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- Communication Protocol for Unmanned Vehicles : An Architectural ApproachPublication . Ramos, João; Ribeiro, Roberto; Safadinho, David; Barroso, João; Pereira, AntonioThe great potential of Unmanned Vehicles (UV) for services is supported by the evolution of drones and their flexible ability to help us performing dangerous, boring and difficult tasks. The common communication solutions e.g., radio, consider a pilot and a vehicle in the same location. Besides, the alternatives based on Ground Control Stations (GCS) are difficult to configure, which makes it difficult for a common user and a developer to use and/or implement new services for drones. This work proposes a solution with a new messaging protocol to overcome the previous problems, allowing controlling a UV in a remote location after a simple configuration. The implementation of this approach is based on a cloud platform, responsible for UV and user management, and on TCP/IP WebSockets for the user to control remotely located vehicles, anytime and anywhere. This research starts with the analysis of the prior work regarding UV communication technologies and the available GCS. Minding the identified problems, we designed an architecture that represents a cloud platform and multiple users and UVs in different networks. To prototype this architecture, we developed the user interface, the platform and the UV control module. The functional assessment considers a generic simplified and controlled scenario, focusing on the real-time control and video stream. The results confirmed the possibility to control a UV in a different location, in real-time, with an average response time of 25ms. This work provides valuable insights regarding the communication in the fields of anytime and anywhere device control and vehicle-based services.
- UAV Landing Using Computer Vision Techniques for Human DetectionPublication . Safadinho, David; Ramos, João; Ribeiro, Roberto; Filipe, Vítor; Barroso, João; Pereira, AntónioThe capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.