Dendritic shapes are commonplace in the natural world such as trees, lichens, coral and lightning. Models of dendritic shapes are widely needed in many areas. Because of their branching fractal and erratic structures modeling dendritic shapes is a tricky task. Existing methods for modeling dendritic shapes are slow and complicated.<p>In this thesis we present a procedural algorithm of using path planning to model dendritic shapes. We generate a dendrite by finding the least-cost paths from multiple endpoints to a common generator and use the dendrite to build the geometric model. With the control handles of endpoint placement, fractal shape, edge weights distribution and path width, we create different shapes of dendrites that simulate different kinds of dendritic shapes very well. Compared with some existing methods, our algorithm is fast and simple.

Dendritic shapes are commonplace in the natural world such as trees, lichens, coral and lightning. Models of dendritic shapes are widely needed in many areas. Because of their branching fractal and erratic structures modeling dendritic shapes is a tricky task. Existing methods for modeling dendritic shapes are slow and complicated.<p>In this thesis we present a procedural algorithm of using path planning to model dendritic shapes. We generate a dendrite by finding the least-cost paths from multiple endpoints to a common generator and use the dendrite to build the geometric model. With the control handles of endpoint placement, fractal shape, edge weights distribution and path width, we create different shapes of dendrites that simulate different kinds of dendritic shapes very well. Compared with some existing methods, our algorithm is fast and simple.

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Crane lift path planning is time-consuming, prone to errors, and requires the practitioners to have exceptional visualization abilities, in particular, as the construction site is congested and dynamically changing. This research presents a methodology based on robotics motion planning to numerically solve the crane path planning problem. The proposed methodology integrates a database in order to automatically conduct 2D path planning for a crane lift operation, and accounts for the rotation of the lifted object during its movements. The proposed methodology has been implemented into a computer module, which provides a user-friendly interface to aid the practitioners to perform a collision-free path planning, and check the feasibility of the path at different stages of the project. Three examples are described in order to demonstrate the effectiveness of the proposed methodology and illustrate the essential features of the developed module. / Construction Engineering and Management

This thesis presents an indoor obstacle avoidance system for car-like mobile robot. The system consists of stereo vision, map building, and path planning. Stereo vision is performed on stereo images to create a geometric map of the environment. A fast sparse stereo approach is employed. For different areas of the image there are different optimal values of disparity range. A multi-pass method to combine results at different disparity range is proposed. To reduce computational complexity the matching is limited to areas that are likely to generate useful data. The stereo vision system outputs a more complete disparity map. Abstract Map building involves converting the disparity map into map coordinates using triangulation and generating a list of obstacles. Occupancy grids are built to aid a hierarchical collision detection. The fast collision detection method is used by the path planner. Abstract A steering set path planner calculates a path that can be directly used by a car-like mobile robot. An adaptive approach using occupancy grid information is proposed to improve efficiency. Using a non-fixed steering set the path planner spends less computation time in areas away from obstacles. The path planner populates a discrete tree to generate a smooth path. Two tree population methods were trialled to execute the path planner. The methods are implemented and experimented on a real car-like mobile robot.

abstract: With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality. / Dissertation/Thesis / M.S. Electrical Engineering 2013

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Long range path planning for an autonomous ground vehicle with minimal a-priori data is still very much an open problem. Previous research has demonstrated that least cost paths generated from aerial LIDAR and GIS data could play a role in automatically determining suitable routes over otherwise unknown terrain. However, most of this research has been theoretical. Consequently, there is very little literature the effectiveness of these techniques in plotting paths of an actual autonomous vehicle. This research aims to develop an algorithm for using aerial LIDAR and imagery to plan paths for a full size autonomous car. Methods of identifying obstacles and potential roadways from the aerial LIDAR and imagery are reviewed. A scheme for integrating the path planning algorithms into the autonomous vehicle existing systems was developed and eight paths were generated and driven by an autonomous vehicle. The paths were then analyzed for their drivability and the model itself was validated against the vehicle measurements. The methods described were found to be suitable for generating paths both on and off road. / Master of Science

As a result of recent technological advances in modernized sensor sets and sensor platforms, sensor management combined with sensor platform path planning are studied to conduct intelligence, surveillance and reconnaissance (ISR) operations in novel ways. This thesis addresses the path planning and sensor management for aerial vehicles to cover areas of interest (AOIs), scan objects of interest (OOIs) and/or track multiple detected targets in surveillance missions. The problems in this thesis, which include 1) the spatio-temporal coordination of sensor platforms to observe AOIs or OOIs, 2) the optimal sensor geometry and path planning for localization and tracking of targets in a mobile three-dimensional (3D) space, and 3) the scheduling of sensors working in different (i.e., active and passive) modes combined with path planning to track targets in the presence of jammers, emerge from real-world demands and scenarios. The platform path planning combined with sensor management is formulated as optimization problems with problem-dependent performance evaluation metrics and constraints. Firstly, to cover disjoint AOIs over an extended time horizon using multiple aerial vehicles for persistent surveillance, a joint multi-period coverage path planning and temporal scheduling, which allows revisiting in a single-period path, is formulated as a combinatorial optimization with novel objective functions. Secondly, to use a group of unmanned aerial vehicles (UAVs) cooperatively carrying out search-and-track (SAT) in a mobile 3D space with a number of targets, a joint path planning and scanning (JPPS) is formulated based on the predictive information gathered from the search space. The optimal 3D sensor geometry for target localization is also analyzed with the objective to minimize the estimation uncertainty under constraints on sensor altitude, sensor-to-sensor and sensor-to-target distances for active or passive sensors. At last, to accurately track targets in the presence of jammers broadcasting wide-band noise by taking advantage of the platform path planning and the jammer's information captured by passive sensors, a joint path planning and active-passive scheduling (JPPAPS) strategy is developed based on the predicted tracking performance at the future time steps in a 3D contested environment. The constraints on platform kinematic, flyable area and sensing capacity are included in these optimization problems. For these multisensor path planning and decision making, solution techniques based on the genetic algorithm are developed with specific chromosome representations and custom genetic operators using either the non-dominated sorting multiobjective optimization (MOO) architecture or the weighted-sum MOO architecture. Simulation results illustrate the performance and advantage of the proposed strategies and methods in real-world surveillance scenarios. / Thesis / Doctor of Philosophy (PhD)