Pathfollowing algorithms comparison using SoftwareintheLoop simulations for UAVs Unmanned Aerial Vehicles (UAVs) are aircraft that can be autonomously operated and doesnxe2x80x99t need a pilot aboard. Consequently, the autopilot is one of the main parts of an UAV, being responsible for stabilising the aircraft during the flight, executing navigation tasks and sensing the environment. Several pathfollowing simulations for loiter paths are described in the literature, but most of them only consider the kinematics model of an aircraft. Therefore, this paper compares pathfollowing algorithms for loiter paths in a more realistic scenario, using SoftwareintheLoop simulations and considering the dynamic model of the aircraft in the flight simulator XPlane. The algorithms compared are: CarrotChasing, Pure Pursuit and LineofSight (PLOS) and NonLinear Guidance Law (NLGL). Lastly, a new algorithm (NLGL  ) is proposed with a modification from the original algorithm and its results show smaller errors and less effort than all pathfollowing algorithms.

Online Trajectory Generation of a MAV for Chasing a Moving Target in 3D Dense Environments This work deals with a moving target chasing mission of an aerial vehicle equipped with a vision sensor in a cluttered environment. In contrast to obstaclefree or sparse environments, the chaser should be able to handle collision and occlusion together with flight efficiency. In order to tackle these challenges in realtime, we introduce a metric for target visibility and propose a hierarchical chasing planner. In the first phase, we generate a sequence of waypoints and chasing corridors which ensure safety and optimize visibility. In the following phase, the corridors and waypoints are utilized as constraints and objective respectively in quadratic programming from which we complete a dynamically feasible trajectory for chasing. The proposed algorithm is tested in multiple dense environments. The simulator AutoChaser with full code implementation u0026 GUI can be found in https:github.comicslJeontraj_gen_vis and video is available at https:youtu.be2d3uDlYR_M.