scholarly journals Iterative learning control for path tracking of service robot in perspective dynamic system with uncertainties

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096852
Author(s):  
Wang Yugang ◽  
Zhou Fengyu ◽  
Zhao Yang ◽  
Li Ming ◽  
Yin Lei
Keyword(s):  
Dynamic System ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Path Tracking ◽  
Iterative Learning ◽  
Service Robots ◽  
Service Robot ◽  
Mobile Service ◽  
Motion Information ◽  
Mobile Service Robots

A novel iterative learning control (ILC) for perspective dynamic system (PDS) is designed and illustrated in detail in this article to overcome the uncertainties in path tracking of mobile service robots. PDS, which transmits the motion information of mobile service robots to image planes (such as a camera), provides a good control theoretical framework to estimate the robot motion problem. The proposed ILC algorithm is applied in accordance with the observed motion information to increase the robustness of the system in path tracking. The convergence of the presented learning algorithm is derived as the number of iterations tends to infinity under a specified condition. Simulation results show that the designed framework performs efficiently and satisfies the requirements of trajectory precision for path tracking of mobile service robots.

Automatically Annotated Mapping for Indoor Mobile Robot Applications

2012 ◽  
Author(s):  
Ali Gürcan Özkil ◽  
Thomas Howard
Keyword(s):  
Mobile Robot ◽  
Practical Method ◽  
Service Robots ◽  
Service Robot ◽  
Mobile Service ◽  
Indoor Environments ◽  
And Topology ◽  
Localization And Mapping ◽  
Indoor Mobile Robot ◽  
Mobile Service Robots

This paper presents a new and practical method for mapping and annotating indoor environments for mobile robot use. The method makes use of 2D occupancy grid maps for metric representation, and topology maps to indicate the connectivity of the ‘places-of-interests’ in the environment. Novel use of 2D visual tags allows encoding information physically at places-of-interest. Moreover, using physical characteristics of the visual tags (i.e. paper size) is exploited to recover relative poses of the tags in the environment using a simple camera. This method extends tag encoding to simultaneous localization and mapping in topology space, and fuses camera and robot pose estimations to build an automatically annotated global topo-metric map. It is developed as a framework for a hospital service robot and tested in a real hospital. Experiments show that the method is capable of producing globally consistent, automatically annotated hybrid metric-topological maps that is needed by mobile service robots.

Lateral robust iterative learning control for unmanned driving robot vehicle

2020 ◽  
Vol 234 (7) ◽  
pp. 792-808
Author(s):  
Shuhua Su ◽  
Gang Chen
Keyword(s):  
Dynamic Model ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Steering System ◽  
Path Tracking ◽  
Iterative Learning ◽  
Control Law ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.

Learning from Demonstration with State Based Obstacle Avoidance for Mobile Service Robots

2013 ◽  
Vol 394 ◽  
pp. 448-455 ◽  
Author(s):  
A.A. Nippun Kumaar ◽  
T.S.B. Sudarshan
Keyword(s):  
Obstacle Avoidance ◽  
Service Robots ◽  
Service Robot ◽  
Mobile Service ◽  
Learning From Demonstration ◽  
Service Robotics ◽  
Mobile Service Robots ◽  
Novel Approaches ◽  
User Friendly ◽  
Mobile Service Robot

Learning from Demonstration (LfD) is a technique for teaching a system through demonstration. In areas like service robotics the robot should be user friendly in terms of coding, so LfD techniques will be of greater advantage in this domain. In this paper two novel approaches, counter based technique and encoder based technique is proposed for teaching a mobile service robot to navigate from one point to another with a novel state based obstacle avoidance technique. The main aim of the work is to develop an LfD Algorithm which is less complex in terms of hardware and software. Both the proposed methods along with obstacle avoidance have been implemented and tested using Player/Stage robotics simulator.

Kinematic path-tracking of mobile robot using iterative learning control

2005 ◽  
Vol 22 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Min K. Kang ◽  
Jin S. Lee ◽  
Kyoung L. Han
Keyword(s):  
Mobile Robot ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Path Tracking ◽  
Iterative Learning
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