Mobile Robot Position Estimation Research Articles

A Modified Kalman Filter-Based Mobile Robot Position Measurement using an Accelerometer and Wheels Encoder

Position measurement is an essential process of mobile robot navigation. In this research, a Kalman Filter is applied to locating a mobile robot furnisher with an encoder and accelerometer. The accelerometer updates its position off-hand. It has an acceptable short period of stability. However, this stability will be decreased over time. The odometry model is utilized to measure the mobile robot's position and heading angle using encoders equipped with the wheels of the mobile robot. Moreover, the odometry model's errors exist because of the wheel rotating speed's integrative nature and non-systematic errors. In this work, the mobile robot position estimation in closed environments was studied. In order to obtain the optimal estimation, a Kalman filter was used to estimate mobile robots' position and velocity, where the Kalman filter has been designed for better assessment of the mobile robot position. The suggested configuration collects accelerometer and odometry reading to assure more delicate position knowledge than standalone odometry or accelerometer. The proposed method's position error has an acceptable level that is less than (0.2 m) for both easy and difficult paths.

Accurate Position Estimation of Mobile Robot Based on Cyber-Physical-Social Systems (CPSS)

When planning the trajectory of a mobile robot, it is usually necessary to use sensors to collect a large amount of position information. Because traditional computing methods cannot effectively use huge data sources, an edge computing that implements edge intelligent services on the side of network edge near the data source is proposed, which speeds up the process of data processing. However, the data collected through sensors may contain gross errors. In general, the influence of gross errors on state estimation are rarely considered when using particle filter algorithms for state estimation. In fact, the measurements of dynamic systems are often affected by different types of gross errors in the actual application process. Therefore, it is a problem worth studying that how to detect and compensate for different types of gross errors. In this paper, an improved particle filter algorithm is proposed for the position estimation of mobile robot dynamic system. Firstly, the gross error identification method is used to identify the types of gross errors, and then the various gross errors are compensated. Finally, the particle filter algorithm based on the measurements compensation is obtained. Simulation experiments on the position estimation of mobile robots are carried out to verify the effectiveness of the proposed method in solving the measurements with gross errors. The precise position estimation of the mobile robot is achieved. Through the simulation experiments on the position estimation problem of mobile robots, it is verified that the proposed method is effective in solving the measurements with gross errors. And the accurate position estimation of the mobile robot is realized.

Realization of robust position estimation of mobile robot by choosing sensor information

Realization of robust position estimation of mobile robot by choosing sensor information

A Mathematical Model to Estimate the Position of Mobile Robot by Sensing Caster Wheel Motion

This paper describes the position estimation of mobile robot by sensing caster wheel motion. A mathematical model is developed to determine the position of mobile robot by sensing the angular velocity and heading angle of the caster wheel. Using the established equations, simulations were carried out using MATLAB version 8.6 to observe and verify the position coordinates of mobile robot and in turn obtain its trajectory. The simulation results show that the angular velocity of caster wheel and heading angle calculated from the sensor output readings with the help of inverse kinematics equations matches well with that of actual values given as input for simulation. Simulation result of tracking rectangular trajectory implies that the path traced by the mobile robot can also be determined from the sensor output readings. This concept can be implemented on a real mobile robot for estimation of its position.

A grey probability measure set based mobile robot position estimation algorithm

A novel algorithm for the position estimation of a mobile robot is proposed, which is based on probability statistics and grey system theory. For the proposed algorithm, the grey probability measure set is established, which is composed of a base set and the corresponding grey probability measure. The base set is used to represent the uncertain information and the grey probability measure distributes probability on the base set. Moreover, the integrating rules are formulated using the grey probability measure set and the q-satisfied rule to estimate the position of a mobile robot. In addition to providing a new way of representing the uncertain information, results of the proposed algorithm are also more reliable in the presence of error and outliers. The algorithm is applied in the position estimation of a Pioneer 3-DX robot in a corridor-office environment. Experimental results have shown that the estimation accuracy of the algorithm is as good as that of the particle filter and better than that of the extended Kalman filter.

A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.

Continuity properties of the appearance manifold for mobile robot position estimation

This paper describes the results of experiments using an appearance manifold of a scene to estimate the position and orientation of a mobile robot. These experiments use principal components of images of the environment as an orthogonal basis for representing appearance from different positions and orientations. A regularly sampled grid of images is projected into a set of N principal component images to produce a 3D manifold within an N dimensional appearance space. The orthogonal basis serves as an index for generating hypotheses of position and orientation from a newly acquired image. The basis provides a similarity metric for matching images. Distance from the projection of an observed image to the manifold gives an estimate of the confidence of a position estimate. Reconstructing the scene from the manifold allows obstacle detection. In these experiments, we examine the effects of variations in image resolution, number of basis images, and density of samples used for constructing the manifold. We investigate the use of interpolation over the appearance manifold in order to improve the precision of the estimated position and orientation. We demonstrate position estimation with 1D, 2D and 3D manifolds. Our results indicate that such an approach can provide a fast and reliable method for visual navigation. q 2001 Elsevier Science B.V. All rights reserved.

Position estimation using principal components of range data

This paper describes a new approach to mobile robot position estimation, based on principal component analysis of laser range data. An eigenspace is constructed from the principal components of a large number of range data sets. The structure of an environment, as seen by a range sensor, is represented as a family of surfaces in this space. Subsequent range data sets from the environment project as a point in this space. Associating this point to the family of surfaces gives a set of candidate positions and orientations (poses) for the sensor. These candidate poses correspond to positions and orientations in the environment which have similar range profiles. A Kalman filter can be used to select the most likely candidate pose based on coherence with small movements. The first part of this paper describes how a relatively small number of depth profiles of an environment can be used to generate a complete eigenspace. This space is used to build a representation of the range scan profiles obtained from a regular grid of positions and orientations (poses). This representation has the form of a family of surface (a manifold). This representation converts the problem of associating a range profile to possible positions and orientations into a table lookup. As a side benefit, the method provides a simple means to detect obstacles in a range profile. The final section of the paper reviews the use of estimation theory to determine the correct pose hypothesis by tracking.

Position estimation of mobile robots based on coded infrared signal transmission

A system based on coded infrared signal transmission for the estimation of position of mobile robots in a structured environment is reported. Particular emphasis is placed on the polar coordinate arrangement in which signals are sent from the transmitters situated at the corners of the boundaries of operation. A multisensor system, strategically situated onboard the robot, has been found to improve the accuracy of the position estimation substantially. The information detected by the sensors is suitably processed to calculate the central position of the robot geometrically. The algorithms for the position calculations and the operational strategy are presented. This system forms the basis for the coordination and cooperation philosophy of multiple mobile robots sharing the same environment and performing cooperative or competitive tasks.