On-Line Path Planning With Collision Avoidance for Coordinate-Controlled Robotic Manipulators

2017 ◽  
Author(s):  
Tuomo Kivelä ◽  
Jouni Mattila ◽  
Jussi Puura ◽  
Sirpa Launis
Keyword(s):  
Control System ◽  
Collision Avoidance ◽  
Motion Control ◽  
Degrees Of Freedom ◽  
Point Clouds ◽  
Robotic Manipulators ◽  
Cloud Data ◽  
Shortest Distance ◽  
Hydraulic Manipulator ◽  
Line Path

This paper presents a generic method for generating joint trajectories for robotic manipulators with collision avoidance capability. The coordinate motion control system of the heavy-duty hydraulic manipulator resolves joint references so that a goal pose can be reached in real-time without any collisions. The control system checks whether any part of the manipulator is at risk of colliding with itself, with other manipulators, or with environmental obstacles. If there is a risk of collision, then the collision server searches the points where the collision is about to occur and calculates the shortest distance between the colliding objects. The collision server retains static and dynamic point clouds, and it uses point cloud data to calculate the shortest distance between the colliding objects. The point clouds on the server are kept up to date with the manipulators’ joint sensors and an external surveillance system. During coordinated motion control, the joint trajectories of the hydraulic manipulator are modified so that collisions can be avoided, while at the same time, the trajectory of the end-effector maintains its initial trajectory if possible. Results are given for a seven degrees of freedom redundant hydraulic manipulator to demonstrate the capability of this collision avoidance control system.

scholarly journals Adaptive Control Design and Stability Analysis of Robotic Manipulators

Actuators ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 89 ◽  
Author(s):  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Stability Analysis ◽  
Motion Control ◽  
Force Control ◽  
Control Design ◽  
Robotic Manipulators ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
Model Reference Adaptive

In this paper, the author presents the adaptive control design and stability analysis of robotic manipulators based on two main approaches, i.e., Lyapunov stability theory and hyperstability theory. For the Lyapunov approach, the author presents the adaptive control of a 2-DOF (degrees of freedom) robotic manipulator. Furthermore, the adaptive control technique and Lyapunov theory are subsequently applied to the end-effector motion control and force control, as in most cases, one only considers the motion control (e.g., position control, trajectory tracking). To make the robot interact with humans or the environment, force control must be considered as well to achieve a safe working environment. For the hyperstability approach, a control system is developed through integrating a PID (proportional–integral–derivative) control system and a model reference adaptive control (MRAC) system, and also the convergent behavior and characteristics under the situation of the PID system, model reference adaptive control system, and PID+MRAC control system are compared.

scholarly journals Object recognition and localization from 3D point clouds by maximum-likelihood estimation

2017 ◽  
Vol 4 (8) ◽  
pp. 160693 ◽  
Author(s):  
Harshana G. Dantanarayana ◽  
Jonathan M. Huntley
Keyword(s):  
Maximum Likelihood ◽  
Degrees Of Freedom ◽  
Likelihood Estimation ◽  
Point Clouds ◽  
Theoretical Description ◽  
Alignment Error ◽  
Computationally Efficient ◽  
Cloud Data ◽  
Depth Images ◽  
3D Point Clouds

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

Development of a Marine Self-Balancing Table with Two-Axis Motion Control System

2014 ◽  
Vol 577 ◽  
pp. 342-345
Author(s):  
Minh Phu Huynh ◽  
Shyh Chour Huang
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Motion Control ◽  
Fuzzy Logic Control ◽  
Degrees Of Freedom ◽  
Experimental Results ◽  
Motion Control System ◽  
Two Degrees Of Freedom ◽  
Logic Control

The purpose of this research is to design and construct a marine self-balancing table with two-axis motion control system. This table has two degrees of freedom, is fixed in an ocean-going ship and can be self-balanced when the ship is moving in the sea. Control of the table is based on fuzzy logic control. A prototype of the marine self-balancing table is also manufactured in this research. Experimental results show that this table can be self-balanced with a tolerance of 10.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Ultrasonic sensor system for the uses in intelligent motion control system of a mobile robots group

2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Motion Control ◽  
High Speed ◽  
Ultrasonic Sensor ◽  
Sensor System ◽  
Motion Control System ◽  
Speed Sensor ◽  
Working Space

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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