Since the concept of industry 4.0 was proposed in 2011, the trend of industry 4.0 has been surging around the world. Intelligent factory is one of the main research points in the industry 4.0 era. In order to improve the intelligent level of the factory, the connection-and-cognition ability has to be established for the factory and its equipment. Connection builds data pipes among equipment and systems while cognition automatically turns the data into knowledge. In an intelligent factory, industrial robot plays a leading role. Hence, the aim of this paper is to synthetically study connection and cognition of industrial robots in intelligent factories. To be specific, open platform communications unified architecture (OPC UA) is applied to establish heterogeneous connection of industrial robots with factory management software. A long short-term memory (LSTM) joint auto encoder method is proposed to establish the unsupervised anomaly detection cognition ability for industrial robot process (e.g. grinding, welding and assembling). In summary, this study puts OPC UA and LSTM auto encoder technology together to study heterogeneous connection and process anomaly detection of industrial robots in intelligent factory. The experimental results showed that the proposed method successfully realized heterogeneous connection of an industrial robot and detected process anomaly from the robot built-in sensors’ data.

In arc welding processes, real time control algorithms have to be developed in order to achieve desired weld quality. However, there could exist big uncertainties and noise in the process, which nullifies the conventional online control method. Besides, due to the modelling difficulty and low experimental efficiency, this task is usually performed offline. In this paper, a real time parameter optimization method is developed to find the optimal welding parameters to achieve desired characteristic performance. Gaussian Process Regression (GPR), a non-parametric modelling technique, is employed to model the relationship between input welding parameters and output characteristic performance. The GPR surrogated Bayesian Optimization Algorithm (GPRBOA) is proposed to optimize the welding parameters. Lower Confidence Bound (LCB) and Upper Confidence Bound (UCB) acquisition functions are utilized. Gas tungsten arc welding experiments were performed and the corresponding experimental data are collected and utilized to validate the proposed modelling method. The predicted characteristic performance is compared with the original data and it shows that the modelling method can accurately predict the weld bead geometry. The control algorithms were demonstrated and the results are presented. This paper opens a door for real time parameter tuning to achieve desired performance, hence the proposed method will innovate the arc welding process.

This article develops a set space visual servoing method that is quiet different from state-of-the-art approaches. Our approach does not require complex image processing techniques for the extraction, matching and tracking of image features. Instead, it only requires a simple matching algorithm and builds visual errors in set space. Each error is mainly related to one degree of freedom of the camera; therefore, we can design a decoupled control law. This control law is robust and does not require calibrated inner parameters of the camera. Our approach has been validated in 4-degree-of-freedom (DOF) visual servoing simulations with common image patterns and 6-DOF visual servoing experiments with specific image patterns. These visual servoing tasks are properly achieved even when partial occlusions occur.

This paper proposes a driver assistance system with a collaborative control framework between the human driver and a Collision Avoidance System (CAS) for vehicles on the road while considering the driver drowsiness status. Driver drowsiness detection is performed with two inputs: driver's facial data from a camera mounted in front of the driver and steering wheel data from the car controller system. We use the driver's drowsiness state as an input to the collaborative control framework in which the CAS algorithm runs in parallel with the human control and only intervenes under certain situations to assist the human driver. Experiments were performed on a simulated vehicle driving testbed to evaluate our drowsiness detection system and demonstrate the effectiveness of the proposed collaborative control framework.

Mobile robot typically has limited on-board resources and may be applied in different indoor environment. Thus, it is necessary that they can learn a map and navigate themselves autonomously with lightweight algorithms. A novel topological map-building-based localization and navigation method is proposed in this paper. Based on the depth curve provided by a 3D sensor, a progressive Bayesian classifier is developed to realize direct corridor type identification. Instead of extracting features from single observation, information from multi-observations are fused to achieve a more robust performance. A topological map generation and loop closing method are proposed to build the environment map through autonomous exploration. Based on the derived map and the Markov localization method, the robot can then localize itself and navigate freely in the indoor environment. Experiments are performed on a recently built mobile robot system, and the results verify the effectiveness of the proposed methodology.

In the high precision robotic assembly processes, the process parameters have to be tuned in order to adapt to variations and satisfy the performance requirements. However, because of the modeling difficulty and low efficiency of the existing solutions, this task is usually performed offline. In this paper, an online parameter optimization method is developed. Gaussian Process Regression(GPR) is utilized to model the relationship between the process parameters and system performance. The GPR surrogated Bayesian Optimization Algorithm(GPRBOA) is proposed to optimize the process parameters. To reduce the risk of converging to a local minimum, a random variation factor is added to the Lower Confidence Bound(LCB) acquisition function to balance the exploration and exploitation processes. To deal with the computational burden of GPR, a switching criterion is proposed to coordinate the optimization process and production process to reduce the computational complexity. Experiments were performed using a peg-in-hole process. The experimental results verify the effectiveness of the proposed algorithm and demonstrate its efficiency and accuracy compared to Design Of Experiment(DOE) methods. The proposed method is the first attempt of model-driven assembly process parameter optimization and will generate big economic impact.

Robot teaching is necessary for the current industrial robot applications. Because work stations have to be stopped to perform teaching processes, the manufacturing efficiency is decreased. In this paper we propose to utilize an uncalibrated vision system mounted on a mobile robot (“Adult” robot) with learning capability to supervise a group of fixed robots (“Child” robots) to accomplish a robot teaching task automatically without stopping work stations. To increase the system flexibility, hand-eye calibration and calibration between the robots are eliminated. A Partial Observable Markov Decision Process(POMDP) is formulated and solved using the Successive Approximation of the Reachable Space under Optimal Policies (SARSOP) algorithm to enable the teaching process using image features with uncertainties. The proposed algorithm was tested using the “adult” robot to teach a “child” robot to perform a high accuracy peg-in-hole assembly process. The experimental results verify the effectiveness of the proposed approach. The proposed method can also be used in other areas to enable robot teaching.

Endurance is a critical problem that most flying robots will definitely encounter. Inspired by flying animals in nature that take frequent short flights with periods of perching in between, we propose an innovative mechanism with flying and adhesion to solve this problem. Previously, we have developed some prototypes of flying and adhesion robots. However, when the robots switch between flying and adhesion, it is difficult to control the contact force; moreover, the robots could be damaged because of the abnormal contact with the environment. Therefore, we propose an impedance control approach for bio-inspired flying and adhesion robots to have smooth contact with the environment. The dynamic model of a bio-inspired robot is described, and the proposed impedance control method is applied to regulate the contact force with the environment. The bio-inspired flying and adhesion robot performs several phases of desired missions in the sequential manner. Firstly, the robot performs position control to approach the desired perch position. Secondly, the robot contacts with the environment and regulate the contact force. Both simulation and experiments were performed to validate the proposed method. The results verified the feasibility of the proposed control methods in controlling a bio-inspired flying and adhesion robot.

The process parameters of high precision robotic assembly have to be tuned in order to deal with part variations and system uncertainties. Some methods such as design-of-experiment, artificial neural network and genetic algorithms have been proposed to optimize these parameters offline. However, these parameters have to be retuned for different batches due to part variations, which increases the production cost and lowers the manufacturing efficiency. Therefore new methods have to be developed to solve the problem. Because of the complexity of high precision assembly process, it is challenging to build a physical model to establish the relationship between an assembly process and its process parameters. Therefore we propose an assembly process modeling method based on support vector regression that constructs a model by observing the relationship between the assembly parameters and assembly output. The effectiveness and accuracy of the support vector regression based algorithm are further demonstrated by experiments using a robotic valve body assembly process in automotive manufacturing. The results show that the proposed method is capable of modeling complex assembly processes.

Autonomous Industrial Mobile Manipulators (AIMMs) combine the advantages of both mobile robots and industrial manipulators, therefore they have great mobility, flexibility and functionality. However, great challenges arise when AIMMs are expected to perform various tasks in unstructured or semi-structured environments. Since AIMMs are typically configured in the indoor environments, indoor localization & navigation is one of these challenges. A novel topological indoor localization & navigation method is proposed in this paper. Based on the depth information provided by a popular Kinect sensor, a progressive Bayesian classifier is developed to realize direct corridor type identification. Instead of extracting features from single observation, information from multi-observations are fused to achieve a more robust performance. With the ability of directly determining the corridor types, a topological map generation and loop closing method is proposed to build the environment map through autonomous exploration. By using the Markov Localization and trajectory planning method, the robot can localize itself and navigating freely in the indoor environment. Experiments are performed on a recently built AIMM system, and the results verify the effectiveness of the proposed methodology.

Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized using motion data from wearable motion sensors mounted on a human subject. Combined with pre-learned models of activity-to-furniture type association and robot pose estimates, the robot can determine the distribution of the furniture types on the 2D metric map. Simulations and real world experiments demonstrate that the proposed method is able to create a reliable metric map with accurate semantic information.

Surface manufacturing is a process of adding material to or removing material from the surfaces of a part, where industrial robots are usually used. Robot programming for these applications is still time consuming and costly. Typical teaching methods are not cost effective and efficient. There are many offline programming methods developed to reduce the robot programming effort. However, these methods suffer many practical issues, such as cable/hose tangling, robot configuration, collision and reachability. To solve these problems, this paper discusses a new method to generate robot programs based on existing ones in the database, which contains not only the robot paths, but also the programmers' knowledge and process parameters. Since industrial robots have been used in production for decades, there are many robot programs for different parts generated by the robot programmers. Some of these parts are quite similar, such as car bodies. Therefore, these robot programs can be transformed to generate new ones with some adjustments based on the geometry of new parts. In this paper, a robot path generation method is developed based on the existing robot paths in the database. Experiments were performed to validate the developed methodology. The results are very promising in reducing the programming efforts in surface manufacturing.

The paper aims to develop an automated wheel loading system in the trim-and-final assembly in automotive manufacturing. Currently most of the trim-and-final assemblies are still done manually since the production lines are typically moving randomly. Industrial robots are hardly used to perform any assembly tasks on the randomly changing environments because it is difficult for conventional industrial robots to adjust to any sort of change. Therefore, more intelligent industrial robotic system has to be developed to adopt the random motion of the moving production line for the wheel loading process. This paper presents an intelligent robotics system that performs the wheel loading process while the car is moving randomly with the production line using a synergic combination of visual servoing and force control technology. The developed intelligent robotic technology has been successfully implemented to assembly the wheel onto the car. This practical solution of performing wheel loading on the moving production line, which is not available on the current industrial robot market, can save a lot of money and increase the assembly quality for automotive manufacturing. Since the developed platform is based on the synergic combination of visual servoing and force control technology, it can be used in other areas, such as seam tracking, battery loading and seat loading etc.

This paper describes an industrial robot joint offset calibration method called the virtual line-based single-point constraint approach. Previous methods such as using CMM, laser trackers or cameras are limited by the cost or the resolution. The proposed method relies mainly upon a laser pointer attached on the end-effector and single position-sensitive detector (PSD) arbitrarily located on the workcell. The automated calibration procedure (about three minutes) involves aiming the laser lines loaded by the robot towards the center of the PSD surface from various robot positions and orientations. The intersections of each pair of laser lines eventually should converge to the same point after compensating the joint offsets. An optimization model and algorithm have been formulated to identify the robot offset. For the highly precise feedback, a segmented PSD with a position resolution of better than 0.1 ¿m is employed. The mean accuracy of robot localization is up to 0.02 mm, and the mean error of the parameter identification is less than 0.08 degrees. Both simulations and experiments implemented on an ABB industrial robot verify the feasibility of the proposed method and demonstrated the effectiveness of the developed calibration system. The goal of fast, automated, low-cost, and high precision offset calibration are achieved.

Cast aluminum wheels must be deburred and polished before chrome plating operation. Manual deburring and polishing is tedious and monotonous. Industrial robots are attracting more and more attention for the deburring applications. However, Each wheel path program was taking eight to ten weeks to create because the position and orientation of the deburring tool must be accurately identified based on the curvature of the wheel. To solve this problem, we developed a practical 6 DOF robot path generation method using hybrid force and visual servoing methodology. The force servoing keeps the robot tool continuously contacting with the wheel surface and the visual servoing controls the robot tool to follow a marked tool path on the wheel while the position and orientation are controlled and recorded. A robot path is then generated from the recorded data. Experiments are performed and the experimental results demonstrate that the developed algorithm can be used to automatically generate accurate 6 DOF robot paths for deburring aluminum wheels in less than an hour. This significantly reduces the robot programming time. The developed technology can also be used for robot path generation in many other manufacturing applications, such as welding and strip painting

Nanoassembly using atomic force microscopy (AFM) is a promising technique for nanomanufacturing. Most AFM-based nanoassembly schemes are implemented either manually using haptic devices or in an interactive way between the users and the atomic force microscope images. These schemes are time consuming and inefficient. Therefore, the computer-aided design (CAD)-guided automated nanoassembly using AFM is desirable for nanomanufacturing. In this paper, a general framework for CAD-guided automated nanoassembly using AFM is developed. Based on the CAD model of a nanostructure, the manipulation paths for both nanoparticles and nanorods are generated automatically. A local scanning method is developed to compensate for the random drift that may cause the failure of the nanoassembly. The experimental results demonstrate that the developed general framework can be employed to manufacture nanostructures efficiently. The research work opens a door to the CAD-guided automated nanomanufacturing using AFM. Note to Practitioners-Atomic force microscope (AFM)-based nanoassembly will lead to potential breakthroughs in manufacturing new revolutionary industrial products because many potential nanostructures and nanodevices are asymmetric, which cannot be manufactured using self-assembly only. In order to increase the efficiency and accuracy of AFM-based nanoassembly, automated computer-aided-design (CAD)-guided nanoassembly is desirable to manufacture nanostructures and nanodevices. Based on the CAD model, the environment model and the model of the nanoobjects, collision-free paths are generated to control the AFM tip to manipulate nanoobjects. A local scanning method is developed to obtain the actual position of each nanoobject to compensate for the random drift. Since the building materials of nanostructures and nanodevices may include nanoparticles, nanorods, nanowires, nanotubes, etc., automated path planning algorithms are developed for both nanoparticles and nanorods. The experimental results show that the developed general framework can be used to manufacture nanostructures more efficiently.

Nanomanufacturing using Atomic Force Microcopy has been widely investigated. Most of nanomanipulation schemes go through the scan-design-manipulation-scan cycle manually which is time consuming and inefficient. Automated AFM tip path planning is desirable for nanomanufacturing, but does not receive much attention. In this paper, a CAD guided automated nanomanufacturing system is presented. Based on the CAD model of a nanostructure, the manipulation paths for both nanoparticles and nanorods are generated automatically. A local scanning method is developed to compensate for the random drift that may cause the failure of the nano-assembly. The experimental results demonstrate that the developed algorithm can be employed to manufacture nanostructures efficiently. The research work opens a door to the CAD guided automated nanomanufacturing.

This paper addresses the path planning problem of a mobile sensor, which is a micro Crawler robot equipped with a Eddy Current probe, for aircraft rivet inspection. Due to the specific movement characteristic of the Crawler robot, the path, or the rivet sequence should enable the Crawler robot to realize the localization-and-reposition process. Therefore a final path is subject to the following three requirements: i) the distance between any two consecutive rivets on the path should be less than a threshold distance; ii) the number of turns should be minimized and iii) the overall distance should be minimized. In this paper, a novel algorithm is developed to generate the required path. This algorithm first identifies the minimum set of line segments partitioning all the rivets and then connects the line segments to minimize the overall distance. Simulation results are provided to validate the proposed algorithm.

This paper introduces a fully automated dimensional inspection (ADI) system using a digital area sensor. Instead of measuring a part surface point-by-point using a Coordinate Measuring Machine (CMM), an ADI system can measure a part surface patch-by-patch. Therefore, the inspection time can be significantly reduced. Many existing sensor planners were developed for one or two cameras. In the proposed ADI system, the sensor planner is developed for a digital area sensor, which is made by a CCD camera and a digital projector. In application, sensor viewpoints are estimated automatically. On each viewpoint, thousands of points on part surfaces can be measured in only several seconds.

Automatic chopper gun trajectory generation for spray forming is highly desirable for today's automotive manufacturing. Generating chopper gun trajectories for free-form surfaces to satisfy the given requirements is still highly challenging due to the complex geometry of free-form surfaces and the spray gun model. A CAD-guided chopper gun trajectory generation system for both uniform and nonuniform material distribution of free-form surfaces has been developed in our previous work. A material distribution model is also presented. To verify the developed algorithms, experiments were performed. In this paper, the experimental results are presented and compared with the simulation results. The results demonstrate that the developed trajectory generation system can be applied to generate trajectories for free-form surfaces such that the material distribution requirements can be satisfied. Also, the material distribution model can be used to compute the material distribution for free-form surfaces. This trajectory generation method can also be applied to generate trajectories for many other CAD-guided robot trajectory planning applications.

This paper presents new calibration methods for a robotic area sensing system developed for industrial manufacturing inspection. A pixel-to-pixel calibration scheme is introduced to obtain standoff and baseline distance of the developed area sensor. In the three-dimensional (3D) space, the area sensor also has an offset angle in a plane parallel to the reference plane. Calibration of this offset angle improves the measurement accuracy. This pixel-to-pixel scheme is particularly useful for calibrating a 3D optical area sensor with off-the-shelf lenses. An exploding vector method was also developed to transform the measured depth map to a 3D point cloud. A robotic area sensing system was developed by integrating an area sensor prototype and a robot sensor planning system. Calibration of the integrated system includes robot calibration, part calibration, and robot hand-eye calibration. Calibration error of the robot system is related to the area sensor positioning accuracy in the robotic system. Experimental results show a successful practice of the developed robotic area sensing system. Measurement performance of the developed system can be improved if advanced lenses are adopted.

Spray forming is an emerging manufacturing process. The automated tool planning for this process is a nontrivial problem, especially for geometry-complicated parts consisting of multiple freeform surfaces. Existing tool planning approaches are not able to deal with this kind of compound surface. This paper proposes a tool-path planning approach which optimizes the tool motion performance and the thickness uniformity. There are two steps in this approach. The first step partitions the part surface into flat patches based on the topology and normal directions. The second step determines the tool movement patterns and the sweeping directions for each flat patch. Based on the above two steps, optimal tool paths can be calculated. Experimental tests are carried out on automotive body parts and the results validate the proposed approach. Note to Practitioners-This paper was motivated by the problem of automatically planning tool paths for spray forming using Programmable Powdered Preforming Process (P4) technology. However, the proposed approach can be applied to other surface manufacturing applications such as spray painting, spray cleaning, rapid tooling, etc. Existing tool planning approaches are not able to handle complicated, multi-patch surfaces. This paper proposes a methodology to partition complicated surfaces into easy-to-handle patches and generate tool paths with optimized thickness uniformity and tool motion performance. We tested the approach using simulation on sample automotive body parts and proved its feasibility. However, this approach requires that the parts to be sprayed belong to the sheet-metal type so that the part geometry can be analyzed on a plane. In our future research, we will run physical tests on actual parts and investigate the deposition effects on the thickness uniformity.

Automated chopper gun trajectory planning for spray forming is highly desirable for today's automotive manufacturing. Generating chopper gun trajectories for free-form surfaces to satisfy thickness requirements is still highly challenging due to the complexity of problems. Automated trajectory planning for constant material distribution has been widely studied. However, achieving a variational (non-uniform) material distribution has not been addressed. A CAD-guided chopper gun trajectory planning system with non-uniform material distribution for free-form surfaces is presented. A multi-objective constrained optimization problem is formulated. The simulation results have shown that this system achieves performance required by production applications. This trajectory planning algorithm can also be applied to generate trajectories for other CAD-guided robot trajectory planning applications.

Assembly of nano-structures involves manipulation of nanoparticles, nano-rods, nanowires and nanotubes. Modelling the behavior of a nano-rod or a nanotube pushed by an AFM tip is much more complex than that of a nano-particle because in the case of the nano-particle usually only translation occurs while for the nano-rod and nanotube both translational and rotational motion occurs during manipulation. In this work, the behavior of nano-rods under pushing is theoretically analyzed and the interaction among tip, substrate and nano-rods has been modelled. Based on these models, the real-time interactive forces are used to update the AFM image. The real-time visual display combined with the real-time force feedback provides an augmented reality environment in which the operator not only can feel the interaction forces but can also observe the real-time changes of the nano-environment. The new developed augmented reality system capable of manipulating not only nanoparticles but also nano-rods makes nano-assembly using AFM based nanomanipulation system feasible and applicable.

Spray forming is an emerging manufacturing process. The automated tool planning for this process is a nontrivial problem, especially for geometry-complicated parts consisting of multiple freeform surfaces. Existing tool planning approaches are not able to deal with this kind of compound surfaces. This paper proposes a tool path planning approach which considers the tool motion performance and the thickness uniformity. There are two steps in this approach. The first step partitions the part surface into flat patches based on its topology and normal directions. The second step determines the tool movement patterns and the sweeping directions for each flat patch. Based on that, optimal tool paths can be calculated. Experimental tests are carried out on automotive body parts and the results validate the proposed approach.

The development of nanomanufacturing technologies will lead to potential breakthroughs in manufacturing of new industrial products. Nanomanufacturing by manipulating nanoparticles using an atomic force microscope is desirable to manufacture asymmetric nanodevices and nanostructures. The complexity of nanomanufacturing requires positioning, manipulating and assembling nanoparticles. Typical manual nanomanipulation is time-consuming and inefficient. Automated path planning is desirable for nanomanufacturing, but does not receive much attention. In this paper, a general framework is developed to manufacture nanostructures and nanodevices. An automated tool path planning algorithm is presented. Simulations are performed to test the generated paths. The generated paths are also implemented to manipulate nanoparticles to manufacture nanostructures automatically. The simulation and experimental results are consistent. The general framework can also be extended to manipulate other nanoobjects.

DNA electronics circuits require an efficient way to accurately position and individually manipulate DNA molecules. The recent development of atomic force microscopy (AFM) seems to be a promising solution. We have recently developed an AFM based augmented reality system. This new system can provide both real-time force feedback and real-time visual feedback during nanomanipulation. We have shown that nano-imprinting and manipulation of nano-particles and nano-rods can be easily performed under assistance of the augmented reality system. In this research, the system's ability is extended to manipulation of DNA molecules. Using a polynomial fitting method, the deformation of DNA molecules is displayed in real time in the augmented reality system during manipulation. Indeed, DNA molecules adopt many different structures including kinks, bends, bulges and distortions. These different structures and inappropriate physical contacts may result in the controversy of DNA conductivity reported over the last decade. The AFM based nanomanipulation system can be used either as a nanolithography tool to make small gap electrodes or a nanomanipulation tool to elongate, deform and cut DNA molecules. The measurement of the conductivity of DNA molecules in their different shapes and structures is a promising method to find conclusive evidences, which verify the electrical conductivity of DNA molecules.

Integrating with the respective advantages of XML Schema and Ontology, this paper puts forward a semantic information processing architecture-OBSA to solve the problem of heterogeneity of information sources and uncertainty of semantic. It introduces an F-Logic based semantic information presentation mechanism, presents a design of an ontology-based semantic representation language and a mapping algorithm converting Ontology to XML DTD/Schema, and an adapter architecture for accessing distributed and heterogeneous information.

Surface manufacturing is widely used in industry. Automatic CAD-guided tool planning has many applications in surface manufacturing, such as spray painting, spray forming, rapid tooling, cleaning and polishing. According to the material quantity requirements, these tasks can be categorized into two groups: the material uniformity problem and coverage problem. A general framework is developed to automatically generate trajectories of a free-form surface for these tasks. A given task is first transformed into one of the groups. Based on the CAD model of a free-form surface, constraints and tool model, a trajectory for a free-form surface is generated. Velocity optimization is discussed to optimize the material quantity. Simulations are performed to verify the developed framework. This framework can also be extended to other applications.

Automatic trajectory generation for spray painting is highly desirable for today's automotive manufacturing. Generating a trajectory for a surface with only one patch is widely studied to satisfy the paint thickness constraints. However, a complex surface has to be divided into several patches to satisfy the paint thickness and paint gun orientation constraints. Trajectory generation for a surface with multiple patches has not been addressed yet. In this paper, optimization processes are developed to optimize the paint thickness on a surface which consists of several patches. Optimization results are presented. Simulations are performed to verify the optimized parameters. Verification results is consistent with the optimization results.

Automatic CAD-guided optimal tool planning has many application in surface manufacturing, such as spray painting, spray forming, rapid tooling, cleaning and polishing. A general framework for CAD-guided optimal tool planning is developed to automatically generate optimal trajectories for these tasks. A material distribution model is developed. A multi-objective constraint optimization problem is formulated. An optimal tool trajectory with an optimal time and material quantity deviation is generated. Simulations are performed to verify the generated trajectory. The results show that the developed optimal tool planning algorithm can be applied to generate optimal tool trajectories. This framework can also be extended to other applications.

Additive manufacturing processes such as spray coating, spray painting and rapid tooling important steps in many products' life cycle. Robotic manipulators are widely adopted to carry out these processes. If done by human operators, the tool planning for these applications is usually time-consuming and the generated tool plans are prone to inaccuracy and errors. This research develops a fully-automated, CAD-guided tool planning system which eliminates the human involvement. Meanwhile, this system can generate optimized tool plans in the sense of robot motion performance. The critical part of this tool planning system is the partitioning of part surfaces into multiple easy-to-handle patches. In this paper, a decomposition-based approach is developed, which models the surface partitioning problem in geometric domain as an integer programming problem in algebraic domain. Experimental tests and evaluation carried out on automotive parts validate this new approach.

Automatic trajectory generation for spray painting is highly desirable for today's automotive manufacturing. Generating paint gun trajectories for free-form surfaces to satisfy paint thickness requirements is still highly challenging due to the complex geometry of free-from surfaces. In this paper, a CAD-guided paint gun trajectory generation system for free-form surfaces has been developed. A paint thickness verification method is also provided to verify the generated trajectories. The results of experiments and simulations show that the trajectory generation system achieves satisfactory performance. This trajectory generation system can also be applied to generate trajectories for many other CAD-guided robot trajectory planning applications.