Abstract
This paper presents a high-resolution imaging system developed for plankton imaging in the context of the MarinEye integrated biological sensor [1]. This sensor aims to produce an autonomous system for marine integrated physical, chemical and biological monitoring combining imaging, acoustic, sonar, and fraction filtration systems (coupled to DNA/RNA preservation) as well as sensors for targeting physical-chemical variables in a modular and compact system that can be deployed on fixed and mobile platforms, such as the TURTLE robotic deep sea lander [2]. The results obtained with the system both in laboratory conditions and in the field are presented and discussed, allowing the characterization and validation of the performance of the Autonomous High-Resolution Image Acquisition System for Plankton.

Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area's roughness, and the spot's slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

Abstract
For the past years, the interest in the use of Unmanned Aerial Vehicles (UAVs) has been increasing due to the multiple research topics provided by the field of aerial robotics. Conversely, vehicles are susceptible to failures or malfunctions. Consequently, one main emergent research topic is the detection of a safe landing spot in these emergency scenarios. Therefore, this paper exposes and details the multiple techniques that attempt to solve the problem of landing site detection. This paper aims to present the current literature with several sensors that can be used to solve the aforementioned problem. Finally, the paper presents our proposed approach with some preliminary results in simulation. © CLAWAR Association Ltd.

Abstract
This paper addresses the topic of emergency landing spot detection for Unmanned Aerial Vehicles (UAVs). During operation, the vehicle is susceptible to faults and must be able to predict the land spot able to ensure that the UAV will be able to land without damages and injuries to humans and structures. A method was developed, based on geometric features extracted from Light Detection And Ranging (LIDAR) data. A simulation environment was developed in order to validate the effectiveness and the robustness of the proposed method.

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