Valéria de Carvalho Santos

Extraction of clavulanic acid using aqueous two-phase micellar system

An investigation of clavulanic acid behavior in an aqueous two‐phase micellar system employing the surfactants n‐decyltetraethylene oxide (C10E4) and dodecyldimethylamine oxide (DDAO) was carried out. According to the results, clavulanic acid partitions evenly between the two phases of DDAO micellar system, mixed DDAO–C10E4 micellar system, as well as C10E4 micellar system. Therefore, electrostatic interactions between positively charged DDAO‐containing micelles and negatively charged drug were not strong enough to influence the partitioning. Nevertheless, clavulanic acid extraction from Streptomyces clavuligerus fermentation broth in C10E4 micellar system employing a previous protein denaturation step provided recovery of 52% clavulanic acid with removal of 70% of the contaminant proteins, which is already promising as a purification strategy.

A hybrid GA-ANN approach for autonomous robots topological navigation

This paper proposes a hybrid approach using genetic algorithm and artificial neural networks for autonomous path planning and motion control for mobile robots. A topological navigation approach is adopted, using the environment mapped as a graph. A genetic algorithm is used to generate and evolve a set of feasible actions, aiming to lead the robot to the goal considering the shortest path. Each action is a different reactive behavior designed for a specific environment feature such as corridors, turns or intersections. Then, an artificial neural network is trained to recognize the different environment features, and the next behavior is activated every time the ANN detects a transition. Experiments were performed in Player/Stage robotics simulator and obtained results showed this approach as a promising way to plan and execute a path.

An exploratory path planning method based on genetic algorithm for autonomous mobile robots

The path planning task for mobile robots consists of define a trajectory to the robot leaves its start position and reach its goal without to collide with obstacles. In general, the robot needs to know previous information about the environment (e.g. maps, predefined routes) to plan its trajectory. In an exploration task, the robot does not know the environment and discovers it when moving to reach the goal coordinates. In this paper, an exploratory path planning aiming to reach a goal position is studied and a new method based on genetic algorithm, topological environment representation and realistic robot actions is proposed. In this method, the robots execute a sequence of reliable local actions (simple reactive behaviors) to move through the unknown environment, adopting a topological environment representation. They plan the path at the same time the environment is explored, in which the genetic algorithm evolves the sequence of actions to be executed. The results show that the squad of robots (GA population) reach the goal faster than an individual search. The proposed approach deal with environment traps better than the classical search A* algorithm and a variation of the A*, named C*, here also introduced.

A FORMAÇÃO DE PROFESSORES EM COMUNIDADES DE PRÁTICA: O CASO DE UM GRUPO DE PROFESSORES DE QUÍMICA EM FORMAÇÃO INICIAL

This work deals with communities of practice and their contribution to pre-service teacher training. A group of eight pre-service chemistry teachers was accompanied during their participation in the PIBID program. Based on their interaction in planning teaching activities, the group was characterized as a community of practice. For this characterization the three dimensions of communities of practice were observed: mutual engagement, joint enterprise and shared repertoire. The results showed that the community of practice was essential for the training of pre-service chemistry teachers. Through community practice, pre-service teachers were able to learn more about their future practice as chemistry teachers.

ND-NCD: Environmental Characteristics Recognition and Novelty Detection for Mobile Robots Control and Navigation

Mobile robot applications usually perform a path planning and its execution considers a previous known map. On the other hand, some application must explore the environment, defining a path from a source to a destination point, without knowing the environment map. The environment exploration, path planning towards a goal and navigation control tasks should be done at the same time. This study proposes a new method for mobile robot control and navigation based on the environmental characteristics recognition and novelty detection, named ND-NCD (Novelty Detection with Normalized Compression Distance). This method can be used as a key component in environment exploration and topological mapping tasks. In a previous work, a Genetic Algorithm (GA) for exploratory path planning was implemented to create a topological map (graph) from the source to the destination point, generating a set of actions which the robot must perform to achieve the goal. Each action was associated to a different reactive behavior specifically designed for characteristic places of the environment, such as corridors, curves or intersections. The proposed method, ND-NCD is used to recognize such different environmental characteristics, allowing to activate/associate the adequate actions whenever the method recognizes a context change (new context). This allows us to integrate the GA based environment exploration method together with the robot control reactive behaviors, which can be properly selected and switched according to the environmental characteristics detected/discovered by the ND-NCD. The ND-NCD uses the robot perception (e.g. laser sensor) to detect novelty and to recognize already known characteristics, thus allowing an incremental representation of the environment structures. The experiments were performed in the Player/Stage simulator and in a real indoor environment. ND-NCD performance is compared with a Neural Network trained to recognize context changes in the same environment. The results indicate that ND-NCD is a promising approach to be used in exploration and navigation control for mobile robots with the advantage of detecting a context change just knowing an initial state (corridor) from the environment. The proposed method does not need to be trained previously in order to know all the states (supervised training), being able to incrementally discover the different environment configurations.

Exploratory path planning using the Max-min ant system algorithm

In the path planning problem for autonomous mobile robots, robots have to plan their path from the start position to the goal. In this paper, we investigate the application of the MMAS algorithm to the exploratory path planning problem, in which the robots should explore the environment at the same time they plan the path. Max-min ant system is an ant colony optimization algorithm that exploits the best solutions found. In addition, to analyze the quality of solutions obtained, we also analyze the traveled distance spent by robots in the first iteration of the algorithm. The environment is previously unknown to the robots, although it is represented by a topological map, that does not require precise information from the environment and provides a simple way to execute the navigation of the path. Thus, the paths are represented by a sequence of actions that the robots should execute to reach the goal. The navigation of the best solution found was implemented in a realistic robotic simulator. The proposed algorithm provides a very good performance in relation to a genetic algorithm and the well-known A* algorithm that deal with this problem.

Novelty Detection Based on NCD for Navigation Control of Mobile Robots

In the path planning and its execution, a path must be defined from a source point to a destination point and then the navigation control task is executed. This study proposes a new method of novelty detection, named NDN (Novelty Detection with Normalized Compression Distance), to contribute in the navigation control of mobile robots. First, a genetic algorithm for path planning, based on a topological map, is used to generate a set of actions which the robot must perform to achieve the goal. Each action is a different reactive behavior designed for characteristic places of the environment, such as corridors, curves or intersections. Next, NDN is used to recognize such different environmental characteristics, activating the action whenever the method recognizes a context change. The experiments were performed in the Player/Stage robotics simulator and in a real indoor environment. NDN performance is compared with a Neural Network trained to recognize context changes in the same environment. The results reported indicate that NDN is a promising approach to be used in navigation control for mobile robots with the advantage of detecting a context change just knowing a initial state (corridor) from the environment.

Imitation of Facial Expressions for a Virtual Robotic Head

Facial expression has an important role for natural interaction among social robots and humans. In this paper, an architecture conceived for imitation of facial expressions is proposed. We describe the computer vision algorithm that was implemented for real-time geometric facial features extraction. It covers face detection, extraction of eyes, eyebrows, nostrils and mouth characteristic points, as well as an estimation of the face tilt angle. Fast processing has been prioritized over precision. Although we cannot make a definitive statement before all of the modules are built, the experiments show promising results on different subjects and illumination circumstances.