A global human–robot interface that meets the needs of Technical Explosive Ordnance Disposal Specialists (TEDAX) for the manipulation of a robotic arm is of utmost importance to make the task of handling explosives safer, more intuitive and also provide high usability and efficiency. This paper aims to evaluate the performance of a multimodal system for a robotic arm that is based on Natural User Interface (NUI) and Graphical User Interface (GUI). The mentioned interfaces are compared to determine the best configuration for the control of the robotic arm in Explosive Ordnance Disposal (EOD) applications and to improve the user experience of TEDAX agents. Tests were conducted with the support of police agents Explosive Ordnance Disposal Unit-Arequipa (UDEX-AQP), who evaluated the developed interfaces to find a more intuitive system that generates the least stress load to the operator, resulting that our proposed multimodal interface presents better results compared to traditional interfaces. The evaluation of the laboratory experiences was based on measuring the workload and usability of each interface evaluated.

In the agricultural industry, accurate and efficient monitoring of the state of crops during the harvest stage is essential to ensure the quality of fresh produce, so this work proposes a system for monitoring fruit at the harvest stage through the use of drones and the development of image processing algorithms to estimate the measures of the fruit by comparing stereo vision and ArUco Marker; in addition also develops an algorithm to specify the stage of maturity in which they are. The tests were carried out on avocado crops in the Majes valley, Arequipa. In the tests, the effectiveness of the algorithms was obtained, evaluating the data obtained by the system and the actual data, demonstrating a reliable detection and an accurate calculation of the size of the avocados. The results are visualized by means of maps highlighting the different stages of maturity. This innovative approach presents significant potential for improving crop monitoring and management, especially in regions such as fruit exporting in Arequipa, where quality and precision are crucial to consolidate its position in international markets.

This work develops a system capable of determining the presence of a person in the water by classifying a convolutional neural network (CNN). When a drowning is alerted, the drone camera takes aerial captures of the water area, the first trained CNN is in charge of determining if it is deep water or not, while the second CNN is in charge of identifying the presence of a person in that video frame; in case both detections are positive, the drone will drop the life ring it carries. The tests confirm that this system is capable of providing a means of survival to the person in an ideal time.