In this paper, we describe our research on bio-inspired locomotion systems using deformable structures and smart materials, concretely shape memory alloys (SMAs). These types of materials allow us to explore the possibility of building motor-less and gear-less robots. A swimming underwater fish-like robot has been developed whose movements are generated using SMAs. These actuators are suitable for bending the continuous backbone of the fish, which in turn causes a change in the curvature of the body. This type of structural arrangement is inspired by fish red muscles, which are mainly recruited during steady swimming for the bending of a flexible but nearly incompressible structure such as the fishbone. This paper reviews the design process of these bio-inspired structures, from the motivations and physiological inspiration to the mechatronics design, control and simulations, leading to actual experimental trials and results. The focus of this work is to present the mechanisms by which standard swimming patterns can be reproduced with the proposed design. Moreover, the performance of the SMA-based actuators' control in terms of actuation speed and position accuracy is also addressed.

This paper presents an approach for explosive-landmine detection by designing, developing and integrating a Ground Penetrating Radar (GPR) onboard an autonomous aerial drone. Our goal is twofold: firstly, we have tackled the development of a custom-designed lightweight GPR by approaching an interplay between hardware and software radio. This technology is called Software-Defined Radio (SDR). Our SDR-based GPR system results on a much lighter sensing device compared against the conventional GPR systems found in the literature and with the capability of re-configuration in real time. This means our system is capable of detecting landmines under terrains with different dielectric characteristics. Secondly, we introduce the integration of the aforementioned SDR-based GPR into an autonomous drone. To this purpose, we have developed an accurate navigation flight control system based on optical laser technology that enables the drone to operate steady at the corresponding altitude over the ground (50 cm). To this purpose, we propose a novel nonlinear model-dependent control strategy for regulating the altitude (z) and the attitude of the drone (roll , pitch , yaw ) named backstepping+DAF. Experiments have been conducted aimed at characterizing how our SDR-based GPR system performs depending on different landmine morphology. Also, how steady flight is achieved by means of the proposed flight control system that consequently enhances the GPR performance. Results have shown that our SDR-based GPR system is able to detect buried landmines up to cm of depth for semi-wet terrains (humidity about 70%), however, the artifact must cover an area with at least of the material made of metal. By calculating ROC data, the accuracy of the GPR is above 80% under the tested scenarios.

This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops.

This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.