• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.218-224
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• 2009

This paper proposes an effective SLAM based on redundant extended Kalman filter for robot navigation in an irregular surface to enhance the accuracy of robot's pose. To establish an accurate model of a caterpillar type robot is very difficult due to the mechanical complexity of the system which results in highly nonlinear behavior. In addition, for robot navigation on an irregular surface, its control suffers from the uncertain pose of the robot heading closely related to the condition of the floor. We show how this problem can be overcome by the proposed approach based on redundant extended Kalman filter through some computer simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.883-887
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• 2016

Navigating an unknown environment is a challenging task for a robot, especially when a large number of obstacles exist and the odometry lacks reliability. Pose tracking allows the robot to determine its location relative to its previous location. The ICP (iterative closest point) has been a powerful method for matching two point clouds and determining the transformation matrix between the maps. However, in a situation where odometry is not available and the robot moves far from its original location, the ICP fails to calculate the exact displacement. In this paper, we suggest a method that is able to match two different point clouds taken a long distance apart. Without using any odometry information, it only exploits the features of corner points containing information on the surroundings. The algorithm is fast enough to run in real time.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2301-2306
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• 2005

Even if there are lots of researches on localization using 2D range finder in static environment, very few researches have been reported for robust real-time localization of mobile robot in uncertain and dynamic environment. In this paper, we present a new localization method based on ICP(Iterative Closest Point) algorithm for navigation of mobile robot under dynamic or uncertain environment. The ICP method is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. We use the method to align global map with 2D scanned data from range finder. The proposed algorithm accelerates the processing time by uniformly sampling the line fitted data from world map of mobile robot. A data filtering method is also used for threshold of occluded data from the range finder sensor. The effectiveness of the proposed method has been demonstrated through computer simulation and experiment in an office environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1771-1776
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• 2003

In mobile robot navigation, one of the key problems is the pose estimation of the mobile robot. Although the odometry can be used to describe the motions of the mobile robots quite simple and accurately, the validities of the models are limited by a number of error sources contaminating the encoder outputs so that applying the conventional extended Kalman filter to these nominal model does not yield the satisfactory performance. As a remedy for this problem, we consider the uncertain nonlinear kinematic model of the mobile robot that contains the norm bounded uncertainties and also propose a new robust extended Kalman filter based on the Krein space approach. The proposed robust filter has the same recursive structure as the conventional extended Kalman filter and can hence be readily designed to effectively account for the uncertainties. The computer simulations will be given to verify the robustness against the parameter variation as well as the reliable performance of the proposed robust filter.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.210-217
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• 2010

This paper presents integrated path planning and collision avoidance for an omni-directional mobile robot. In this scheme, the autonomous mobile robot finds the shortest path by the descendent gradient of a navigation function to reach a goal. In doing so, the robot based on the proposed approach attempts to overcome some of the typical problems that may pose to the conventional robot navigation. In particular, this paper presents a set of analysis for an omni-directional mobile robot to avoid trapped situations for two representative scenarios: 1) Ushaped deep narrow obstacle and 2) narrow passage problem between two obstacles. The proposed navigation scheme eliminates the nonfeasible area for the two cases by the help of the descendent gradient of the navigation function and the characteristics of an omni-directional mobile robot. The simulation results show that the proposed navigation scheme can effectively construct a path-planning system in the capability of reaching a goal and avoiding obstacles despite possible trapped situations under uncertain world knowledge.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.433-436
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• 2003

The estimation of the position and the orientation for the mobile robot constitutes an important problem in mobile robot navigation. Although the odometry can be used to describe the motions of the mobile robots, there inherently exist the gaps between the real robots and the mathematical model, which may be caused by a number of error sources contaminating the encoder outputs. Hence, applying the standard extended Kalman filter for the nominal model is not supposed to give the satisfactory performance. As a solution to this problem, a new robust extended Kalman filter is proposed based on the Krein space approach. We consider the uncertain discrete time nonlinear model of the mobile robot that contains the uncertainties represented as sum quadratic constraints. The proposed robust filter has the merit of being constructed by the same recursive structure as the standard extended Kalman filter and can, therefore, be easily designed to effectively account for the uncertainties. The simulations will be given to verify the robustness against the parameter variation as veil as the reliable performance of the proposed robust filter.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.931-938
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• 2008

In this paper, a novel localization algorithm robust to the unmodeled systematic odometry errors is proposed for low-cost non-holonomic mobile robots. It is well known that the most pose estimators using odometry measurements cannot avoid the performance degradation due to the dead-reckoning of systematic odometry errors. As a remedy for this problem, we tty to reflect the wheelbase error in the robot motion model as a parametric uncertainty. Applying the Krein space estimation theory for the discrete-time uncertain nonlinear motion model results in the extended robust Kalman filter. This idea comes from the fact that systematic odometry errors might be regarded as the parametric uncertainties satisfying the sum quadratic constrains (SQCs). The advantage of the proposed methodology is that it has the same recursive structure as the conventional extended Kalman filter, which makes our scheme suitable for real-time applications. Moreover, it guarantees the satisfactoty localization performance even in the presence of wheelbase uncertainty which is hard to model or estimate but often arises from real driving environments. The computer simulations will be given to demonstrate the robustness of the suggested localization algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.549-559
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• 2015

In this paper, an efficient dynamic reactive motion planning method for an autonomous vehicle in a dynamic environment is proposed. The purpose of the proposed method is to improve the robustness of autonomous robot motion planning capabilities within dynamic, uncertain environments by integrating a virtual plane-based reactive motion planning technique with a sensor fusion-based obstacle detection approach. The dynamic reactive motion planning method assumes a local observer in the virtual plane, which allows the effective transformation of complex dynamic planning problems into simple stationary ones proving the speed and orientation information between the robot and obstacles. In addition, the sensor fusion-based obstacle detection technique allows the pose estimation of moving obstacles using a Kinect sensor and sonar sensors, thus improving the accuracy and robustness of the reactive motion planning approach. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles in hostile dynamic environments.