The path search problem considers a simple model of communication networks as channel graphs: directed acyclic graphs with a single source and sink. We consider each vertex to represent a switching point, and each edge a single communication line. Under a probabilistic model where each edge may independently be free (available for use) or blocked (already in use) with some constant probability, we seek to efficiently search the graph: examine (on average) as few edges as possible before determining if a path of free edges exists from source to sink. We consider the difficulty of searching various graphs under different search models, and examine the computational complexity of calculating the search cost of arbitrary graphs.

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In this thesis we study a new type of pursuit-evasion game, which we call the herding problem. Unlike typical pursuit evasion games where the pursuer aims to catch or intercept the evader, the goal of the pursuer in this game is to drive the evader to a certain location or region in the x-y plane. This herding model is proposed and represented using dynamic equations. The model is implemented in an effort to understand how two pursuers work cooperatively to drive multiple evaders to the desired destination following weighted time-optimal and effort-optimal control paths. Simulation of this herding problem is accomplished through dynamic programming by utilizing the SNOPT software in the MATLAB environment. The numerical solution gives us the optimal path for all agents and the corresponding controls as well as the relative distance and angle variables. The results show that the pursuers can work cooperatively to drive multiple evaders to the goal.

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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.

CTF3 is a test facility for a new CLIC high energy linear collider. For this beamsteering and beam focusing is vital. Because physically running a beamline and changingsetup is expensive and takes much effort it is beneficial to use a simulator for thebeamline. The transportation of the beam through the beamline can be representedwith matrix multiplications and for this reason MATLAB is a fitting environment tosimulate in. A Flight Path Simulator was written in MATLAB and was succefullyimplemented and tested for the CALIFES beamline of the two-beam test stand that ispart of the CTF3 facility. / <p>återuppladdning</p>

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.