Five-axis trajectory generation considering synchronisation and nonlinear interpolation errors

Author(s):  
Robert Ward ◽  
Burak Sencer ◽  
Bryn Jones ◽  
Erdem Ozturk

Abstract This paper presents a novel real-time interpolation technique for 5-axis machine tools to attain higher speedand accuracy. To realize computationally efficient real-time interpolation of 6DOF tool motion, a joint workpiece-machine coordinate system interpolation scheme is proposed. Cartesian motion of the tool centre point (TCP) isinterpolated in the workpiece coordinate system (WCS), whereas tool orientation is interpolated in the machinecoordinate system (MCS) based on the finite impulse response (FIR) filtering. Such approach provides several ad-vantages: i) it eliminates the need for complex real-time spherical interpolation techniques, ii) facilitates efficientuse of slower rotary drive kinematics to compensate for the dynamic mismatch between Cartesian and rotary axesand achieve higher tool acceleration, iii) mitigates feed fluctuations while interpolating near kinematic singulari-ties. To take advantage of such benefits and realize accurate joint WCS-MCS interpolation scheme, tool orientationinterpolation errors are analysed. A novel approach is developed to adaptively discretize long linear tool movesand confine interpolation errors within user set tolerances. Synchronization errors between TCP and tool orienta-tion are also characterized, and peak synchronization error level is determined to guide the interpolation parameterselection. Finally, blending errors during non-stop continuous interpolation of linear toolpaths are modelled andconfined. Advantages of the proposed interpolation scheme are demonstrated through simulation studies and vali-dated experimentally. Overall, proposed technique can improve cycle times up to 10% while providing smooth and accurate non-stop real-time interpolation of tool motion in 5-axis machining.

Author(s):  
Qin Hu ◽  
Youping Chen ◽  
Xiaoliang Jin ◽  
Jixiang Yang

Abstract Local corner smoothing method is commonly adopted to smooth linear (G01) tool path segments in computer numerical control (CNC) machining to realize continuous motion at transition corners. However, because of the highly non-linear relation between the arc-length and the spline parameter, and the challenge to synchronize the tool tip position and tool orientation, real-time and high-order continuous five-axis tool path smoothing and interpolation algorithms have not been well studied. This paper proposes a real-time C3 continuous corner smoothing and interpolation algorithm for five-axis machine tools. The transition corners of the tool tip position and tool orientation are analytically smoothed in the workpiece coordinate system (WCS) and the machine coordinate system (MCS) by C3 continuous PH splines, respectively. The maximum deviation errors of the smoothed tool tip position and the tool orientation are both constrained in the WCS. An analytical synchronization algorithm is developed to guarantee the motion variance of the smoothed tool orientation related to the tool tip displacement is also C3 continuous. The corresponding real-time interpolation method is developed with a continuous and peak-constrained jerk. Simulation results verify that the maximum deviation errors caused by the tool path smoothing algorithm are constrained, and continuous acceleration and jerk of each axis are achieved along the entire tool path. Comparisons demonstrate that the proposed algorithms achieve lower amplitude and variance of acceleration and jerk when compared with existing methods. Experiments show that the proposed five-axis corner smoothing and interpolation algorithms are serially executed in real-time with 0.5-ms cycle.


Author(s):  
Zongze Li ◽  
Ryuta Sato ◽  
Keiichi Shirase ◽  
Yukitoshi Ihara

Abstract Five-axis machining center, combined three linear and two rotary axes, has been increasingly used in complex surface machining. However, as the two additional axes, the machined surface under table coordinate system is usually different from the tool motion under machine coordinate system, and as a result, it is very tough to predict the machined shape errors caused by each axes error motions. This research presents a new kind of sensitivity analysis method, to find the relationship between error motions of each axis and geometric errors of machined shape directly. In this research, the S-shaped machining test is taken as a sample to explain how the sensitivity analysis makes sense. The results show that the presented sensitivity analysis can investigate how the error motions affect the S-shaped machining accuracy and predicted the influence of error motions on certain positions, such as the reversal errors of the axes around motion reversal points. It can be proved that the presented method can help the five-axis machining center users to predict the machining errors on the designed surface of each axes error motions.


Author(s):  
Shingo Tajima ◽  
Burak Sencer ◽  
Hayato Yoshioka ◽  
Hidenori Shinno

Abstract 5-axis machining is widely used to manufacture complex sculptured parts, such as impellers used in jet engines. In order to machine complex part surfaces, the surface is discretized by a series of short-segmented point to point linear segments by CAD/CAM systems. Smooth non-stop motion of the tool must be interpolated along those discrete tool-paths. This paper proposes novel discrete linear path smoothing algorithms to interpolate tool position and orientation commands synchronously for 5-axis machining. Finite Impulse Response (FIR) filtering based feed profiling technique is developed to generate a smooth tool-pose trajectory with both local and global smoothing functionality. Analytical techniques are proposed to confine the blending errors within user specified tolerances. The proposed technique is computationally efficient and suitable for real-time implementation on modern NC systems. Path blending errors are defined in both the Cartesian workpiece coordinate system for tool positioning errors and the spherical coordinate system for tool orientation errors. Both position and orientation contouring errors are controlled in each coordinate system with respect to the user-defined tolerances. Simulation results validate that the proposed FIR based corner smoothing algorithm can generate smooth and non-stop trajectories for 5-axis machining. It can lead to significant cycle time gain without jeopardizing part tolerances.


1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.


Author(s):  
Brij B. Gupta ◽  
Krishna Yadav ◽  
Imran Razzak ◽  
Konstantinos Psannis ◽  
Arcangelo Castiglione ◽  
...  

Author(s):  
Rakesh Kumar ◽  
Gaurav Dhiman ◽  
Neeraj Kumar ◽  
Rajesh Kumar Chandrawat ◽  
Varun Joshi ◽  
...  

AbstractThis article offers a comparative study of maximizing and modelling production costs by means of composite triangular fuzzy and trapezoidal FLPP. It also outlines five different scenarios of instability and has developed realistic models to minimize production costs. Herein, the first attempt is made to examine the credibility of optimized cost via two different composite FLP models, and the results were compared with its extension, i.e., the trapezoidal FLP model. To validate the models with real-time phenomena, the Production cost data of Rail Coach Factory (RCF) Kapurthala has been taken. The lower, static, and upper bounds have been computed for each situation, and then systems of optimized FLP are constructed. The credibility of each model of composite-triangular and trapezoidal FLP concerning all situations has been obtained, and using this membership grade, the minimum and the greatest minimum costs have been illustrated. The performance of each composite-triangular FLP model was compared to trapezoidal FLP models, and the intense effects of trapezoidal on composite fuzzy LPP models are investigated.


2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.


Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.


2021 ◽  
Vol 161 ◽  
pp. 104307
Author(s):  
Qun-Bao Xiao ◽  
Min Wan ◽  
Xue-Bin Qin ◽  
Yang Liu ◽  
Wei-Hong Zhang
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.


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