Zengxi Pan

Machining with flexible manipulator: toward improving robotic machining performance

This paper presents the critical issues and methodologies to improve robotic machining performance with flexible industrial robots. Compared with CNC machines, the stiffness of industrial robots is significantly lower, resulting in unacceptable quality and lower productivity. The problem is treated with a novel methodology that consists of stiffness modeling, real-time deformation compensation for quality and controlled material removal rate for efficiency. Experimental results show that higher productivity as well as better surface accuracy can be achieved, indicating a promising and practical use of industrial robots for machining applications that is not possible at present

Robotic machining from programming to process control: a complete solution by force control

Purpose – This paper aims to present the critical issues and methodologies to improve robotic machining performance with flexile industrial robots.Design/methodology/approach – A complete solution using active force control is introduced to address various issues during the robotic machining process.Findings – Programming complex couture parts without a CAD model is made easy by using force control functions such as lead‐through and path‐learning. The problem of process control is treated with a novel methodology that consists of stiffness modeling, real‐time deformation compensation for quality and controlled material removal rate for process efficiency.Originality/value – Experimental results showed that higher productivity as well as better surface quality can be achieved, indicating a promising and practical use of industrial robots for machining applications that is not available at present.

Recognizing human motions through mixture modeling of inertial data

Systems that recognize patterns in human motion are central to improvements in automation and human computer interaction. This work addresses challenges which arise in the context of recognizing arbitrary human actions from body-worn sensors. Chiefly the invariance to temporal scaling of events, coping with unlabeled data and estimating an appropriate model complexity. In order to deal with the severe case of unlabeled data, a method is proposed based on dynamic time alignment of Gaussian mixture model clusters for matching actions in an unsupervised temporal segmentation. In facilitation of this, an extensive corpus of continuous motion sequences composed of everyday tasks was recorded as analysis scenarios. The technique achieved an average accuracy of 72% for correctly merging actions performed by different participants. With labeled data and recognition models designed for particular classes, an accuracy of 89% was achieved in classifying the motion of participants left out of the modeling process. These results are contrasted with benchmark methods for recognition in a systematic validation revealing, in particular, an improved performance for mixture model prediction utilizing segments. HighlightsA method is proposed for unsupervised segment clustering of human motion capture data.Gaussian mixture models and dynamic time warping are used to compare similar data sequences.Human motion capture data was collected with a set of body-worn inertial sensors.The resultant classifier is compared with k-nearest-neighbor and support vector machine approaches.

Human motion capture sensors and analysis in robotics

Purpose – The purpose of this paper is to provide a review of various motion capture technologies and discuss the methods for handling the captured data in applications related to robotics.Design/methodology/approach – The approach taken in the paper is to compare the features and limitations of motion trackers in common use. After introducing the technology, a summary is given of robotic‐related work undertaken with the sensors and the strengths of different approaches in handling the data are discussed. Each comparison is presented in a table. Results from the authors experimentation with an inertial motion capture system are discussed based on clustering and segmentation techniques.Findings – The trend in methodology is towards stochastic machine learning techniques such as hidden Markov model or Gaussian mixture model, their extensions in hierarchical forms and non‐linear dimension reduction. The resulting empirical models tend to handle uncertainty well and are suitable for incrementally updating mo...

Motion capture in robotics review

This survey reviews motion capture technologies and the current challenges associated with their application in robotic systems. Various sensor systems used in current literature are introduced and evaluated based on the relative strengths and weaknesses. Some research problems pursued with these sensors in robotics are reviewed and application areas are discussed. Significant methodologies in analysing the sensor data are discussed and evaluated based on the perceived benefits and limitations. Finally, results from experimentation with an inertial motion capture system are shown based on clustering and segmentation techniques.

Machining with Flexible Manipulators: Critical Issues and Solutions

The automotive industry represents the fastest-growing market segment of the aluminium industry, due to the increasing usage of aluminium in cars. The drive behind this is not only to reduce the vehicle weight in order to achieve lower fuel consumption and improved vehicle performance, but also the desire for more sustainable transport and the support from new legislation. Cars produced in 1998, for example, contained on average about 85 Kg of aluminium. By 2005, the automotive industry will be using more than 125 Kg of aluminium per vehicle. It is estimated that aluminium for automotive industry alone will be a 50B

Robotic machining from programming to process control

/year market. Most of the automotive aluminium parts start from a casting in a foundry plant. The downstream processes usually include cleaning and pre-machining of the gating system and riser, etc., machining for high tolerance surfaces, painting and assembly. Today, most of the cleaning operations are done manually in an extremely noisy, dusty and unhealthy environment. Therefore, automation for these operations is highly desirable. However, due to the variations and highly irregular shape of the automotive casting parts, solutions based on CNC machining center usually presented a high cost, difficult-to-change capital investment. To this end, robotics based flexible automation is considered as an ideal solution for its programmability, adaptivity, flexibility and relatively low cost, especially for the fact that industrial robot is already applied to tend foundry machines and transport parts in the process. Nevertheless, the foundry industry has not seen many success stories for such applications and installations. Currently, more than 80% of the application of industrial robots is still limited to the fields of material handling and welding. (Figure 1) The major hurdle preventing the adoption of robots for material removal processes is the fact that the stiffness of today’s industrial robot is much lower than that of a standard CNC machine. The stiffness for a typical articulated robot is usually less than 1 N/μm, while a standard CNC machine center very often has stiffness greater than 50 N/μm. Most of the existing literature on machining process, such as process force modelling (Kim et al., 2003; Stein & Huh, 2002], accuracy improvement (Yang 1996) and vibration suppression (Budak & Altintas, 1998) are based on the CNC machine. Research in the field of robotic machining is still focused on accurate off-line programming and calibration (Chen & Hu, 1999; Sallinen & Heikkila, 2000; Wang et al., 2003a, 2003b). Akbari et al. (Akbari & Higuchhi, 2000) describe a tool angle adjustment method in a grinding application with a

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers

This paper presents the critical issues and methodologies to improve robotic machining performance with industrial robots. A complete solution using active force control is introduced to address various issues arouse during the robotic machining process. Programming complex contour parts without a CAD model is made easy using force control functions such as lead-through and path-learning. The problem of process control is treated with a novel methodology that consists of real-time deformation compensation for quality and controlled material removal rate (CMRR) for process efficiency. Experimental results show that higher productivity as well as better surface quality can be achieved, indicating a promising and practical use of industrial robots for machining applications that is not available at present.

Characterization of in-situ alloyed and additively manufactured titanium aluminides

This paper presents an approach for learning robust models of humanoid robot trajectories from demonstration. In this formulation, a model of the joint space trajectory is represented as a sequence of motion primitives where a nonlinear dynamical system is learned by constructing a hidden Markov model (HMM) predicting the probability of residing in each motion primitive. With a coordinated mixture of factor analyzers as the emission probability density of the HMM, we are able to synthesize motion from a dynamic system acting along a manifold shared by both demonstrator and robot. This provides significant advantages in model complexity for kinematically redundant robots and can reduce the number of corresponding observations required for further learning. A stability analysis shows that the system is robust to deviations from the expected trajectory as well as transitional motion between manifolds. This approach is demonstrated experimentally by recording human motion with inertial sensors, learning a motion primitive model and correspondence map between the human and robot, and synthesizing motion from the manifold to control a 19 degree-of-freedom humanoid robot.

Control of autonomous airship

Titanium aluminide components were fabricated using in-situ alloying and layer additive manufacturing based on the gas tungsten arc welding process combined with separate wire feeding of titanium and aluminum elements. The new fabrication process promises significant time and cost saving in comparison to traditional methods. In the present study, issues such as processing parameters, microstructure, and properties are discussed. The results presented here demonstrate the potential to produce full density titanium aluminide components directly using the new technique.