面對通訊與資訊科技的大幅進步、網際網路的蓬勃發展、以及電信自由化帶來的激烈競爭，通訊網路正在進行一個巨大的變革，企圖將原有Circuit Switching 與 Packet Switching 網路整合成一個單一整合型網路－All-IP網路以支援所有的應用服務。All-IP網路受限於封包交換網路原有的特性，有服務品質問題(QoS)有待克服，因此有必要在All-IP網路上提供服務品質管理機制以實現整合型網路的目標。而要提供適當的QoS 管理，其成功之關鍵主要在於是否能提供一個簡單易行之架構。本論文先提出BBQ(Budget-Based QoS)採用以預算為基礎之服務品質管理，BBQ提供一個高適用性的管理架構和相關的管理工具，可適用於不同的下層網路架構和不同營運目標的網管政策。 而在本文中提出以路徑規劃(Path Planning)的方式作為此架構的路由方法。路徑規劃係以過去的歷史訊務資料為基礎，為未來可能進入網路中的訊務規劃出一組具有品質要求的路徑，而在系統運作時，只需根據訊務的需求，指定一條符合其品質要求的路徑即可。此方法不但可以給予進入網路的訊務絕對的服務品質保證，而且事前的運算可以免除在系統運作時大量的計算負擔。在本論文中，我們設計了一套路徑規劃的演算法，來驗證我們的方法之效能。而經過反覆的模擬測試，發現本路徑規劃方法的確較傳統OSPF演算法擁有較佳的網路效能，且可以提供進入網路的訊務較佳的服務品質保證。 / The advance in information technology and impact of telecommunications liberalization cause a revolution in commucation world. It attempt to merge circuit-switching and packet-switching network into one standlone perform. All-IP is one of those candidates. But All-IP also inherits the characteristics of packet-switching network, the problem of transmission quality. So, we need some management system of quality on All-IP network to achieve the ambition of converged network. In this thesis, we propose BBQ management system. BBQ offers a highly adaptive management architecture and some management tools for operators. Those tools can be used in different underly network layer and can be tuned by different network policy. We propose the path planning as routing approach in BBQ architecture. Path-planning is doing path calculation for the traffic may happen in future using the historic traffic statistic. In run-time, admission control component will assign a planned path that satisfy the requirements of admitted traffic. In this method, absolute transmission quality is guaranteed to admitted traffic. And pre-planning can reduce heavy calculation overhead in run-time. In this thesis, we design a path-planning algorithm, the GPPA algorithm, which takes pre-defined profit as its maximize objective under bandwidth and quality constraints. Through an performance evaluation using simulation method, we demonstrate that the path-planning might outperform the traditional OSPF algorithm.

This research proposes a VR - based CMM operation system combined with the path planning algorithm to effectively shorten the inspection time. The virtual CMM system is also a good educational tool for industries to train new employees. The realistic interface of virtual CMM environment allows users to practise the CMM operation process before operating the real CMM. The system is also useful to reduce the collision possibility by verifying the collision-free path for products inspection. This system uses Eon Studio as the simulation tool to build the virtual environment of CMM operations. Collision detection is implemented in Eon studio by using built-in functions and VB script programming. A new algorithm combines the integer programming model. A big penalty M method is proposed to avoid unnecessary collisions. The simulation and optimization results prove that the proposed algorithm can effectively reduce the total probe travelling distance with improved inspection efficiency.

This research proposes a VR - based CMM operation system combined with the path planning algorithm to effectively shorten the inspection time. The virtual CMM system is also a good educational tool for industries to train new employees. The realistic interface of virtual CMM environment allows users to practise the CMM operation process before operating the real CMM. The system is also useful to reduce the collision possibility by verifying the collision-free path for products inspection. This system uses Eon Studio as the simulation tool to build the virtual environment of CMM operations. Collision detection is implemented in Eon studio by using built-in functions and VB script programming. A new algorithm combines the integer programming model. A big penalty M method is proposed to avoid unnecessary collisions. The simulation and optimization results prove that the proposed algorithm can effectively reduce the total probe travelling distance with improved inspection efficiency.

Mobile robots are widely used in many industrial fields. Research on path planning for mobile robots is one of the most important aspects in mobile robots research. Path planning for a mobile robot is to find a collision-free route, through the robot’s environment with obstacles, from a specified start location to a desired goal destination while satisfying certain optimization criteria. Most of the existing path planning methods, such as the visibility graph, the cell decomposition, and the potential field are designed with the focus on static environments, in which there are only stationary obstacles. However, in practical systems such as Marine Science Research, Robots in Mining Industry, and RoboCup games, robots usually face dynamic environments, in which both moving and stationary obstacles exist. Because of the complexity of the dynamic environments, research on path planning in the environments with dynamic obstacles is limited. Limited numbers of papers have been published in this area in comparison with hundreds of reports on path planning in stationary environments in the open literature. Recently, a genetic algorithm based approach has been introduced to plan the optimal path for a mobile robot in a dynamic environment with moving obstacles. However, with the increase of the number of the obstacles in the environment, and the changes of the moving speed and direction of the robot and obstacles, the size of the problem to be solved increases sharply. Consequently, the performance of the genetic algorithm based approach deteriorates significantly. This motivates the research of this work. This research develops and implements a simulated annealing algorithm based approach to find the optimal path for a mobile robot in a dynamic environment with moving obstacles. The simulated annealing algorithm is an optimization algorithm similar to the genetic algorithm in principle. However, our investigation and simulations have indicated that the simulated annealing algorithm based approach is simpler and easier to implement. Its performance is also shown to be superior to that of the genetic algorithm based approach in both online and offline processing times as well as in obtaining the optimal solution for path planning of the robot in the dynamic environment. The first step of many path planning methods is to search an initial feasible path for the robot. A commonly used method for searching the initial path is to randomly pick up some vertices of the obstacles in the search space. This is time consuming in both static and dynamic path planning, and has an important impact on the efficiency of the dynamic path planning. This research proposes a heuristic method to search the feasible initial path efficiently. Then, the heuristic method is incorporated into the proposed simulated annealing algorithm based approach for dynamic robot path planning. Simulation experiments have shown that with the incorporation of the heuristic method, the developed simulated annealing algorithm based approach requires much shorter processing time to get the optimal solutions in the dynamic path planning problem. Furthermore, the quality of the solution, as characterized by the length of the planned path, is also improved with the incorporated heuristic method in the simulated annealing based approach for both online and offline path planning.

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There are various kinds of creature living in the world and each kind of creatures has its own unique life habits and behavior patterns. For these reasons between the creature as well as the biology and the environment can have many interactions with each other, and we may observe these interactions easily in the frequently daily life. However the humanity may be the quantity hugest, and also have the most complex behavior at the fine race group in all kind of creatures on earth. So how could we penetrate into by observed and analysis to obtain the information which translates to the computer simulation realistically is a topic of the very hardship with challenge to presents the human behavior. Virtually all previous work has been agent-based, meaning that motion is computed separately for each individual. Such models can capture each person¡¦s unique situation. So the agent-based modeling will inevitably result in a large number of calculation and make poor efficiency. In addition, there are leader-follower system which consists of a leader and multiple followers. This leader will lead the entire group to the destination, and the followers will follow the motion of their leader closely. In our research apply the physical properties of electric charge to the simulation of pedestrians by using the basic concepts of electromagnetism. The simulation method contains the agent-based modeling and the leader-follower system at the same time. The agent-based modeling simulates pedestrian of the individual motion. The goal of the leader-follower system is to simulate the real world behavior that people in a community often move by following a specific object.

This thesis describes the design of a data structure for use with Digitised Terrain Elevation Data (DTED) in Terrain Reference Navigation (TRN) systems. The data structure is based on a variant of quad-tree and oct-tree data structures to provide an efficient representation of terrain in terms of storage requirements and acccss operations. These data structure are applied to flight path planning operations in mission management applications. The algorithms developed for flight path planning have becri implemented in the C programming language for a standard PC. Current research in TRN systems is reviewed and attention is given to the use of hierarchical data structures to cope with the potentially large data base needed for DTED files. Data structure combining quad-trees and oct-trees are developed with an emphasis on data reduction using pointerless trees and the use of locational codes to provide straightforward mapping between quad-trees and oct-trees, in other words, between two-dimensional co-ordinates and three-dimensional co-ordinates. Analysis of these algorithms is described for two DTED files to illustrate storage improvements and to verify a set of database access operations. These data structures are applied to problems of flight path planning where the navigation space comprises objects above a specific altitude and this three-dimensional space is searched for a flight path which avoids the obstacles and satisfies specific operational criteria. Algorithms are developed to extract a visibility graph from the terrain database and to determine the preferred flight path from a set of paths which satisfy defined constraints. Several search techniques are developed which exploit the efficiency of the quad-tree and oct-tree data structures. These methods are extended to real-time flight-path planning where predicted times for access operations are used to direct flight path extraction by varying the tree resolution during computation of the flight path. A comprehensive set of results are provided to illustrate: the storage efficiency of quad-tree and oct-tree data structures the application of pyramid structures to represent navigation space analysis of the time to compute the visibility graph and to extract flight paths integration of these methods with a real-time mission management simulation on a PC The thesis draws conclusions on the efficiency of these techniques for the represcntation of DTEDs and to access objects in TRN systems. It is observed that the use of hierarchical data structures in the form of quad-trees and oct-trees offers significant improvement in accessing DTEDS, for future use in TRN systems. The thesis concludes by outlining areas of further work where the techniques can be further &N, cloped for applications in mission management and navigation using DTED files.

The vertical take-off and landing capabilities of quadrotors, and their maneuverability has contributed towards their recent popularity. They are widely used for indoors applications, where robust control strategies and automation of mission planning is necessary. In this thesis, a mathematical model for a quadrotor is derived using Newton's and Euler's laws. The model is linearized around hover and optimal control theory is used to derive a standard linear quadratic regulator controller for trajectory following. A feedorward of the tracking error is introduced to the standard LQR to improve its transient response. The performance of the proposed controller is compared with a conventional PID controller and the standard LQR controller for a variety of trajectories. The proposed controller produced a faster transient response with better disturbance rejection. A* algorithm is used to generate collisionree paths for the quadrotor where the proposed LQR is used to follow the trajectory.

Planning optimal paths for large numbers of robots is computationally expensive. In this thesis, we present a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically subdimensional expansion initially creates a one-dimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during planning by a robot-robot collision, the dimensionality of the search space is locally increased to ensure that an alternative path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a path. Subdimensional expansion is a exible framework that can be used with multiple planning algorithms. For discrete planning problems, subdimensional expansion can be combined with A* to produce the M* algorithm, a complete and optimal multirobot path planning problem. When the configuration space of individual robots is too large to be explored effectively with A*, subdimensional expansion can be combined with probabilistic planning algorithms to produce sRRT and sPRM. M* is then extended to solve variants of the multirobot path planning algorithm. We present the Constraint Manifold Subsearch (CMS) algorithm to solve problems where robots must dynamically form and dissolve teams with other robots to perform cooperative tasks. Uncertainty M* (UM*) is a variant of M* that handles systems with probabilistic dynamics. Finally, we apply M* to multirobot sequential composition. Results are validated with extensive simulations and experiments on multiple physical robots.

There are millions of robots in operation around the world today, and almost all of them operate on factory floors in isolation from people. However, it is now becoming clear that robots can provide much more value assisting people in daily tasks in human environments. Perhaps the most fundamental capability for a mobile robot is navigating from one location to another. Advances in mapping and motion planning research in the past decades made indoor navigation a commodity for mobile robots. Yet, questions remain on how the robots should move around humans. This thesis advocates the use of semantic maps and spatial rules of engagement to enable non-expert users to effortlessly interact with and control a mobile robot. A core concept explored in this thesis is the Tour Scenario, where the task is to familiarize a mobile robot to a new environment after it is first shipped and unpacked in a home or office setting. During the tour, the robot follows the user and creates a semantic representation of the environment. The user labels objects, landmarks and locations by performing pointing gestures and using the robot's user interface. The spatial semantic information is meaningful to humans, as it allows providing commands to the robot such as ``bring me a cup from the kitchen table". While the robot is navigating towards the goal, it should not treat nearby humans as obstacles and should move in a socially acceptable manner. Three main navigation behaviors are studied in this work. The first behavior is the point-to-point navigation. The navigation planner presented in this thesis borrows ideas from human-human spatial interactions, and takes into account personal spaces as well as reactions of people who are in close proximity to the trajectory of the robot. The second navigation behavior is person following. After the description of a basic following behavior, a user study on person following for telepresence robots is presented. Additionally, situation awareness for person following is demonstrated, where the robot facilitates tasks by predicting the intent of the user and utilizing the semantic map. The third behavior is person guidance. A tour-guide robot is presented with a particular application for visually impaired users.