In order to increase the carrying capacity of subway train, it is necessary to adjust the running of the train for the realization of high operation frequency and safety. After an analysis of the subway train's running under moving block system, a dynamic mathematical model of subway train is constructed. The control among multiple trains is discussed, the method of decreasing the headway and increasing the carrying capacity is discussed by using the event-based technology and formation approach to ensure the safety and to avoid the stop out of station, this approach can reconstruct the system easily and harmonize the cooperation between subsystems. Take account of three trains, the control strategy of the following train is given, the velocity, acceleration of the train can be adjusted according to the safety distance between the successive trains and the distance which the train had traveled

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.

This paper addresses the robot path planning problem for automotive part inspection using structured light method. This problem is rendered as a traveling salesman problem (TSP). A new approach is developed to solve the TSP into its sub-optimality quickly. Instead of solving a large size TSP, this approach utilizes the clustered nature of the viewpoints and converts the TSP into a clustered traveling salesman problem (CTSP). A new algorithm, which favors the inter-group paths, is proposed to solve the CTSP. Experimental results on various automotive parts validate the new algorithm.

A CAD-guided camera planning system for dimensional inspection of automotive parts has been developed. This system utilizes the CAD information of inspected parts and the camera model to plan camera viewpoints that satisfy certain task constraints. To overcome some limitation inherited in the existing method, a new CAD data representation is adopted to combine the advantages of parametric surface and triangulation. A graph-based surface merging algorithm is proposed to reduce the number of flat patches, and eventually, the number of viewpoints. Tests on different real part models show that the surface merging algorithm is effective. This algorithm can also benefit many other CAD-guided robot path planning applications.

Sensor planning is critical in many inspection systems. In typical part dimensional inspection systems, eye-in-hand robots are usually adopted. One practical problem that needs to be addressed is that the viewpoints, or correspondingly, the robot hand-tip positions and orientations, should be within the robot's reachability space. Furthermore, it is desirable that the robot can reach these viewpoints "comfortably". In this sense the robot kinematics constraint should be considered in sensor planning. In the paper, an approach is developed to integrate the kinematics constraint into sensor planning. This approach models the problem as an integer programming problem. With this approach, the computation burden of the robot placement problem can be avoided. Experiment and simulation results demonstrate the effectiveness of our approach.

Structured light is one of the well-known methods in part dimensional inspection that have been successfully employed in various applications in the past decades. In this method, the positioning of the camera is very critical, which affects the accuracy and efficiency of the whole inspection system. Here we develop a CAD-guided camera positioning system to aid the 3-D part inspection. The geometric information in the CAD model of the inspected part and the camera model are used to plan camera configurations that satisfy certain task constraints. The overall system we propose can be applied to the 3-D inspection of parts with free-form surfaces, such as automobile door panels. Experiments on different parts show satisfying results.

In this paper, a CAD-guided robot path generator is developed for the spray painting of compound surfaces commonly seen in automotive manufacturing. Instead of widely used parametric representation of surfaces, a planar facet scheme is used to approximate the painting surfaces. As a result, a new combinatorial gun path planning algorithm is proposed. In this algorithm, big patches are formed by stitching small surfaces together. Therefore, the path planning is solved based on the global characteristics of tire part, and the resulting spray gun paths are well-behaved in the sense of time, coverage, and wastage. The proposed algorithm has been implemented and tested using ROBCAD/sup TM//Paint.

This paper presents a novel approach for solving a challenging problem in the intelligent control of robotic manufacturing systems, i.e., the integration of low-level system sensing and control with high-level system behavior and perception. First, a newly developed event-based planning and control method will be introduced. It will then be extended to a robotic manufacturing system via a hybrid system approach. The tasks of a robotic manufacturing system usually consist of multiple segments of robotic actions, which involve both continuous and discrete types of actions. The max-plus algebra model has been proposed to model such a system. Combined with the event-based planning and control methods, both discrete and continuous actions can be planned and controlled based on the max-plus algebra model. More important, the interactions between discrete and continuous actions can be formulated analytically. A typical parts-sorting task in a robotic manufacturing system is used to illustrate the proposed approach. The experimental results clearly demonstrate the advantages of this method.

This paper presents a novel approach for solving the challenging problem in intelligent control of manufacturing systems. The proposed max-plus algebra model combined with event-based planning and control provides a mechanism to efficiently integrate task scheduling, sensing, planning and real-time execution so that the task scheduling which usually deals with discrete types of events, as well as action planning which usually deals with continuous events, can be treated systematically in a unified analytical model. The unique feature of this approach is that interactions between discrete and continuous events can be considered in a unified framework. This feature allows the manufacturing system to intelligently cope with unexpected events and uncertainties so that the efficiency and reliability of the task schedule and action plan can increase significantly. A robotic manufacturing system is used to illustrate the proposed approach. The experimental results demonstrate the advantages of the proposed approach.

The analytical and robust integration of low-level system sensing and simple control with high-level system behavior and perception is critical technology in the intelligent control of modern manufacturing systems. The paper presents a novel approach by using a max-plus algebra model and a newly designed event-based planning and control scheme to provide an advanced mechanism to efficiently integrate manufacturing systems, so that task scheduling, which usually deals with discrete events, as well as action planning/control, which usually deals with continuous dynamics, can be treated in a unified analytical model. More importantly, the interactions between discrete event systems and continuous dynamics are achieved so that manufacturing systems are allowed to intelligently cope with unexpected system faults and uncertainties during well-scheduled tasks. A robotic manufacturing system is used to illustrate the proposed approach. The experimental results clearly demonstrate the advantages of the proposed approach.