Path following and obstacle avoidance with vision and laser for ROV

O veículo submarino operado remotamente (na sigla em inglês - ROV) tem sido usado em actividades subaquáticas ao longo dos anos. As suas aplicações podem variar desde o uso para actividades de lazer até operações militares ou investigação científica. Existe um grande potencial na automatização de tarefas repetitivas, por exemplo, conduzir o ROV ao destino onde vai operar. O desenvolvimento de um sistema que automatize este processo é um bom ponto de partida para diminuir o controlo por parte do operador humano numa tarefa recorrente. Assim, o objectivo principal desta dissertação passa pelo desenvolvimento de um sistema de navegação que permita ao ROV VideoRay Pro 4 navegar até um determinado destino e encontrar um método de evitar obstáculos que possam surgir no seu caminho. Pretende-se encontrar uma solução de baixo custo que permita criar um sistema simples e funcional, para que este possa ser aplicado a qualquer ROV deste tipo. Para a parte da navegação foi desenvolvido um sistema que faz uso do magnetómetro existente no ROV. Este permite a navegação assistida e ainda a execução de percursos pré-definidos. Foram implementados dois percursos: o seguimento de uma trajectória rectilínea, seguindo a orientação inicial; o seguimento de uma trajectória rectangular, voltando ao ponto inicial. Durante a execução dos percursos mencionados anteriormente é possível detectar e evitar obstáculos. Para a detecção de obstáculos, o sistema baseia-se na visão computacional sendo a câmara do ROV o instrumento principal, auxiliado por um laser acoplado, que projecta uma linha laser no obstáculo permitindo calcular a distância ao mesmo. A utilização da câmara tem várias vantagens dado que é um sensor comum a quase todos os ROVs, tem um custo baixo e existe muita documentação no campo da visão computacional. Estas implementações introduzem três desafios principais: (i) calibração da câmara (ii) calibração do laser e (iii) automação da navegação do veículo. O sistema foi validado numa piscina de um clube de natação, onde foram testadas várias manobras, assim como a detecção e desvio de obstáculos. Os resultados obtidos foram satisfatórios, mostrando um bom desempenho do veículo na correcção de desvios ao longo dos percursos pré-definidos e também na detecção de obstáculos, ainda que com algum atraso no tempo de resposta.Over the years, remotely operated vehicles (ROVs) have been used in underwater activities and their applications range from leisure use to military operations or scientific research. There is a great potential for automation of recurring tasks, for instance, leading the ROV to the destination where it will operate. Developing a system that automates this process is a good starting point for reducing the control by the human operator in a recurring task. The main goal of this thesis is to develop a navigation system that allows ROV VideoRay Pro 4 to navigate to a specific destination and find a method to avoid obstacles that may arise in its path. It also aims to find a low-cost solution that allows to create a simple and functional system, so that it can be applied to any ROV of this type. For the navigation part, the system that was developed makes use of the existing ROV magnetometer. This allows both assisted navigation and the execution of pre-defined routes. Two routes were implemented: a straight line, following the initial orientation; and a rectangular route, returning to the initial point. During the execution of both those routes it is possible to detect and avoid obstacles. To detect obstacles, the system is based on the computer vision being the ROV camera the main instrument, and aided by a laser attached that projects a laser line to the obstacle thus allowing to calculate the distance to it. The use of the camera has several advantages since it is a sensor used by almost all ROVs, it has a low cost and there is significant literature in the field of computer vision. These implementations have three main challenges: (i) camera calibration (ii) laser calibration and (iii) vehicle navigation automation. The system was validated in a pool of a swimming club, where several manoeuvres were tested, as well as the detection and diversion of obstacles. The results were satisfactory, showing a good performance of the vehicle in correcting the deviations along the pre-defined routes and in detecting obstacles, although with some delay in the response time.Mestrado em Engenharia de Computadores e Telemátic

Altitude Control of an Underwatervehicle Based on Computer Vision

The desire of improving and developing new technologies targeting the ocean's supervision is continuously increasing. Since underwater tasks might involve hostile environments far too hazardous for human, it is typical to resort to system based on Remotely Operated Vehicles (ROVs) and/or Autonomous Underwater Vehicles (AUVs). During the extraction of information, the position control of the vehicle is critical. Specifically, the distance between the vehicle and the sea floor must be warily controlled to ensure its safety and the reliability of the missions that require proximity to the object of interest. Commonly, to deal with the altitude control, a system based on sonar technology is used. Although this solution simplifies the problem and is effective in most cases, it carries a lot of disadvantages in some underwater conditions and in some vehicles with certain specifications. Particularly the sensors based on acoustic waves, like the sonar, might present difficulties on the interpretation of the signals received when the vehicle is too close to the obstacle, requiring a minimum distance to retrieve valuable and reliable information. Furthermore, the inclusion of the sonar sensor demands an increase on the energetic cost of the system that in the case of vehicles powered by an external source through a cable like the ROVs is not a problem, but in AUVs, it might be valuable to avoid it since these vehicles are powered by batteries. Lastly, sometimes the space occupation of the sonar sensor represents a problem in some vehicles with meticulous limits relative to space usage, a common problem found in AUVs.In order to overcome these problems, the acquirement of the distance measurement can be accomplish through image processing using a system based on a camera and laser pointer devices. Since several underwater vehicles already have an embedded camera and it is common the existence of laser pointer devices as a scale, this approach is opportune and can accomplish the task with high reliability and efficiency.In this work it is presented a module capable of measuring the distance based on computer vision (Sensor module) and a module able to filter the data gathered though the use of Kalman Filter and capable of using this data to control the distance of the vehicle using a velocity and position controller that are adaptable to the mission characteristics (Filtering and Control module). The vehicle used in order to test the modules created was a profiler developed in \cite{Monteiro}. The Sensor module was implemented based on two laser pointer devices placed parallel to one another beside a CCD camera. In order to calculate the distance of the vehicle towards the obstacle was used the laser triangulation principle. Furthermore, the Sensor module is capable of retrieving information about the quality of the measurements and apply mathematical operations like circular average. It allows the user to fully configure the information that is gathered and what type of operations are performed through an configuration file. The communication with the Sensor module is made through UDP. In order to characterize and test the module, the laser triangulation principle was analyzed and a series of experimental tests were performed to know the error induced through the utilization of non ideal components and possible software limitations. The Filtering and Control module is responsible for the interface between the Sensor module and the vehicle, and the control of the thruster's actuation. It receives the data gathered, filters it through a Kalman filter that is tuned using the quality factor of the measurements, and then makes the information available through a shared memory block to the vehicle's software. The solution adopted regarding the control stands on the switching of two controllers, a velocity controller (based on a PI controller approach), and a position controller (based on a PID controller approach). The mathematical model of the vehicle was used in order to design the parameters of the controllers to accomplish certain temporal demands of the mission. The designed controllers were validated using the simulink toolbox from Matlab.In order to validate the systems created in a real environment, a series of operational tests were performed where the profiler is commanded to different altitudes. These tests were realized on a tank where the environment conditions are controllable and the results can be compared to the exact values.The desire of improving and developing new technologies targeting the ocean's supervision is continuously increasing. Since underwater tasks might involve hostile environments far too hazardous for human, it is typical to resort to system based on Remotely Operated Vehicles (ROVs) and/or Autonomous Underwater Vehicles (AUVs). During the extraction of information, the position control of the vehicle is critical. Specifically, the distance between the vehicle and the sea floor must be warily controlled to ensure its safety and the reliability of the missions that require proximity to the object of interest. Commonly, to deal with the altitude control, a system based on sonar technology is used. Although this solution simplifies the problem and is effective in most cases, it carries a lot of disadvantages in some underwater conditions and in some vehicles with certain specifications. Particularly the sensors based on acoustic waves, like the sonar, might present difficulties on the interpretation of the signals received when the vehicle is too close to the obstacle, requiring a minimum distance to retrieve valuable and reliable information. Furthermore, the inclusion of the sonar sensor demands an increase on the energetic cost of the system that in the case of vehicles powered by an external source through a cable like the ROVs is not a problem, but in AUVs, it might be valuable to avoid it since these vehicles are powered by batteries. Lastly, sometimes the space occupation of the sonar sensor represents a problem in some vehicles with meticulous limits relative to space usage, a common problem found in AUVs.In order to overcome these problems, the acquirement of the distance measurement can be accomplish through image processing using a system based on a camera and laser pointer devices. Since several underwater vehicles already have an embedded camera and it is common the existence of laser pointer devices as a scale, this approach is opportune and can accomplish the task with high reliability and efficiency.In this work it is presented a module capable of measuring the distance based on computer vision (Sensor module) and a module able to filter the data gathered though the use of Kalman Filter and capable of using this data to control the distance of the vehicle using a velocity and position controller that are adaptable to the mission characteristics (Filtering and Control module). The vehicle used in order to test the modules created was a profiler developed in \cite{Monteiro}. The Sensor module was implemented based on two laser pointer devices placed parallel to one another beside a CCD camera. In order to calculate the distance of the vehicle towards the obstacle was used the laser triangulation principle. Furthermore, the Sensor module is capable of retrieving information about the quality of the measurements and apply mathematical operations like circular average. It allows the user to fully configure the information that is gathered and what type of operations are performed through an configuration file. The communication with the Sensor module is made through UDP. In order to characterize and test the module, the laser triangulation principle was analyzed and a series of experimental tests were performed to know the error induced through the utilization of non ideal components and possible software limitations. The Filtering and Control module is responsible for the interface between the Sensor module and the vehicle, and the control of the thruster's actuation. It receives the data gathered, filters it through a Kalman filter that is tuned using the quality factor of the measurements, and then makes the information available through a shared memory block to the vehicle's software. The solution adopted regarding the control stands on the switching of two controllers, a velocity controller (based on a PI controller approach), and a position controller (based on a PID controller approach). The mathematical model of the vehicle was used in order to design the parameters of the controllers to accomplish certain temporal demands of the mission. The designed controllers were validated using the simulink toolbox from Matlab.In order to validate the systems created in a real environment, a series of operational tests were performed where the profiler is commanded to different altitudes. These tests were realized on a tank where the environment conditions are controllable and the results can be compared to the exact values

Evaluation of SLAM algorithms in realistic sensor test conditions

Autonomous robotic systems rely on Simultaneous Localisation and Mapping (SLAM) algorithms that use ranging or other sensory data as input to create a map of the environment. Numerous algorithms have been developed and demonstrated, many of which utilise data from high precision ranging instruments. Small Unmanned Aircraft Systems (UAS) have significant restrictions on the size and weight of sensors they can carry, and light-weight ranging sensors tend to be subject to greater error than their larger counterparts. The effect of these errors on the mapping capabilities of SLAM algorithms will depend on the combination of algorithm and sensor. To quantitatively determine the quality of the map, a map quality metric is needed. This thesis presents an evaluation of the mapping performance of a variety of SLAM algorithms that are freely available in the Robot Operating System (ROS), in conjunction with ranging data from various ranging sensors suitable for use onboard small UAS. To compare the quality of the generated maps, an existing metric was initially employed, however deficiencies noted in this metric led to the development of two new metrics. A discussion of both the existing and new map quality metrics, and the advantages and disadvantages of each, is presented as part of this thesis. To evaluate the performance of algorithm/sensor combinations, ranging data was collected from various sensors in a known environment. Both sensor poses and the ground truth map were obtained using a highly-accurate motion capture system. The measured sensor poses were then corrupted with noise and drift to simulate odometry measurements required for the SLAM algorithms. Of the SLAM algorithms tested, Gmapping was found to produce high quality maps with wide-field-of-regard range sensors in the presence of odometry noise and drift. KartoSLAM produced similar maps to Gmapping (with wide field of regard sensors), though it did not cope as well with odometry errors. Hector Mapping tends to excel at creating maps with wide field of regard ranging sensors