A generalized laser simulator algorithm for optimal path planning in constraints environment

Path planning plays a vital role in autonomous mobile robot navigation, and it has thus become one of the most studied areas in robotics. Path planning refers to a robot's search for a collision-free and optimal path from a start point to a predefined goal position in a given environment. This research focuses on developing a novel path planning algorithm, called Generalized Laser Simulator (GLS), to solve the path planning problem of mobile robots in a constrained environment. This approach allows finding the path for a mobile robot while avoiding obstacles, searching for a goal, considering some constraints and finding an optimal path during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating a wave of points in all directions towards the goal point with adhering to constraints. A simulation study employing the proposed approach is applied to the grid map settings to determine a collision-free path from the start to goal positions. First, the grid mapping of the robot's workspace environment is constructed, and then the borders of the workspace environment are detected based on the new proposed function. This function guides the robot to move toward the desired goal. Two concepts have been implemented to find the best candidate point to move next: minimum distance to goal and maximum index distance to the boundary, integrated by negative probability to sort out the most preferred point for the robot trajectory determination. In order to construct an optimal collision-free path, an optimization step was included to find out the minimum distance within the candidate points that have been determined by GLS while adhering to particular constraint's rules and avoiding obstacles. The proposed algorithm will switch its working pattern based on the goal minimum and boundary maximum index distances. For static obstacle avoidance, the boundaries of the obstacle(s) are considered borders of the environment. However, the algorithm detects obstacles as a new border in dynamic obstacles once it occurs in front of the GLS waves. The proposed method has been tested in several test environments with different degrees of complexity. Twenty different arbitrary environments are categorized into four: Simple, complex, narrow, and maze, with five test environments in each. The results demonstrated that the proposed method could generate an optimal collision-free path. Moreover, the proposed algorithm result are compared to some common algorithms such as the A* algorithm, Probabilistic Road Map, RRT, Bi-directional RRT, and Laser Simulator algorithm to demonstrate its effectiveness. The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared methods. The GLS is 7.8 and 5.5 times faster than A* and LS, respectively, generating a path 1.2 and 1.5 times shorter than A* and LS. The mean value of the path cost achieved by the proposed approach is 4% and 15% lower than PRM and RRT, respectively. The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. Finally, to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out using an existing WMR platform in labs and roads. The experimental work investigates complete autonomous WMR path planning in the lab and road environments using live video streaming. The local maps were built using data from live video streaming s by real-time image processing to detect the segments of the lab and road environments. The image processing includes several operations to apply GLS on the prepared local map. The proposed algorithm generates the path within the prepared local map to find the path between start-to-goal positions to avoid obstacles and adhere to constraints. The experimental test shows that the proposed method can generate the shortest path and best smooth trajectory from start to goal points in comparison with the laser simulator

탄성 가중치 개념을 이용한 다수 무인항공기의 편대비행 및 충돌회피

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2013. 2. 김유단.In this thesis, the guidance law for formation flight and collision avoidance of multiple Unmanned Aerial Vehicles(UAVs) is proposed. To construct the physically comprehensible guidance law for formation flight, virtual structure approach is used. To develop guidance law for collision avoidance for both other UAVs and unknown static obstacles, geometric approach using geometric information such as relative vector is utilized. Through Lyapunov theorem, the stability of the proposed guidance law is proved. To combine guidance commands, the concept of elastic weighting factor inspired by the elastic behavior of shape memory polymer, which tends to regain its original shape after deformation, is introduced. By using the concept of elastic weighting factor, multiple UAVs are able to cope actively with situation of collision between both UAVs and static obstacles during the formation flight. To verify the performance of proposed method, numerical simulations are performed for various scenarios during formation flight.1. Introduction 2. Formation Flight 3. Collision Avoidance 4. Combined Guidance Law for Formation Flight and Collision Avoidance 5. Numerical Simulation 6. ConclusionMaste

Autonomous Robot Based on Android Phone

Import 03/11/2016Cílem této práce bylo naprogramovat aplikaci pro systém Android, která se bude snažit autonomně ovládat automobil. K tomuto ovládání aplikace využívá komunikaci přes technologii Bluetooth a dokáže pomocí lidarového senzoru snímat a využívat detekci obrazu přes kameru zařízení. Tyto technologie by měly pomoci k lepší detekci překážek a měly by být využity k lepšímu řízení automobilu. Zaměřil jsem se na ovládání pomocí GPS bodů a aktuální pozice automobilu. Tento problém práce s body, směrem a kompasem jsem vyřešil s pomocí knihoven Android. Pomocí OpenCV detekuji v reálném čase různé druhy překážek. V reálné simulaci se však nepodařilo, aby se automobil pohyboval úplně sám. Problém nastal při ovládání volantu, jelikož volant se nedokáže vrátit na původní pozici, je velmi těžké odhadovat jízdu směru. Také jsem se setkával s velkou prodlevou GPS lokátoru v mobilním zařízením a s velmi pomalou detekcí objektů v reálném čase (někdy až 0.5 snímku za sekundu) z důvodu vysoké náročnosti na výkon mobilního zařízení. Toto testování proběhlo na mobilním zařízení Google Galaxy Nexus. Hlavním zjištěním této práce bylo, že detekce objektů v reálném čase je možná a navigování vozidla také, pokud máme dostatečně výkonný počítač a větší možnosti ovládání vozidla. Výsledky této práce umožňují pokračovat v tomto výzkumu a zlepšovat metody řízení autonomního vozidla, popřípadě využít jinou technologii než je Android pro řízení autonomních vozidel.The aim of my thesis was to make an Android application which will be able to drive a car autonomously. For this purpose, the application uses a Bluetooth technology, a lidar sensor ( which detects obstacles) and also an object detection in a real time. These technologies should be helpful for better detection of obstacles and should be used for better control while driving a car. I focused on controlling a car by a gps sensor and current car's position .I solved the problem with GPS points, direction and compass by Android libraries.I can detect various obstacles in real time by using OpenCV. In a real simulation the car was not able to drive itself autonomously. The problem began when manipulating the steering wheel because it could not have got back to the original position. It's very difficult to predict the car's direction. I have also found out that the GPS locator delays in the mobile device and the detection of objects is very slow in a real time (sometimes about 0,5 frames per second) due to the high memory consumption. It was tested on the mobile Google Galaxy Nexus. The finding of my thesis is that detection of objects in a real time and navigation of a car is possible but there has to be a high-performance PC and better car driving. Nevertheless, my thesis' results enable another researches of this topic and it is possible to improve the methods of driving an autonomous car or to use other technology than Android.460 - Katedra informatikydobř

Navigational control of multiple mobile robots in various environments

The thesis addresses the problem of mobile robots navigation in various cluttered environments and proposes methodologies based on a soft computing approach, concerning to three main techniques: Potential Field technique, Genetic Algorithm technique and Fuzzy Logic technique. The selected techniques along with their hybrid models, based on a mathematical support, solve the three main issues of path planning of robots such as environment representation, localization and navigation. The motivation of the thesis is based on some cutting edge issues for path planning and navigation capabilities, that retrieve the essential for various situations found in day-to-day life. For this purpose, complete algorithms are developed and analysed for standalone techniques and their hybrid models. In the potential field technique the local minima due to existence of dead cycle problem has been addressed and the possible solution for different situations has been carried out. In fuzzy logic technique the different controllers have been designed and their performance analysis has been done during their navigational control in various environments. Firstly, the fuzzy controller having all triangular members with five membership functions have been considered. Subsequently the membership functions are changed from Triangular to other functions, e.g. Trapezoidal, Gaussian functions and combinational form to have a more smooth and optimised control response. It has been found that the fuzzy controller with all Gaussian membership function works better compared to other chosen membership functions. Similarly the proposed Genetic algorithm is based on the suitable population size and fitness functions for finding out the robot steering angle in various cluttered field. At the end hybrid approaches e.g. Potential-Fuzzy, otential-Genetic, Fuzzy-Genetic and Potential-Fuzzy-Genetic are considered for navigation of multiple mobile robots. Initially the combination of two techniques has been selected in order to model the controllers and then all the techniques have been hybridized to get a better controller. These hybrid controllers are first designed and analysed for possible solutions for various situations provided by human intelligence. Then computer simulations have been executed extensively for various known and unknown environments. The proposed hybrid algorithms are embedded in the controllers of the real robots and tested in realistic scenarios to demonstrate the effectiveness of the developed controllers. Finally, the thesis concludes in a chapter describing the comparison of results acquired from various environments, showing that the developed algorithms achieve the main goals proposed by different approaches with a high level of simulations. The main contribution provided in the thesis is the definition and demonstration of the applicability of multiple mobile robots navigations with multiple targets in various environments based on the strategy of path optimisation

Navegação de robô móvel em ambiente com humanos

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia MecânicaA robótica é um tema que desde a sua introdução tem capturado o interesse e a imaginação das pessoas. A sua aplicação menos fantasiosa e mais realista pode ser vista na indústria. No entanto, um dos maiores desafios encontra-se em criar um robô com capacidade de navegação autónoma, incluindo o reconhecimento e desvio de pessoas, em ambientes estruturados. A robótica móvel baseada no controlo por visão tem-se desenvolvido nos últimos 30 anos. Ao longo deste período, diversos modelos matemáticos foram apresentados com o objectivo de resolver os múltiplos desafios que o controlo de uma unidade robótica autónoma apresenta. A visão robótica, tal como no ser Humano, permite à unidade uma compreensão do mundo onde se insere. No caso de o ambiente ser partilhado com pessoas, a necessidade de interpretar e separar uma pessoa de um objecto é fundamental, para a segurança física e psíquica do ser Humano. Esta dissertação propõe o estudo e desenvolvimento de um robô móvel autónomo apto para navegar em espaços com pessoas. Os trabalhos realizados focaram-se na localização no espaço, reconhecimento de pessoas e planeamento e controlo de trajectórias. Com este conjunto de soluções pretende-se contribuir com um sistema autónomo robusto capaz de interagir em segurança em ambientes de trabalho partilhados com pessoas, com elevado grau de confiança de modo a que seja possível implantar num ambiente industrial.Abstract: Robotics is a subject known to capture the imagination of people. It can be seen in a realistic fashion in many fields of modern industry. Current robotics challenges can be found in the deployment of autonomous mobile robotswhen operating in Human shared environments. Vision based mobile robotics is a subject being studied for over 30 years. Robotic Vision, supports the perception of the surrounding world, including the discrimination of objects from people, therefore itis paramount to gurantee the physical and psychological safety of Humans. This work presents a study and development of a mobile robot with the ability to detect people, and to modify the navigation path accordingly. This thesis aims to provide a robust autonomous robot system that can safely operate in Human shared environments, thus being suitable to areal industrial situation