Browsing by Author "Kurt, Huseyin Burak"
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Item Open Access An investigation of control allocation methods for the ADMIRE simulation model(AIAA, 2023-06-08) Gulcan, Nilsu; Kurt, Huseyin Burak; Karaman, Atakan; Millidere, MuratThis paper presents a comparative study of various control allocation methods, using ADMIRE as a benchmark simulation model. The Ganged Pseudo-Inverse, Weighted Pseudo-Inverse, Cascaded Generalized-Inverse, Daisy Chain, and Linear Programming approaches are evaluated and compared against each other using open loop and closed loop analysis with Euclidean-Norm. In open-loop analysis, control allocation methods are analyzed for each approach that can produce an admissible solution and be able to attain commanded moments. Then, in closed-loop analysis, control allocation methods are compared using ADMIRE nonlinear simulation model for predefined maneuvers which are defined by multiple points in the flight envelope.Item Open Access Real-time motion planning for quadcopter using adaptive spherical expansion and sequential convex optimization(AIAA, 2023-01-19) Karaman, Atakan; Kurt, Huseyin Burak; Millidere, Murat; Alam, MushfiqulThis paper introduces the Adaptive Spherical Expansion and Sequential Convex Programming (ASE-SCP) as a real-time motion planning algorithm. ASE-SCP algorithm is an improved version of the Spherical Expansion and Sequential Convex Programming (SE-SCP) algorithm in terms of computational speed. ASE-SCP is a hybrid real-time motion planning algorithm which combines the advantages of the Adaptive Spherical Expansion, such as approaching the neighborhood of the global optimal path, and the quick convergence ability of the Sequential Convex Programming. The ASE-SCP algorithm first finds a collision-free path using the adaptive spherical expansion approach. After finding a feasible path from the start point to the target point, the feasible path is re-optimized (tuned) using sequential convex optimization to find the suboptimal path. ASE-SCP Algorithm is applied to a quadcopter model to demonstrate its applicability.Item Open Access Vision-based autonomous UGV detection, tracking, and following for a UAV(AIAA, 2024-01-04) Amil, Fatma G.; Sen, Muhammet; Kurt, Huseyin Burak; Beycimen, Semih; Millidere, MuratThis study proposes a methodology for unmanned ground vehicle (UGV) navigation in off-road environments where GPS signals are not available. The Husky-A200 at Cranfield University, United Kingdom has been used as a UGV in this research project. Due to the limited field of vision of UGVs, a UAV-UGV collaboration approach was adopted. The methodology involves five steps. The first step is divided into three phases: The aerial images of UGV from UAV are generated in the first phase. In the second phase, the UGV is detected and tracked using computer vision techniques. In the third phase, the relative pose (position and heading) between the UAV and UGV is estimated continuously using visual data. In the second step, the UAV maintain a fixed location (position and heading) relative to the UGV. The third step involves capturing aerial images from the UAV‘s mounted camera and transmitting it to the ground station instantly to create a global traversability map that classifies terrain features based on their traversability. In the fourth step, additional sensors such as LiDAR, radar, and IMU are used to refine the global traversability map. In the final step, the UGV navigates automatically using the refined traversability map. This study will focus on the first two steps of the methodology, while subsequent studies will address the remaining steps.