This paper reports a training process undertaken by teachers from the Universidad Politécnica Salesiana (Salesian Polytechnic University) in Guayaquil. The process was carried out between the months of March and April 2020 using crowdlearning as a teacher training innovative initiative to face the challenge of online university education as a measure proposed by the Ecuadorian Government to tackle the health crisis caused by COVID-19. This crowdlearning proposal convened, trained and motivated faculty to face the health emergency in the academic period that was about to begin (May-September 2020). This initiative, which originated from a group of professors, summoned (voluntarily) the entire academic team from the Guayaquil campus to be part of a virtual space for the exchange of knowledge on the use of technologies for virtual education. A training experience based on virtual conferences was developed applying multiple teaching tools, exploring innovative strategies for online education, and training the academics in the use of digital tools to enhance the teachinglearning process. Beside the meaningful and effective learning on the use of online and virtual teaching-learning strategies, resources and tools, it was possible to help the teachers mitigate the negative effects of isolation such as apprehension, fear, anxiety about the unknown and uncertainty, according to what the participants stated.

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<div><p class="4">Various researches in the field of robotics have made great progress in developing methods to effectively determine the position of robots in unknown environments. The simultaneous localization and mapping (SLAM) task make determining the current position of the robot and performing path mapping possible. In this mapping, solid elements (landmarks) existing in the actual environment are even detected, which indicate that the direction of the robot changes during walking. This scheme provides the implementation analysis of the probabilistic particle filter method, which ensures the correct performance in the controlled actual scene under specific conditions, obtains the non-network connection environment information by storing the data in the temperature value sampling in the CVS file, and monitors the temperature measurement by displaying the heat map. Successful analysis must ensure the robustness of the results obtained when implementing these systems and take into account the feasibility of applying this work to the proposed objectivesd.</p></div>