Dezhen Song

Steady-State Throughput and Scheduling Analysis of Multicluster Tools: A Decomposition Approach

Cluster tools are widely used as semiconductor manufacturing equipment. While throughput analysis and scheduling of single-cluster tools have been well-studied, research work on multicluster tools is still at an early stage. In this paper, we analyze steady-state throughput and scheduling of multicluster tools. We consider the case where all wafers follow the same visit flow within a multicluster tool. We propose a decomposition method that reduces a multicluster tool problem to multiple independent single-cluster tool problems. We then apply the existing and extended results of throughput and scheduling analysis for each single-cluster tool. Computation of lower-bound cycle time (fundamental period) is presented. Optimality conditions and robot schedules that realize such lower-bound values are then provided using ldquopullrdquo and ldquoswaprdquo strategies for single-blade and double-blade robots, respectively. For an -cluster tool, we present lower-bound cycle time computation and robot scheduling algorithms. The impact of buffer/process modules on throughput and robot schedules is also studied. A chemical vapor deposition tool is used as an example of multicluster tools to illustrate the decomposition method and algorithms. The numerical and experimental results demonstrate that the proposed decomposition approach provides a powerful method to analyze the throughput and robot schedules of multicluster tools.

Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation

Skid-steered mobile robots are widely used because of their simple mechanism and high reliability. Understanding the kinematics and dynamics of such a robotic platform is, however, challenging due to the complex wheel/ground interactions and kinematic constraints. In this paper, we develop a kinematic modeling scheme to analyze the skid-steered mobile robot. Based on the analysis of the kinematics of the skid-steered mobile robot, we reveal the underlying geometric and kinematic relationships between the wheel slips and locations of the instantaneous rotation centers. As an application example, we also present how to utilize the modeling and analysis for robot positioning and wheel slip estimation using only low-cost strapdown inertial measurement units. The robot positioning and wheel slip-estimation scheme is based on an extended Kalman filter (EKF) design that incorporates the kinematic constraints for accuracy enhancement. The performance of the EKF-based positioning and wheel slip-estimation scheme are also presented. The estimation methodology is tested and validated experimentally on a robotic test bed.

Trajectory tracking and balance stabilization control of autonomous motorcycles

We report a new trajectory tracking and balancing control algorithm for an autonomous motorcycle. Building on the existing modeling work of a bicycle, the new dynamic model of the autonomous motorcycle considers the bicycle caster angle and captures the steering effect on the vehicle tracking and balancing. The trajectory tracking control takes an external/internal model decomposition approach. A nonlinear controller is designed to handle the vehicle balancing. The motorcycle balancing is guaranteed by the system internal equilibria calculation and by the trajectory and system dynamics requirements. The proposed control system is validated by numerical simulations, and is based on a real prototype motorcycle system

Optimal Scheduling of Multicluster Tools With Constant Robot Moving Times, Part II: Tree-Like Topology Configurations

In this paper, we analyze optimal scheduling of a tree-like multicluster tool with single-blade robots and constant robot moving times. We present a recursive minimal cycle time algorithm to reveal a multi-unit resource cycle for multicluster tools under a given robot schedule. For a serial-cluster tool, we provide a closed-form formulation for the minimal cycle time. The formulation explicitly provides the interaction relationship among clusters. We further present decomposition conditions under which the optimal scheduling of multicluster becomes much easier and straightforward. Optimality conditions for the widely used robot pull schedule are also provided. An example from industry production is used to illustrate the analytical results. The decomposition and optimality conditions for the robot pull schedule are also illustrated by Monte Carlo simulation for the industrial example.

Adaptive Trajectory Tracking Control of Skid-Steered Mobile Robots

Skid-steered mobile robots have been widely used for terrain exploration and navigation. In this paper, we present an adaptive trajectory control design for a skid-steered wheeled mobile robot. Kinematic and dynamic modeling of the robot is first presented. A pseudo-static friction model is used to capture the interaction between the wheels and the ground. An adaptive control algorithm is designed to simultaneously estimate the wheel/ground contact friction information and control the mobile robot to follow a desired trajectory. A Lyapunov-based convergence analysis of the controller and the estimation of the friction model parameter are presented. Simulation and preliminary experimental results based on a four-wheel robot prototype are demonstrated for the effectiveness and efficiency of the proposed modeling and control scheme

IMU-based localization and slip estimation for skid-steered mobile robots

Localization and wheel slip estimation of a skid- steered mobile robot is challenging because of the complex wheel/ground interactions and kinematics constraints. In this paper, we present a localization and slip estimation scheme for a skid-steered mobile robot using low-cost inertial measurement units (IMU). We first analyze the kinematics of the skid-steered mobile robot and present a nonlinear Kalman filter (KF)- based simultaneous localization and slip estimation scheme. The KF-based localization design incorporates the wheel slip estimation and utilizes robot velocity constraints and estimates to overcome the large drift resulting from the integration of the IMU acceleration measurements. The estimation methodology is tested and validated experimentally with a computer vision- based localization system.

Vision-based motion planning for an autonomous motorcycle on ill-structured roads

Abstract We report our development of a vision-based motion planning system for an autonomous motorcycle designed for desert terrain, where uniform road surface and lane markings are not present. The motion planning is based on a vision vector space (V2-Space), which is a unitary vector set that represents local collision-free directions in the image coordinate system. The V2-Space is constructed by extracting the vectors based on the similarity of adjacent pixels, which captures both the color information and the directional information from prior vehicle tire tracks and pedestrian footsteps. We report how the V2-Space is constructed to reduce the impact of varying lighting conditions in outdoor environments. We also show how the V2-Space can be used to incorporate vehicle kinematic, dynamic, and time-delay constraints in motion planning to fit the highly dynamic requirements of the motorcycle. The combined algorithm of the V2-Space construction and the motion planning runs in O(n) time, where n is the number of pixels in the captured image. Experiments show that our algorithm outputs correct robot motion commands more than 90% of the time.

Visual Navigation Using Heterogeneous Landmarks and Unsupervised Geometric Constraints

We present a heterogeneous landmark-based visual navigation approach for a monocular mobile robot. We utilize heterogeneous visual features, such as points, line segments, lines, planes, and vanishing points, and their inner geometric constraints managed by a novel multilayer feature graph (MFG). Our method extends the local bundle adjustment-based visual simultaneous localization and mapping (SLAM) framework by explicitly exploiting the heterogeneous features and their inner geometric relationships in an unsupervised manner. As the result, our heterogeneous landmark-based visual navigation algorithm takes a video stream as input, initializes and iteratively updates MFG based on extracted key frames, and refines robot localization and MFG landmarks through the process. We present pseudocode for the algorithm and analyze its complexity. We have evaluated our method and compared it with state-of-the-art point landmark-based visual SLAM methods using multiple indoor and outdoor datasets. In particular, on the KITTI dataset, our method reduces the translational error by 52.5% under urban sequences where rectilinear structures dominate the scene.

Simultaneous Localization of Multiple Unknown and Transient Radio Sources Using a Mobile Robot

We report system and algorithm developments that utilize a single mobile robot to simultaneously localize multiple unknown transient radio sources. Because of signal source anonymity, short transmission durations, and dynamic transmission patterns, the robot cannot treat the radio sources as continuous radio beacons. To deal with this challenging localization problem, we model the radio source behaviors using a novel spatiotemporal probability occupancy grid that captures transient characteristics of radio transmissions and tracks posterior probability distributions of radio sources. As a Monte Carlo method, a ridge walking motion planning algorithm is proposed to enable the robot to efficiently traverse the high-probability regions to accelerate the convergence of the posterior probability distribution. We also formally show that the time to find a radio source is insensitive to the number of radio sources, and hence, our algorithm has great scalability. We have implemented the algorithms and extensively tested them in comparison with two heuristic methods: a random walk and a fixed-route patrol. The localization time of our algorithms is consistently shorter than that of the two heuristic methods.

Efficient algorithms for shared camera control

We consider a system that allows n networked users to share control over a robotic webcamera. Each user guides the camera pan, tilt and zoom, by drawing a rectangle in the user interface. The server adjusts the camera to best satisfy the user requests, by solving a geometric optimization problem that requires fitting one rectangle to many. We improve upon previous results with an O(n3/2 log3 n) time exact algorithm for this problem. We also present a simple near-linear time e-approximation algorithm. We have implemented the latter and report on experimental results.