Jung Leng Foo

THREE-DIMENSIONAL PATH PLANNING OF UNMANNED AERIAL VEHICLES USING PARTICLE SWARM OPTIMIZATION

Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircrafts or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to fly to. To date, path-planning algorithms have been limited to two-dimensional problem formulations. This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO). The problem formulation was designed to minimize risk due to enemy threats while simultaneously minimizing fuel consumption. The initial design point is a straight path between the current position and the desired target. Using PSO, an optimized path is generated through B-spline curves. The resulting paths can be optimized with a preference towards maximum safety, minimum fuel consumption or a combination of the two. The problem formulation and solution implementation is described along with the results from several simulated scenarios.

Evaluating Mental Workload of Two-Dimensional and Three-Dimensional Visualization for Anatomical Structure Localization

Visualization of medical data in three-dimensional (3D) or two-dimensional (2D) views is a complex area of research. In many fields 3D views are used to understand the shape of an object, and 2D views are used to understand spatial relationships. It is unclear how 2D/3D views play a role in the medical field. Using 3D views can potentially decrease the learning curve experienced with traditional 2D views by providing a whole representation of the patients anatomy. However, there are challenges with 3D views compared with 2D. This current study expands on a previous study to evaluate the mental workload associated with both 2D and 3D views. Twenty-five first-year medical students were asked to localize three anatomical structures--gallbladder, celiac trunk, and superior mesenteric artery--in either 2D or 3D environments. Accuracy and time were taken as the objective measures for mental workload. The NASA Task Load Index (NASA-TLX) was used as a subjective measure for mental workload. Results showed that participants viewing in 3D had higher localization accuracy and a lower subjective measure of mental workload, specifically, the mental demand component of the NASA-TLX. Results from this study may prove useful for designing curricula in anatomy education and improving training procedures for surgeons.

A Framework for Interactive Visualization of Digital Medical Images

The visualization of medical images obtained from scanning techniques such as computed tomography and magnetic resonance imaging is a well-researched field. However, advanced tools and methods to manipulate these data for surgical planning and other tasks have not seen widespread use among medical professionals. Radiologists have begun using more advanced visualization packages on desktop computer systems, but most physicians continue to work with basic two-dimensional grayscale images or not work directly with the data at all. In addition, new display technologies that are in use in other fields have yet to be fully applied in medicine. It is our estimation that usability is the key aspect in keeping this new technology from being more widely used by the medical community at large. Therefore, we have a software and hardware framework that not only make use of advanced visualization techniques, but also feature powerful, yet simple-to-use, interfaces. A virtual reality system was created to display volume-rendered medical models in three dimensions. It was designed to run in many configurations, from a large cluster of machines powering a multiwalled display down to a single desktop computer. An augmented reality system was also created for, literally, hands-on interaction when viewing models of medical data. Last, a desktop application was designed to provide a simple visualization tool, which can be run on nearly any computer at a users disposal. This research is directed toward improving the capabilities of medical professionals in the tasks of preoperative planning, surgical training, diagnostic assistance, and patient education.

A parallel implementation of particle swarm optimization using digital pheromones

*† ‡ A parallel implementation of Particle Swarm Optimization (PSO) using digital pheromones to coordinate the movements of the swarm within an n-dimensional design space is presented in this paper. Digital pheromones are models simulating real pheromones emitted by insects for communication to indicate a source of food or a nesting location. This principle of communication and organization between each insect in a swarm offers substantial improvement when integrated into a Particle Swarm Optimization algorithm. Digital swarms are used to search a design space with digital pheromones aiding communication within the swarm to improve search efficiency. With statistical analysis, the pheromone strength in a region of the design space is determined. The swarm then reacts accordingly based on the probability that this region may contain an optimum. When implemented in a parallel computing architecture, significant performance increases were observed. This paper presents the method development and results from several test cases.

THREE-DIMENSIONAL MULTI-OBJECTIVE UAV PATH PLANNER USING META-PATHS FOR DECISION MAKING AND VISUALIZATION

Military operati ons are turning to more complex and advanced automation technolog y for mini mum risk and maximum efficiency. A critical piece to this strategy is unmanned aeri al vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoi ding obstacles such as other aircrafts or enemy threats. Often automated path planning algori thms are employed to s pecify targets for a UAV to fl y to. To date, path-pl anning alg orithms have been limi ted to provi ding only a single solution (alternate) path without further inputs from the UAV controller. This paper presents a uni que platform for decision making in a three-di mensional path pl anner where mul ti ple solution paths are generated in the form of meta-paths. The path pl anner uses Particle Swarm Optimizati on (PSO) to generate multi ple solution paths based on predefined criteria. The problem formulati on was designed to mini mize risk due to enemy threats, to mini mize fuel consumption incurred by deviating from the original path, and takes into account reconnaissance targets. Using PSO, al ternate paths are generated using B -spline curves, optimized based on preferences set for the three objecti ves. The resulting paths can be optimized wi th a preference towards maxi mum safety, mini mum fuel co nsumpti on, or target reconnaissance. For each preference, the top fi ve solutions generated by PSO are presented, for a total of 15 alternate paths. In order to efficientl y present these multi ple solution paths, meta-paths are implemented, which is a single summarized representation of all the paths generated for a particular preference. This allows the decision making process to be completed in an efficient and organized manner. The problem formulati on and soluti on implementation is described along wi th the results from several simulated scenarios.

Colorization of CT images to improve tissue contrast for tumor segmentation

Segmenting tumors from grayscale medical image data can be difficult due to the close intensity values between tumor and healthy tissue. This paper presents a study that demonstrates how colorizing CT images prior to segmentation can address this problem. Colorizing the data a priori accentuates the tissue density differences between tumor and healthy tissue, thereby allowing for easier identification of the tumor tissue(s). The method presented allows pixels representing tumor and healthy tissues to be colorized distinctly in an accurate and efficient manner. The associated segmentation process is then tailored to utilize this color data. It is shown that colorization significantly decreases segmentation time and allows the method to be performed on commodity hardware. To show the effectiveness of the method, a basic segmentation method, thresholding, was implemented with and without colorization. To evaluate the method, False Positives (FP) and False Negatives (FN) were calculated from 10 datasets (476 slices) with tumors of varying size and tissue composition. The colorization method demonstrated statistically significant differences for lower FP in nine out of 10 cases and lower FN in five out of 10 datasets.

Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system

A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROIs spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. With a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Test cases success in segmenting the tumor from seven of the 10 CT datasets with <10% false positive errors and five test cases with <10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.

Three Dimensional Multi-Objective UAV Path Planning Using Digital Pheromone Particle Swarm Optimization

Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircraft or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to investigate. To date, path-planning algorithms have been limited to providing only a single solution (alternate path) without further input from a pilot. This paper uses digital pheromones to improve upon a previously developed multi-objective path planner that uses Particle Swarm Optimization (PSO) to generate multiple solution paths based on predefined criteria. The problem formulation is designed to minimize risk due to enemy threats and fuel consumption while maximizing reconnaissance and eliminating terrain violations. The implementation of digital pheromone PSO increases the efficiency and reliability of paths returned to the operator. The decrease in iterations allows alternate paths to be returned in real time, aiding in efficient decision making by the UAV operator. The implementation of Digital Pheromone PSO is described below along with the results of simulated scenarios.

Multi-Objective UAV Path Planning with Refined Reconnaissance and Threat Formulations

Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircraft or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to investigate. To date, path-planning algorithms have been limited to providing only a single solution (alternate path) without further input from a pilot. This paper improves upon a previously developed multi-objective path planner that uses Particle Swarm Optimization (PSO) to generate multiple solution paths based on predefined criteria. The original path planner consisted of 4 components: fuel efficiency, reconnaissance, threat avoidance, and terrain. In this paper, the focus will be on improvements made to the formulations of the cost functions for reconnaissance and threat avoidance as well as the ability of the pilot to adjust the weights for the mission objectives. The alternate paths can be optimized with a preference towards maximum safety, minimum fuel consumption, or target reconnaissance. The weight adjustment techniques presented enhance the pilot’s ability to find desirable solutions when re-tasking the UAV. Most importantly, the paths were generated in real time to allow efficient decision making by the UAV pilot. These improvements were noted in the simulated test scenarios used to evaluate the path planner.

Isis: Patient Data Visualization and Surgical Planning in an Interactive Virtual Environment

As medical scanning technology continues to accommodate the need for higher quality medical imaging, there is a continuing need for additional research in efficient ways of extracting crucial information from these vast amounts of data. The visualization software, Isis, has been developed to view and manipulate digital medical images in an immersive environment for surgical planning. Isis is designed to display any DICOM/PACS compatible three-dimensional image data for visualization and interaction in an immersive environment. Pseudo-coloring can be applied in real time, with multiple interactive clipping planes to slice into the volume for an interior view, and the windowing feature controls the tissue density ranges to display. Features such as virtual trocars placement, tumor inspection, and an endoscopic view provides surgeons with essential tools for surgical planning. A wireless gamepad controller and an intuitive menu interface allow the user to interact with the software. By wearing a pair of stereo glasses, the surgeon is immersed within the model, providing a sense of realism as if the surgeon is “inside” the patient.Copyright