Towards a ground navigation system based in visual feedback provided by a mini UAV

This paper addresses initial efforts to develop a navigation system for ground vehicles supported by visual feedback from a mini aerial vehicle. A visual-based algorithm computes the ground vehicle pose in the world frame, as well as possible obstacles within the ground vehicle pathway. Relying on that information, a navigation and obstacle avoidance system is used to re-plan the ground vehicle trajectory, ensuring an optimal detour. Finally, some experiments are presented employing a unmanned ground vehicle (UGV) and a low cost mini unmanned aerial vehicle (UAV).

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.

A vision based aerial rbot solution for the IARC 2014 by the Technical University of Madrid

The IARC competitions aim at making the state of the art in UAV progress. The 2014 challenge deals mainly with GPS/Laser denied navigation, Robot-Robot interaction and Obstacle avoidance in the setting of a ground robot herding problem. We present in this paper a drone which will take part in this competition. The platform and hardware it is composed of and the software we designed are introduced. This software has three main components: the visual information acquisition, the mapping algorithm and the Aritificial Intelligence mission planner. A statement of the safety measures integrated in the drone and of our efforts to ensure field testing in conditions as close as possible to the challenge?s is also included.

Método de desvio de obstáculos aplicado em veículo autônomo.

A operação de veículos autônomos necessita de meios para evitar colisões quando obstáculos não conhecidos previamente são interpostos em sua trajetória. Algoritmos para executar o desvio e sensores apropriados para a detecção destes obstáculos são essenciais para a operação destes veículos. Esta dissertação apresenta estudos sobre quatro algoritmos de desvio de obstáculos e tecnologia de três tipos de sensores aplicáveis à operação de veículos autônomos. Após os estudos teóricos, um dos algoritmos foi testado para a comprovação da aplicabilidade ao veículo de teste. A etapa experimental foi a realização de um programa, escrito em linguagem de programação Java, que aplicou o algoritmo Inseto 2 para o desvio de obstáculos em uma plataforma robótica (Robodeck) com o uso de sensores ultrassônicos embarcados na referida plataforma. Os experimentos foram conduzidos em ambiente fechado (indoor), bidimensional e horizontal (plano), fazendo o Robodeck executar uma trajetória. Para os testes, obstáculos foram colocados para simular situações variadas e avaliar a eficácia do algoritmo nestas configurações de caminho. O algoritmo executou o desvio dos obstáculos com sucesso e, quando havia solução para a trajetória, ela foi encontrada. Quando não havia solução, o algoritmo detectou esta situação e parou o veículo.

Developmental changes in the response to obstacles during prehension

Adults are proficient at reaching to grasp objects of interest in a cluttered workspace. The issue of concern, obstacle avoidance, was studied in 3 groups of young children aged 11-12, 9-10, and 7-8 years (n = 6 in each) and in 6 adults aged 18-24 years. Adults slowed their movements and decreased their maximum grip aperture when an obstacle was positioned close to a target object (the effect declined as the distance between target and obstacle increased). The children showed the same pattern, but the magnitude of the effect was quite different. In contrast to the adults, the obstacle continued to have a large effect when it was some distance from the target (and provided no physical obstruction to movement).

A ROS-based Workspace Control and Trajectory Planner for a seven degrees of freedom Robotic arm

In this Bachelor Thesis I want to provide readers with tools and scripts for the control of a 7DOF manipulator, backed up by some theory of Robotics and Computer Science, in order to better contextualize the work done. In practice, we will see most common software, and developing environments, used to cope with our task: these include ROS, along with visual simulation by VREP and RVIZ, and an almost "stand-alone" ROS extension called MoveIt!, a very complete programming interface for trajectory planning and obstacle avoidance. As we will better appreciate and understand in the introduction chapter, the capability of detecting collision objects through a camera sensor, and re-plan to the desired end-effector pose, are not enough. In fact, this work is implemented in a more complex system, where recognition of particular objects is needed. Through a package of ROS and customized scripts, a detailed procedure will be provided on how to distinguish a particular object, retrieve its reference frame with respect to a known one, and then allow navigation to that target. Together with technical details, the aim is also to report working scripts and a specific appendix (A) you can refer to, if desiring to put things together.

Uma abordagem evolucionária para o planejamento online de rotas para múltiplos robôs móveis em ambiente multiagente

O problema de planejamento de rotas de robôs móveis consiste em determinar a melhor rota para um robô, em um ambiente estático e/ou dinâmico, que seja capaz de deslocá-lo de um ponto inicial até e um ponto final, também em conhecido como estado objetivo. O presente trabalho emprega o uso de uma abordagem baseada em Algoritmos Genéticos para o planejamento de rotas de múltiplos robôs em um ambiente complexo composto por obstáculos fixos e obstáculos moveis. Através da implementação do modelo no software do NetLogo, uma ferramenta utilizada em simulações de aplicações multiagentes, possibilitou-se a modelagem de robôs e obstáculos presentes no ambiente como agentes interativos, viabilizando assim o desenvolvimento de processos de detecção e desvio de obstáculos. A abordagem empregada busca pela melhor rota para robôs e apresenta um modelo composto pelos operadores básicos de reprodução e mutação, acrescido de um novo operador duplo de refinamento capaz de aperfeiçoar as melhores soluções encontradas através da eliminação de movimentos inúteis. Além disso, o calculo da rota de cada robô adota um método de geração de subtrechos, ou seja, não calcula apenas uma unica rota que conecta os pontos inicial e final do cenário, mas sim várias pequenas subrotas que conectadas formam um caminho único capaz de levar o robô ao estado objetivo. Neste trabalho foram desenvolvidos dois cenários, para avaliação da sua escalabilidade: o primeiro consiste em um cenário simples composto apenas por um robô, um obstáculo movel e alguns obstáculos fixos; já o segundo, apresenta um cenário mais robusto, mais amplo, composto por múltiplos robôs e diversos obstáculos fixos e moveis. Ao final, testes de desempenho comparativos foram efetuados entre a abordagem baseada em Algoritmos Genéticos e o Algoritmo A*. Como critério de comparação foi utilizado o tamanho das rotas obtidas nas vinte simulações executadas em cada abordagem. A analise dos resultados foi especificada através do Teste t de Student.

AUTONOMOUS POWER DISTRIBUTION SYSTEMS

Using robotic systems for many missions that require power distribution can decrease the need for human intervention in such missions significantly. For accomplishing this capability a robotic system capable of autonomous navigation, power systems adaptation, and establishing physical connection needs to be developed. This thesis presents developed path planning and navigation algorithms for an autonomous ground power distribution system. In this work, a survey on existing path planning methods along with two developed algorithms by author is presented. One of these algorithms is a simple path planner suitable for implementation on lab-size platforms. A navigation hierarchy is developed for experimental validation of the path planner and proof of concept for autonomous ground power distribution system in lab environment. The second algorithm is a robust path planner developed for real-size implementation based on lessons learned from lab-size experiments. The simulation results illustrates that the algorithm is efficient and reliable in unknown environments. Future plans for developing intelligent power electronics and integrating them with robotic systems is presented. The ultimate goal is to create a power distribution system capable of regulating power flow at a desired voltage and frequency adaptable to load demands.

Perceiving the 3d world from single images

Depth represents a crucial piece of information in many practical applications, such as obstacle avoidance and environment mapping. This information can be provided either by active sensors, such as LiDARs, or by passive devices like cameras. A popular passive device is the binocular rig, which allows triangulating the depth of the scene through two synchronized and aligned cameras. However, many devices that are already available in several infrastructures are monocular passive sensors, such as most of the surveillance cameras. The intrinsic ambiguity of the problem makes monocular depth estimation a challenging task. Nevertheless, the recent progress of deep learning strategies is paving the way towards a new class of algorithms able to handle this complexity. This work addresses many relevant topics related to the monocular depth estimation problem. It presents networks capable of predicting accurate depth values even on embedded devices and without the need of expensive ground-truth labels at training time. Moreover, it introduces strategies to estimate the uncertainty of these models, and it shows that monocular networks can easily generate training labels for different tasks at scale. Finally, it evaluates off-the-shelf monocular depth predictors for the relevant use case of social distance monitoring, and shows how this technology allows to overcome already existing strategies limitations.

On the motion planning & control of nonlinear robotic systems

In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Sviluppo e sperimentazione di un sistema "obstacle detection & collision avoidance" per velivoli multirotori

Il lavoro di tesi svolto riguarda lo sviluppo e la sperimentazione di un primo prototipo di sistema per l’obstacle detection e collision avoidance, capace di identificare un ostacolo e inibire i comandi del pilota in modo da evitare collisioni.

Comment On 'practical Advice For Avoidance Of Pain Associated With Insertion Of Intrauterine Contraceptives': Authors' Response.

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Tradução, adaptação transcultural e validação do "Body Image Avoidance Questionnaire (BIAQ)" e do "Body Checking Questionnaire (BCQ)" para lingua portuguesa no Brasil

Universidade Estadual de Campinas . Faculdade de Educação Física