Energy-Aware Planning-Scheduling for Autonomous Aerial Robots

The TeamPlay Project: Analysing and Optimising Time, Energy, and Security for Cyber-Physical Systems

“…The second use case aims at increasing the quality of agricultural produce, relying on diverse sensing mechanisms and facilitating the transition towards precision agriculture (PA), by, e.g., detecting ground hazards. In both cases, fixed-wing drones, i.e., UAVs where propellers provide thrust, wings lift, and maneuvers are performed utilizing control surfaces [31], embed a computing payload connected to a camera [32]. Object detection is performed on the payload, and any results are transmitted to the ground station.…”

2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)

“…Three different hardware platforms have been tested: 1) an Apalis TK1; 2) Nvidia TX2; and, 3) Nvidia Nano. In all cases, Linux OS runs use case-specific software, e.g., C++/CUDA computer vision algorithms for detections on the GPU, and is eventually complemented with Robot Operating System (ROS) middleware [31], [33]. We applied the TeamPlay toolchain for unpredictable architectures but, as a result of platform complexity, omitted fact checker and SecurityAnalyser, which both require further investigation.…”

“…Using our TeamPlay methodology, we observe an energy improvement of 18%, resulting in the flight time being increased by approximately 4 minutes. Conversely to the SAR use case, the PA use case [31]- [33] utilized merely energy analysis, yet enabled flight time optimisations. When cruising, the mechanical components of the UAV consumed 28 Watts on average, whereas software components consumed between 2 and 11 Watts, with the toolchain enabling in-flight battery-aware schedulability [31].…”