind speed

Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems

International audienceThis paper presents different Maximum Power Point Tracking (MPPT) methods belonging to different classes as well as two overviews. The first was about the procedures used in the test and evaluation of MPPTs. The second is an overview of Fuzzy Logic Controller (FLC) MPPTs and improved MPPTs. Conventional MPPTs such as Perturb and Observe (P&O), Hill Climbing (HC) and Incremental Conductance (InCond); Improved MPPTs (are the modified versions of conventional MPPTs) such as Improved Incremental Conductance (Improved-InCond) and intelligent MPPTs such as FLC have been implemented and tested under two different levels of irradiance and temperature. A detailed description about the hardware and software implementation platforms (designed and built in our laboratory) is provided. Based on measured data, the MPPTs under consideration have been evaluated and compared in terms of different criteria, showing the advantages and disadvantages of each one. The comparison results showed that Improved-InCond gives a fast convergence to the MPP(Maximum Power Point). Whereas, FLC is able to adapt to the variation of irradiance and temperature levels. Thereby, a good performance is obtained wherein the MPP is reached in a short time as well as the power ripples are very small