A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning

The need to improve motion planning techniques for manipulator robots, and new effective strategies to manipulate different objects to perform more complex tasks, is crucial for various real-world applications where robots cooperate with humans. This paper proposes a novel framework that aims to improve the motion planning of a robotic agent (a manipulator robot) through semantic knowledge-based reasoning. The Semantic Web Rule Language (SWRL) was used to infer new knowledge based on the known environment and the robotic system. Ontological knowledge, e.g., semantic maps, were generated through a deep neural network, trained to detect and classify objects in the environment where the robotic agent performs. Manipulation constraints were deduced, and the environment corresponding to the agent’s manipulation workspace was created so the planner could interpret it to generate a collision-free path. For reasoning with the ontology, different SPARQL queries were used. The proposed framework was implemented and validated in a real experimental setup, using the planning framework ROSPlan to perform the planning tasks. The proposed framework proved to be a promising strategy to improve motion planning of robotics systems, showing the benefits of artificial intelligence, for knowledge representation and reasoning in robotics.info:eu-repo/semantics/publishedVersio

Computational intelligence applied to discriminate bee pollen quality and botanical origin

The aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To obtain the predominant plant genus of pollen (was the output variable), based on physicochemical composition (were the input variables of the predictive model), prediction models were learned from data. For the inverse case study, input/output variables were swapped. The probabilistic NN prediction model obtained 98.4% of correct classification of the predominant plant genus of pollen. To obtain the secondary and tertiary plant genus of pollen, the results present a lower accuracy. To predict the physicochemical characteristic of a mixture of bee pollen, given their botanical origin, fuzzy models proven the best results with small prediction errors, and variability lower than 10%.info:eu-repo/semantics/publishedVersio

Survey on robotic systems for internal logistics

The evolution of production systems has established major challenges in internal logistics. In order to overcome these challenges, new automation solutions have been developed and implemented. This paper is a literature review and analysis of selected scientific studies, which has as the main focus the existing solutions in robotics for internal logistics. The review aims to provide a broad perspective of the existing robotic systems for internal logistics to determine which research paths have been followed to date and highlight the current and future research directions. The survey has been subdivided into the following topics: localisation and path planning; task planning; optimisation and knowledge representation in robotic systems; and applications. The analysis of the works developed until the date of this review highlights the appearance of strategies in the different disciplines based on meta-heuristics. These are replacing the classical and heuristic approaches due to their limitations in dealing with a large amount of information in internal logistic systems. Due to the increase of information that robotic agents have to process, strategies based on semantic knowledge have been gaining prominence to make the domain knowledge explicit and eliminate ambiguities, allowing agents to reason and facilitate knowledge sharing between robotic agents and humans.info:eu-repo/semantics/publishedVersio

Optimizing probabilistic fuzzy systems for classification using metaheuristics

Two new methods for the optimization of probabilistic fuzzy classifiers are proposed. Probabilistic fuzzy systems are specially attractive due to their explicit and simultaneous modelling of two kinds of uncertainty, namely vagueness in linguistic terms (fuzziness) and probabilistic uncertainty. The current method uses the maximization of the likelihood with the stochastic gradient descent, which not only converges to local minima but also does not guarantee the minimization of the misclassification error. The proposed methods address this specific problem by incorporating global search techniques. The first algorithm proposed is a genetic algorithm with simple crossover and mutation operations. The other is a first generation memetic algorithm which combines the genetic algorithm with the stochastic gradient descent. A total of five benchmarks were used to compare the three algorithms. The results show that the proposed methods have an average relative improvement of 2% and 6% for the accuracy with the genetic and memetic algorithms, respectively

Fuzzy classification of bariatric post-surgery effectiveness

The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome