scholarly journals Trajectory Planning for Nonholonomic Mobile Robot Using Extended Kalman Filter

2010 ◽  
Vol 2010 ◽  
pp. 1-22 ◽  
Author(s):  
Leonimer Flavio de Melo ◽  
Jose Fernando Mangili Junior
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Indoor Environment ◽  
Nonholonomic Constraints ◽  
Digital Signal ◽  
Dsp Processors ◽  
Gps System ◽  
Robot Platform

In the mobile robotic systems, a precise estimate of the robot pose with the intention of the optimization in the path planning is essential for the correct performance, on the part of the robots, for tasks that are destined to it. This paper describes the use of RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bidimensional indoor environment, where GPS system is out of range. This methodology takes advantage of high-performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass, and the result of triangulation Cartesian estimative, are fused for better position estimative. It uses a mathematical and computational tool for nonlinear systems with time-discrete sampling for pose estimative calculation of mobile robots, with the utilization of extended Kalman filter (EKF). A mobile robot platform with differential drive and nonholonomic constraints is used as a base for state space, plants and measurements models that are used in the simulations and validation of the experiments.

Wheelchairs Embedded Control System Design for Secure Navigation with RF Signal Triangulation

2013 ◽  
Vol 6 (2) ◽  
pp. 60-92
Author(s):  
Leonimer Flávio de Melo ◽  
Felipe Andrade Allemand Borges ◽  
João Maurício Rosário
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
High Performance ◽  
New Technology ◽  
Orientation Angle ◽  
Nonholonomic Constraints ◽  
Digital Signal ◽  
Planning Optimization ◽  
Dsp Processors

In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle ?) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It's a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments. After being tested and validated in the simulator, the control system is programmed in the control board memory of the mobile robot or wheelchair. Thus, the use of material is optimized, firstly validating the entire model virtually and afterwards operating the physical implementation of the navigation system.

Wheelchairs Embedded Control System Design for Secure Navigation With RF Signal Triangulation

2019 ◽  
pp. 814-849
Author(s):  
Leonimer Flávio de Melo ◽  
Felipe Andrade Allemand Borges ◽  
João Maurício Rosário
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
High Performance ◽  
New Technology ◽  
Orientation Angle ◽  
Nonholonomic Constraints ◽  
Digital Signal ◽  
Planning Optimization ◽  
Dsp Processors

In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle θ) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It's a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments. After being tested and validated in the simulator, the control system is programmed in the control board memory of the mobile robot or wheelchair. Thus, the use of material is optimized, firstly validating the entire model virtually and afterwards operating the physical implementation of the navigation system.

Parameter Estimation in a Hydrostatic System Using Extended Kalman Filter

2001 ◽  
Author(s):  
Yuvin A. Chinniah ◽  
Richard Burton ◽  
Saeid Habibi
Keyword(s):  
Kalman Filter ◽  
Bulk Modulus ◽  
Extended Kalman Filter ◽  
High Performance ◽  
State Space Model ◽  
System Response ◽  
Actuation System ◽  
Accuracy And Precision ◽  
Hydrostatic System ◽  
Entrained Air

Abstract In this paper, the Extended Kalman Filter (EKF) estimation technique is applied to a novel hydrostatic actuation system referred to as the Electrohydraulic Actuator (EHA). A state space model of the EHA is developed and the effective bulk modulus is estimated in simulation. The EHA is a high performance actuation system capable of moving large loads with very high accuracy and precision. In a practical situation, this parameter is very difficult to measure directly as it depends on entrained air which cannot be known at a particular point of time. The bulk modulus is critical for system response and a low bulk modulus as a result of air in the system can seriously hinder the performance of EHA and cause safety problems.

LRF-based self-localization of mobile robot using Extended Kalman Filter

2008 ◽  
Author(s):  
Songmin Jia ◽  
Akira Yasuda ◽  
Daisuke Chugo ◽  
Kunikatsu Takase
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter

scholarly journals EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism

2020 ◽  
Vol 10 (3) ◽  
pp. 940
Author(s):  
Baiping Chen ◽  
Huifeng Wu ◽  
Hongwei Zhou ◽  
Danfeng Sun
Keyword(s):  
Kalman Filter ◽  
Injection Molding ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Foreign Bodies ◽  
Adaptive Method ◽  
Protection Method ◽  
Current Curve ◽  
Maintenance Process ◽  
Self Adaptive

Nowadays, the plastic injection molding industry is ever-growing, crucial, and its plastic products can be seen everywhere. However, the mold damage problem still frustrates operators because of its high maintenance price and time-consuming maintenance process. This damage is commonly caused by foreign bodies in mold area, and the conventional mold protection method is insufficient for high-performance injection molding machines because of the uncertainty from many setting parameters. To improve detection precision of mold protection driven by a toggle mechanism ( T M ), this paper puts forward E M P , i.e., an extended Kalman filter ( E K F ) based self-adaptive mold protection method, wherein the E K F is used in current curve optimization, and the self-adaptive method ( S A M ) is proposed to gain an safety range of current curve. The E M P was verified in a 140-ton electric injection molding machine. Compared with a general method, the proposed method decreases the detected distance of mold protection by 22% under different thickness foreign bodies.

Comparative Study on Autonomous Robot Trajectory Determination in an Unknown Indoor Environment

2014 ◽  
Vol 555 ◽  
pp. 327-333
Author(s):  
Teodora Gîrbacia
Keyword(s):  
Kalman Filter ◽  
Comparative Study ◽  
Mobile Robot ◽  
Particle Filter ◽  
Measurement Errors ◽  
Indoor Environment ◽  
Autonomous Mobile Robot ◽  
Robot Trajectory ◽  
Slam Algorithm ◽  
Closed Environment

In this paper is presented a comparative study between using extended Kalman filter and particle filter applied on SLAM algorithm for an autonomous mobile robot. The robot navigates through an unknown indoor environment in which are placed 80 landmarks and it creates the map of the environment. Because the sensors placed on the robots produce measurement errors it is necessary to use Bayesian filters as the Kalman filter or the particle filter. An application was implemented that shows the estimated measurement errors produced while using both filters in order to create the estimated map of the closed environment in which the autonomous mobile robot is navigating.

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