A comparison of mobile robot pose estimation using nonlinear filters: simulation and experimental results

2015 ◽  
Vol 5 (2/3) ◽  
pp. 92 ◽  
Author(s):  
Zongwen Xue ◽  
Howard M. Schwartz
Keyword(s):  
Mobile Robot ◽  
Pose Estimation ◽  
Nonlinear Filters ◽  
Experimental Results

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

scholarly journals Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1254 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Shiou-Yun Jeng ◽  
Cheng-Jian Lin
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Search Algorithm ◽  
Experimental Results ◽  
Average Error ◽  
Stopover Site ◽  
Accumulated Reward ◽  
Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

Research on Pose Estimation for Electric Vehicle’s Fast-Swap Battery Box Based on Visual Information

2012 ◽  
Vol 253-255 ◽  
pp. 2252-2257
Author(s):  
Yu Ming Wu ◽  
Shuo Liu ◽  
Gao Yang Zhang ◽  
Xiao Yan Yin ◽  
Ming Yu Zhao ◽  
...  
Keyword(s):  
Coordinate System ◽  
Pose Estimation ◽  
Visual Information ◽  
Experimental Results ◽  
Noise Effects ◽  
Simulation Results ◽  
Camera Coordinate System

For the reason of difficult to get battery box pose information, we research the battery box pose measure method based on visual information. We get the coplanar four points at the lines constraints which extracted form image. We get the pose relationship between the battery box coordinate system and camera coordinate system, and then calculate the average of the measure results to reduce noise effects for measure precision. Simulation results show that the method through calculate average of the measure results can effectively reduce noise effects for measure accuracy. The actual experimental results show that the pose estimate accuracy is meet robot requirements for battery swap.

Localization of Mobile Robot Based on Least Square Method

2013 ◽  
Vol 347-350 ◽  
pp. 808-811
Author(s):  
Jia Lu Li ◽  
Lin Bing Long ◽  
Bao Feng Zhang
Keyword(s):  
Motion Estimation ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Least Squares ◽  
Least Square Method ◽  
Least Square ◽  
Experimental Results ◽  
Sift Algorithm ◽  
Key Techniques ◽  
Point Detection

Localization is the basis for navigation of mobile robots. This paper focuses on key techniques of localization for mobile robots based on vision. Firstly, the specific measures and steps of the algorithm are analyzed and researched in depth. In the study, SIFT algorithm combined with epipolar geometry constraint is used on the environment feature point detection, matching and tracking. And the method of RANSAC combined with the least squares is used to obtain accurate results of the motion estimation. Then the necessary experiments are carried out to verify the correctness and effectiveness of algorithms. The experimental results verified the accuracy of the improved algorithm.

Image-Based Motion Analysis for Self-Reconfigurable Mobile Robot With Integrated Docking

2019 ◽  
Author(s):  
Shubhdildeep S. Sohal ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Mobile Robot ◽  
Motion Analysis ◽  
Visual Servoing ◽  
Experimental Results ◽  
Sensory Data ◽  
Simulated Environment ◽  
Locomotion Behavior ◽  
Coupling Performance ◽  
Design Integration ◽  
Hybrid Locomotion

Abstract This paper presents the design integration and experimental results of target-based autonomous locomotion of a Self-Reconfigurable Mobile Robot. Uncertainties in the sensory data can accumulate the misalignments in locomotion behavior of the robot. Such misalignments can result in a poor coupling performance resulting in the failure of the overall docked system. Therefore, it is desirable for a robot to be capable of mechanically tolerating such misalignments. As a result, a Hybrid-Wheeled mobile robot, interfaced with a 2-DOF, high misalignment tolerant coupling (GHEFT) mechanism is presented in this paper. This combined assembly is used as a source of locomotion for autonomous docking in a multi-robot assembly using Image-Based Visual Servoing (IBVS). The resulting output is then implemented in a simulated environment for the autonomous locomotion of the robot. Experimental results demonstrate the feature motion and trajectory followed under the hybrid locomotion of the robot.

scholarly journals Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoyuan Ren ◽  
Libing Jiang ◽  
Zhuang Wang
Keyword(s):  
Pose Estimation ◽  
Experimental Results ◽  
Single Image ◽  
Space Object ◽  
Shape Model ◽  
Structure Information ◽  
Space Objects ◽  
Specific Object ◽  
Unknown Objects ◽  
Unknown Object

Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

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