GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System

Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time

Decision support using the Multistatic Tactical Planning Aid (MSTPA)

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Self-adaptive fitness in evolutionary processes

Most optimization algorithms or methods in artificial intelligence can be regarded as evolutionary processes. They start from (basically) random guesses and produce increasingly better results with respect to a given target function, which is defined by the process's designer. The value of the achieved results is communicated to the evolutionary process via a fitness function that is usually somewhat correlated with the target function but does not need to be exactly the same. When the values of the fitness function change purely for reasons intrinsic to the evolutionary process, i.e., even though the externally motivated goals (as represented by the target function) remain constant, we call that phenomenon self-adaptive fitness. We trace the phenomenon of self-adaptive fitness back to emergent goals in artificial chemistry systems, for which we develop a new variant based on neural networks. We perform an in-depth analysis of diversity-aware evolutionary algorithms as a prime example of how to effectively integrate self-adaptive fitness into evolutionary processes. We sketch the concept of productive fitness as a new tool to reason about the intrinsic goals of evolution. We introduce the pattern of scenario co-evolution, which we apply to a reinforcement learning agent competing against an evolutionary algorithm to improve performance and generate hard test cases and which we also consider as a more general pattern for software engineering based on a solid formal framework. Multiple connections to related topics in natural computing, quantum computing and artificial intelligence are discovered and may shape future research in the combined fields.Die meisten Optimierungsalgorithmen und die meisten Verfahren in Bereich künstlicher Intelligenz können als evolutionäre Prozesse aufgefasst werden. Diese beginnen mit (prinzipiell) zufällig geratenen Lösungskandidaten und erzeugen dann immer weiter verbesserte Ergebnisse für gegebene Zielfunktion, die der Designer des gesamten Prozesses definiert hat. Der Wert der erreichten Ergebnisse wird dem evolutionären Prozess durch eine Fitnessfunktion mitgeteilt, die normalerweise in gewissem Rahmen mit der Zielfunktion korreliert ist, aber auch nicht notwendigerweise mit dieser identisch sein muss. Wenn die Werte der Fitnessfunktion sich allein aus für den evolutionären Prozess intrinsischen Gründen ändern, d.h. auch dann, wenn die extern motivierten Ziele (repräsentiert durch die Zielfunktion) konstant bleiben, nennen wir dieses Phänomen selbst-adaptive Fitness. Wir verfolgen das Phänomen der selbst-adaptiven Fitness zurück bis zu künstlichen Chemiesystemen (artificial chemistry systems), für die wir eine neue Variante auf Basis neuronaler Netze entwickeln. Wir führen eine tiefgreifende Analyse diversitätsbewusster evolutionärer Algorithmen durch, welche wir als Paradebeispiel für die effektive Integration von selbst-adaptiver Fitness in evolutionäre Prozesse betrachten. Wir skizzieren das Konzept der produktiven Fitness als ein neues Werkzeug zur Untersuchung von intrinsischen Zielen der Evolution. Wir führen das Muster der Szenarien-Ko-Evolution (scenario co-evolution) ein und wenden es auf einen Agenten an, der mittels verstärkendem Lernen (reinforcement learning) mit einem evolutionären Algorithmus darum wetteifert, seine Leistung zu erhöhen bzw. härtere Testszenarien zu finden. Wir erkennen dieses Muster auch in einem generelleren Kontext als formale Methode in der Softwareentwicklung. Wir entdecken mehrere Verbindungen der besprochenen Phänomene zu Forschungsgebieten wie natural computing, quantum computing oder künstlicher Intelligenz, welche die zukünftige Forschung in den kombinierten Forschungsgebieten prägen könnten

PSO-Based Optimal Coverage Path Planning for Surface Defect Inspection of 3C Components with a Robotic Line Scanner

The automatic inspection of surface defects is an important task for quality control in the computers, communications, and consumer electronics (3C) industry. Conventional devices for defect inspection (viz. line-scan sensors) have a limited field of view, thus, a robot-aided defect inspection system needs to scan the object from multiple viewpoints. Optimally selecting the robot's viewpoints and planning a path is regarded as coverage path planning (CPP), a problem that enables inspecting the object's complete surface while reducing the scanning time and avoiding misdetection of defects. However, the development of CPP strategies for robotic line scanners has not been sufficiently studied by researchers. To fill this gap in the literature, in this paper, we present a new approach for robotic line scanners to detect surface defects of 3C free-form objects automatically. Our proposed solution consists of generating a local path by a new hybrid region segmentation method and an adaptive planning algorithm to ensure the coverage of the complete object surface. An optimization method for the global path sequence is developed to maximize the scanning efficiency. To verify our proposed methodology, we conduct detailed simulation-based and experimental studies on various free-form workpieces, and compare its performance with a state-of-the-art solution. The reported results demonstrate the feasibility and effectiveness of our approach

Wireless indoor network planning for advanced exposure and installation cost minimization

The possibility of having information access anytime and anywhere has caused a huge increase of the popularity of wireless networks. Requirements of users and owners have been ever-increasing. However, concerns about the potential health impact of exposure to radio frequency (RF) sources have arisen and are getting accounted for in wireless network planning. In addition to adequate coverage and reduced human exposure, the installation cost of the wireless network is also an important criterion in the planning process. In this paper, a hybrid algorithm is used to optimize indoor wireless network planning while satisfying three demands: maximum coverage, minimal full installation cost (cabling, cable gutters, drilling holes, labor, etc.), and minimal human exposure. For the first time, wireless indoor networks are being optimized based on these advanced and realistic conditions. The algorithm is investigated for three scenarios and for different configurations. The impact of different exposure requirements and cost scenarios is assessed

Leksell Gamma Knife Treatment Planning via Kernel Regression Data Mining Initialization and Genetic Algorithm Optimization

Gamma Knife is a medical procedure that is used to treat several types of intracranial disease. The system utilizes gamma rays from Cobalt-60 radiation sources focused at an isocenter and a stereotactic frame system that serves as an immobilization device coordinate system. Treatment is performed by localizing the patient’s disease with a medical imaging study and positioning the diseased area at the focused intersection of the beams. Patient treatment may require multiple treatment positions and varying beam sizes. The treatment position, time, and beam size is determined through a treatment planning process. Traditionally Gamma Knife treatment planning is performed manually by an expert planner. This process can be time consuming and arrival at an optimal plan may depend on the skill of the planner. This work automates the treatment planning process with a multi-module optimization system. First, a kernel regression data mining module compares the treatment volume to a database of past treatment plans to create a set of initial plans. These plans seed a genetic algorithm optimizer that produces an optimized plan. The cost function for the optimization is a weighted average of several traditional metric for assessing stereotactic radiosurgery plan quality. A gradient descent optimizer is utilized to further refine the optimized treatment plan. The developed system was applied to three Gamma Knife planning cases; a solitary metastasis, an acoustic schwannoma, and a pituitary adenoma. The system produced an average percent isodose coverage for the three plans of 94.5% and the average Paddick Conformity index was 0.76 in an average time of 17.16 minutes for the three plans. The system was compared to an expert planner and an optimizer included with the Gamma Knife planning software. The developed system and expert planner performance was essentially equivalent (average percent isodose coverage 95.8%, average Paddick Conformity index 0.70, optimization time 20.52). The developed system performed much better than the Gamma Plan optimizer (average percent isodose coverage 85.8%, average Paddick Conformity index 0.71) however the Gamma Plan optimizer result was obtained quicker (optimization time about 1 minute). The developed system can be utilized for efficient high-quality Gamma Knife treatment planning

Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles

This thesis addresses coordinated path planning for fixed-wing Unmanned Aerial Vehicles (UAVs) engaged in persistent surveillance missions. While uniquely suited to this mission, fixed wing vehicles have maneuver constraints that can limit their performance in this role. Current technology vehicles are capable of long duration flight with a minimal acoustic footprint while carrying an array of cameras and sensors. Both military tactical and civilian safety applications can benefit from this technology. We make three main contributions: C1 A sequential path planner that generates a C2 flight plan to persistently acquire a covering set of data over a user designated area of interest. The planner features the following innovations: • A path length abstraction that embeds kino-dynamic motion constraints to estimate feasible path length • A Traveling Salesman-type planner to generate a covering set route based on the path length abstraction • A smooth path generator that provides C2 routes that satisfy user specified curvature constraints C2 A set of algorithms to coordinate multiple UAVs, including mission commencement from arbitrary locations to the start of a coordinated mission and de-confliction of paths to avoid collisions with other vehicles and fixed obstacles iv C3 A numerically robust toolbox of spline-based algorithms tailored for vehicle routing validated through flight test experiments on multiple platforms. A variety of tests and platforms are discussed. The algorithms presented are based on a technical approach with approximately equal emphasis on analysis, computation, dynamic simulation, and flight test experimentation. Our planner (C1) directly takes into account vehicle maneuverability and agility constraints that could otherwise render simple solutions infeasible. This is especially important when surveillance objectives elevate the importance of optimized paths. Researchers have devel oped a diverse range of solutions for persistent surveillance applications but few directly address dynamic maneuver constraints. The key feature of C1 is a two stage sequential solution that discretizes the problem so that graph search techniques can be combined with parametric polynomial curve generation. A method to abstract the kino-dynamics of the aerial platforms is then presented so that a graph search solution can be adapted for this application. An A* Traveling Salesman Problem (TSP) algorithm is developed to search the discretized space using the abstract distance metric to acquire more data or avoid obstacles. Results of the graph search are then transcribed into smooth paths based on vehicle maneuver constraints. A complete solution for a single vehicle periodic tour of the area is developed using the results of the graph search algorithm. To execute the mission, we present a simultaneous arrival algorithm (C2) to coordinate execution by multiple vehicles to satisfy data refresh requirements and to ensure there are no collisions at any of the path intersections. We present a toolbox of spline-based algorithms (C3) to streamline the development of C2 continuous paths with numerical stability. These tools are applied to an aerial persistent surveillance application to illustrate their utility. Comparisons with other parametric poly nomial approaches are highlighted to underscore the benefits of the B-spline framework. Performance limits with respect to feasibility constraints are documented

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

The present book contains ten articles illustrating the different possible uses of UAVs and satellite remotely sensed data integration in Geographical Information Systems to model and predict changes in both the natural and the human environment. It illustrates the powerful instruments given by modern geo-statistical methods, modeling, and visualization techniques. These methods are applied to Arctic, tropical and mid-latitude environments, agriculture, forest, wetlands, and aquatic environments, as well as further engineering-related problems. The present Special Issue gives a balanced view of the present state of the field of geoinformatics