# Control and Position Tracking for UAVs
## 3 Conclusions  
The articles published in this Special Issue of _Applied Science_ entitled \"Control and Position Tracking for UAVs\" provide a broad cross section of the issues that are crucial for real-life tasks for quadrotors, like firefighting, crop spraying, monitoring areas around vessels to secure them against collisions, the maintenance of offshore wind farms, creating an early-warning system based on distributed airborne radar, suspicious ground-vehicle tracking in intelligence tasks, and many others. All these topics and the relevant algorithms are presented by the authors. The diversity of algorithms presented in this Special Issue shows the range of meanings of the phrase \"control and position tracking\". Most of the articles presented numerical simulations, and real world experiments are needed. Considering real-world applications can be challenging due to the limitations of UAV technology, especially when addressing the accuracy of onboard measurement systems, data processing, and the calculation performance of onboard computers, as well as the robustness against external disturbances. Although UAV technology is continuously improving, most commercial drones still rely on standard PID loops as a well-known and easy-to-use control scheme. The gap between the research that provides control schemes that are more robust and effective in trajectory-tracking applications and the possibilities of ready-to-use UAVs should be filled with research including prototyping, implementation, and experimental verifications to make these control schemes the state of the art in reality. As stated in the introduction, there are many approaches to finding optimal paths in local and global horizons, with or without a map of the environment and with or without a model of UAV dynamics, whether deterministic or heuristic. Global path planning algorithms depend on the possession of detailed information of a map to explore all possible paths, and this implies requirements for measurement systems and the use of a SLAM technique. Being based on predefined maps significantly limits the usage of these algorithms in unknown areas. In turn, in most cases, local path planning algorithms require knowledge about both the robots and target dynamics models, and they are also sensitive to the accuracy of measurements and those models, external disturbances, and the costs of computations in real time. In conclusion, there is still a broad area in which to perform research on control and position tracking using unmanned aerial vehicles, especially if artificial intelligence and deep and machine learning become common and universal tools that will be able to eliminate the weaknesses of traditional approaches and make them more robust against insufficient knowledge, time-varying and unknown parameters, or unexpected flight scenarios. The author declares no conflicts of interest. List of Contributions  
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