Nishanth Bobbili

Mattia Secchiero, Nishanth Bobbili, Yang Zhou et al.

Autonomous identification and evaluation of safe landing zones are of paramount importance for ensuring the safety and effectiveness of aerial robots in the event of system failures, low battery, or the successful completion of specific tasks. In this paper, we present a novel approach for detection and assessment of potential landing sites for safe quadrotor landing. Our solution efficiently integrates 2D and 3D environmental information, eliminating the need for external aids such as GPS and computationally intensive elevation maps. The proposed pipeline combines semantic data derived from a Neural Network (NN), to extract environmental features, with geometric data obtained from a disparity map, to extract critical geometric attributes such as slope, flatness, and roughness. We define several cost metrics based on these attributes to evaluate safety, stability, and suitability of regions in the environments and identify the most suitable landing area. Our approach runs in real-time on quadrotors equipped with limited computational capabilities. Experimental results conducted in diverse environments demonstrate that the proposed method can effectively assess and identify suitable landing areas, enabling the safe and autonomous landing of a quadrotor.

Luca Morando, Nishanth Bobbili, Giuseppe Loianno

Gliding offers small fixed-wing UAVs extended endurance and silent operation but requires accurate energy management, especially under wind disturbances and obstacle constraints. Traditional Total Energy Control Systems based controllers regulate the trade between potential and kinetic energy reactively, often requiring fine-tuning and trim-conditions knowledge. In this work, we shift the regulation to the planning level and present a nonlinear, multi-cost trajectory planner for small UAV gliders. The method generates $\mathcal{C}^3$ continuous trajectories based on Bernstein polynomials, mapped into control commands through differential flatness, and re-planned online to match experimentally derived sink polar curves. A simulated netto variometer is integrated into the optimization to estimate air mass motion, constraining the glide to energy-balanced states. Consecutive gliding trajectories are linked by cruising segments computed through trajectories initialized on Dubins path-based waypoints, enabling hybrid missions that combine powered and unpowered flight. The approach is validated in CFD simulations and real-world experiments with a fixed-wing platform, showing reliable stabilization of sink rate, airspeed, and glide ratio under wind gusts and in presence of obstacles.