An evaluation of 2D SLAM techniques available in Robot Operating System

n this work, a study of several laser-based 2D Simultaneous Localization and Mapping (SLAM) techniques available in Robot Operating System (ROS) is conducted. All the approaches have been evaluated and compared in 2D simulations and real world experiments. In order to draw conclusions on the performance of the tested techniques, the experimental results were collected under the same conditions and a generalized performance metric based on the k-nearest neighbours concept was applied. Moreover, the CPU load of each technique is examined. This work provides insight on the weaknesses and strengths of each solution. Such analysis is fundamental to decide which solution to adopt according to the properties of the intended final application

3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization

This work presents a 3D multi-robot exploration framework for a team of UGVs moving on uneven terrains. The framework was designed by casting the two-level coordination strategy presented in [1] into the context of multi-robot exploration. The resulting distributed exploration technique minimizes and explicitly manages the occurrence of conflicts and interferences in the robot team. Each robot selects where to scan next by using a receding horizon next-best-view approach [2]. A sampling-based tree is directly expanded on segmented traversable regions of the terrain 3D map to generate the candidate next viewpoints. During the exploration, users can assign locations with higher priorities on-demand to steer the robot exploration toward areas of interest. The proposed framework can be also used to perform coverage tasks in the case a map of the environment is a priori provided as input. An open-source implementation is available online

Fusing sonars and LRF data to perform SLAM in reduced visibility scenarios

Simultaneous Localization and Mapping (SLAM) approaches have evolved considerably in recent years. However, there are many situations which are not easily handled, such as the case of smoky, dusty, or foggy environments where commonly used range sensors for SLAM are highly disturbed by noise induced in the measurement process by particles of smoke, dust or steam. This work presents a sensor fusion method for range sensing in Simultaneous Localization and Mapping (SLAM) under reduced visibility conditions. The proposed method uses the complementary characteristics between a Laser Range Finder (LRF) and an array of sonars in order to ultimately map smoky environments. The method was validated through experiments in a smoky indoor scenario, and results showed that it is able to adequately cope with induced disturbances, thus decreasing the impact of smoke particles in the mapping task

Effective Cooperation and Scalability in Multi-Robot Teams for Automatic Patrolling of Infrastructures

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraIn the digital era that we live in, advances in technology have proliferated throughout our society, quickening the completion of tasks that were painful in the old days, improving solutions to the everyday problems that we face, and generally assisting human beings both in their professional and personal life. Robotics is a clear example of a broad technological field that evolves every day. In fact, scientists predict that in the upcoming few decades, robots will naturally interact and coexist alongside human beings. While it is true that robots already have a strong presence in industrial environments, e.g., robotic arms for manufacturing, the average person still looks upon robots with suspicion, since they are not acquainted by such type of technology. In this thesis, the author deploys teams of mobile robots in indoor scenarios to cooperatively perform patrolling missions, which represents an effort to bring robots closer to humans and assist them in monotonous or repetitive tasks, such as supervising and monitoring indoor infrastructures or simply cooperatively cleaning floors. In this context, the team of robots should be able to sense the environment, localize and navigate autonomously between way points while avoiding obstacles, incorporate any number of robots, communicate actions in a distributed way and being robust not only to agent failures but also communication failures, so as to effectively coordinate to achieve optimal collective performance. The referred capabilities are an evidence that such systems can only prove their reliability in real-world environments if robots are endowed with intelligence and autonomy. Thus, the author follows a line of research where patrolling units have the necessary tools for intelligent decision-making, according to the information of the mission, the environment and teammates' actions, using distributed coordination architectures. An incremental approach is followed. Firstly, the problem is presented and the literature is deeply studied in order to identify potential weaknesses and research opportunities, backing up the objectives and contributions proposed in this thesis. Then, problem fundamentals are described and benchmarking of multi-robot patrolling algorithms in realistic conditions is conducted. In these earlier stages, the role of different parameters of the problem, like environment connectivity, team size and strategy philosophy, will become evident through extensive empirical results and statistical analysis. In addition, scalability is deeply analyzed and tied with inter-robot interference and coordination, imposed by each patrolling strategy. After gaining sensibility to the problem, preliminary models for multi-robot patrol with special focus on real-world application are presented, using a Bayesian inspired formalism. Based on these, distributed strategies that lead to superior team performance are described. Interference between autonomous agents is explicitly dealt with, and the approaches are shown to scale to large teams of robots. Additionally, the robustness to agent and communication failures is demonstrated, as well as the flexibility of the model proposed. In fact, by later generalizing the model with learning agents and maintaining memory of past events, it is then shown that these capabilities can be inherited, while at the same time increasing team performance even further and fostering adaptability. This is verified in simulation experiments and real-world results in a large indoor scenario. Furthermore, since the issue of team scalability is highly in focus in this thesis, a method for estimating the optimal team size in a patrolling mission, according to the environment topology is proposed. Upper bounds for team performance prior to the mission start are provided, supporting the choice of the number of robots to be used so that temporal constraints can be satisfied. All methods developed in this thesis are tested and corroborated by experimental results, showing the usefulness of employing cooperative teams of robots in real-world environments and the potential for similar systems to emerge in our society.FCT - SFRH/BD/64426/200

MRsensing: environmental monitoring and context recognition with cooperative mobile robots in catastrophic incidents

Dissertação de Mestrado em Engenharia Electrotécnica e de Computadores, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraMulti-sensor information fusion theory concerns the environmental perception activities to combine data from multiple sensory resources. Humans, as any other animals, gather information from the environment around them using different biological sensors. Combining them allows structuring the decisions and actions when interacting with the environment. Under disaster conditions, effective mult-robot information sensor fusion can yield a better situation awareness to support the collective decision-making. Mobile robots can gather information from the environment by combining data from different sensors as a way to organize decisions and augment human perception. The is especially useful to retrieve contextual environmental information in catastrophic incidents where human perception may be limited (e.g., lack of visibility). To that end, this work proposes a specific configuration of sensors assembled in a mobile robot, which can be used as a proof of concept to measure important environmental variables in an urban search and rescue (USAR) mission, such as toxic gas density, temperature gradient and smoke particles density. This data is processed through a support vector machine classifier with the purpose of detecting relevant contexts in the course of the mission. The outcome provided by the experiments conducted with TraxBot and Pioneer-3DX robots under the Robot Operating System framework opens the door for new multi-robot applications on USAR scenarios. This work was developed within the CHOPIN research project1 which aims at exploiting the cooperation between human and robotic teams in catastrophic accidents.O tema da fusão sensorial abrange a perceção ambiental para combinar dados de vários recursos naturais. Os seres humanos, como todos os outros animais, recolhem informações do seu redor, utilizando diferentes sensores biológicos. Combinando-se informação dos diferentes sensores é possível estruturar decisões e ações ao interagir com o meio ambiente. Sob condições de desastres, a fusão sensorial de informação eficaz proveniente de múltiplos robôs pode levar a um melhor reconhecimento da situação para a tomada de decisão coletiva. Os robôs móveis podem extrair informações do ambiente através da combinação de dados de diferentes sensores, como forma de organizar as decisões e aumentar a perceção humana. Isto é especialmente útil para obter informações de contexto ambientais em cenários de catástrofe, onde a perceção humana pode ser limitada (por exemplo, a falta de visibilidade). Para este fim, este trabalho propõe uma configuração específica de sensores aplicados num robô móvel, que pode ser usado como prova de conceito para medir variáveis ambientais importantes em missões de busca e salvamento urbano (USAR), tais como a densidade do gás tóxico, gradiente de temperatura e densidade de partículas de fumo. Esta informação é processada através de uma máquina de vetores de suporte com a finalidade de classificar contextos relevantes no decorrer da missão. O resultado fornecido pelas experiências realizadas com os robôs TraxBot e Pioneer 3DX usando a arquitetura Robot Operating System abre a porta para novas aplicações com múltiplos robôs em cenários USAR

P2X receptors as targets for the treatment of status epilepticus.

Prolonged seizures are amongst the most common neurological emergencies. Status epilepticus is a state of continuous seizures that is life-threatening and prompt termination of status epilepticus is critical to protect the brain from permanent damage. Frontline treatment comprises parenteral administration of anticonvulsants such as lorazepam that facilitate γ-amino butyric acid (GABA) transmission. Because status epilepticus can become refractory to anticonvulsants in a significant proportion of patients, drugs which act on different neurotransmitter systems may represent potential adjunctive treatments. P2X receptors are a class of ligand-gated ion channel activated by ATP that contributes to neuro- and glio-transmission. P2X receptors are expressed by both neurons and glia in various brain regions, including the hippocampus. Electrophysiology, pharmacology and genetic studies suggest certain P2X receptors are activated during pathologic brain activity. Expression of several members of the family including P2X2, P2X4, and P2X7 receptors has been reported to be altered in the hippocampus following status epilepticus. Recent studies have shown that ligands of the P2X7 receptor can have potent effects on seizure severity during status epilepticus and mice lacking this receptor display altered seizures in response to chemoconvulsants. Antagonists of the P2X7 receptor also modulate neuronal death, microglial responses and neuroinflammatory signaling. Recent work also found altered neuronal injury and inflammation after status epilepticus in mice lacking the P2X4 receptor. In summary, members of the P2X receptor family may serve important roles in the pathophysiology of status epilepticus and represent novel targets for seizure control and neuroprotection

First report of fatty acid-derived alkaloids produced by species of the ladybird genus Scymnus (Coccinellidae: Coleoptera)

ABSTRACT: Species in the genus Scymnus Kugelann are common, but there is little information available on their defence compounds. Pupae are covered with setae on tips of which there are small droplets of liquid. This study explored the patterns of the distribution of the droplets and the influence of diet on their production in five species of Scymnus. GC-MS analyses were used to determine the chemical identity of droplets. All these species produced droplets, but the patterns in the distributions varied among species. Preliminary data indicates a de novo synthesis of the droplet compounds by these ladybirds. The results of the GC-MS analyses suggest that Scymnus spp. produce azamacrolides, which are fatty acid-derived alkaloids.info:eu-repo/semantics/publishedVersio