Lightweight QuadPlane System

• Lightweight QuadPlane systems are hybrid UAV architectures combining fixed-wing efficiency and VTOL capability without mechanical transition mechanisms.
• Their design leverages carbon-fiber composites and optimized airframe geometry alongside a unified controller for seamless hover-to-cruise transitions and energy savings.
• Integrated perception and navigation modules enable autonomous missions in confined and GPS-denied environments, validated by wind tunnel and flight tests.

A lightweight QuadPlane system refers to a hybrid uncrewed aerial vehicle (UAV) architecture that integrates the efficient forward flight characteristics of fixed-wing aircraft with the vertical takeoff and landing (VTOL) versatility of multicopter platforms. These vehicles employ a rigid fixed wing combined with four electric rotors, typically in an X-configuration, and are optimized to minimize mass through the use of carbon-fiber spars, composite panels, and streamlined design. Lightweight QuadPlane systems are distinguished by their absence of mechanical transition mechanisms, robust response to wind and confined space operations, and unified control approaches that handle both hover and forward flight using a single, continuous controller. Characterization, modeling, and optimization studies show that these platforms can reduce structural weight, improve energy efficiency through cooperative control allocation, and offer scalable solutions for autonomous missions—including perception-based navigation and landing in GPS-denied environments (Quan et al., 2023, Rasul et al., 10 Dec 2025, Mathur et al., 2023).

1. Airframe Architecture and Structural Design

Lightweight QuadPlane airframes combine a compact fixed wing mounted at a calculated installation angle ($\kappa$), typically 30°–45° (e.g., $\kappa$ = 34°), and four symmetric rotor arms arrayed about the vehicle’s center of gravity. Wing geometry parameters (e.g., $b$ ≈ 0.94 m span, $c$ ≈ 0.17 m chord, $S=b\cdot c$ planform area) are chosen to favor low-speed cruise in confined airspace, with short spans and rigid composite structures. Rotor arms are spaced at equal distances ($dx$ ≈ 0.25 m fore–aft, $dy$ ≈ 0.2125 m lateral), with a tilt angle $\eta$ (≈10° outward for outboard, inward for inboard) to generate lateral thrust for yaw authority without sacrificing simplicity.

Table 1. Representative Airframe Mass Budget (from (Rasul et al., 10 Dec 2025))

Component	Mass (kg)	Notes
Wing (left/right)	0.84 ea	Carbon-fiber reinforced
Fuselage	1.45	Houses avionics, battery, CG
Battery (6S LiPo)	0.70	High energy density
Rotor arms (CF tube)	0.11	With mounts, connectors
Propellers (4x)	0.08	Low-inertia composite
Sensors (DNN/VIO suite)	0.38	Jetson Orin Nano + RealSense D435i
Avionics (FC, GPS, etc.)	0.30	Pixhawk, sensors
Total (Prototype II)	4.56	Complete configuration

The central fuselage locates heavy components at the CG and uses rigid monocoque panels. The absence of tilting wings or variable-pitch rotors eliminates transition mechanisms and further reduces both part count and overall mass, yielding improved manufacturability and resistance to aerodynamic and structural loads (Quan et al., 2023).

2. Aerodynamic and Dynamic Modeling

QuadPlane dynamics are modeled using multiple coordinate frames: an earth-fixed inertial frame, a body frame attached at the CG, a lifting-wing frame rotated by the wing installation angle, and a wind frame used for aerodynamic state estimation (angle of attack $\alpha$, sideslip $\beta$). Core state variables include position $\mathbf{p}\in\mathbb{R}^3$, velocity $\mathbf{v}\in\mathbb{R}^3$, attitude $R_l^e\in SO(3)$, and angular velocity $\omega_l\in\mathbb{R}^3$.

Forces and moments are decomposed as:

• Rotor thrust/moments: $T_i = K_f\omega_i^2$ and $M_i = K_m\omega_i^2 = (K_m/K_f)T_i$, mapped to body axes via $M_r(\eta)$.
• Aerodynamic forces: $L = 0.5\rho V_a^2 S C_L(\alpha)$, $D = 0.5\rho V_a^2 S C_D(\alpha)$, with coefficients $C_L$, $C_D$ parameterized from wind tunnel experiments (Mathur et al., 2023).
• Control surface effects: Longitudinal (elevator contribution), lateral (aileron), and yaw (rudder) modeled with appropriate influence coefficients.

Experimental characterization shows significant dynamic thrust reduction at forward airspeeds (e.g., only ~44% of static thrust at $V_a$ = 15 m/s), with rotor/wake interactions in hybrid mode introducing 20–40% increased drag and substantial unbalanced pitching moments (≈1.5 N·m at $V_a$ = 11 m/s) (Mathur et al., 2023).

Table 2. Aerodynamic Coefficient Fits ($V_a$ = 11 m/s, (Mathur et al., 2023))

Mode	$C_L(\alpha)$	$C_{D_P}(\alpha)$	$C_{D_Q}(\alpha)$
Plane	0.3118 + 0.11$\alpha$	0.3154 – 0.00133$\alpha$ + 0.001534$\alpha^2$	–
Quad	0.3329 + 0.0667$\alpha$	–	0.1538 – 0.00266$\alpha$
Hybrid	0.2622 + 0.0930$\alpha$	–	0.3519 – 0.01079$\alpha$

3. Unified Control Architecture

Control of a lightweight QuadPlane is accomplished using a unified full-flight controller capable of continuous transitions between hover and forward cruise. The control stack operates in a successive loop closure configuration:

• Outer loop: position controller (PID law) generates desired acceleration $u_d$.
• Inner loop: quaternion-based attitude controller computes target moment $m_d$, with axis–angle and coordinated turn feedforward for enhanced high-speed maneuvering.
• Control allocation: actuation distributed among rotor throttles and aileron deflections, mapped via $u_v = B\delta$ (over-actuated mixer form, $rank(B)=4$ for 6 actuators).

Energy optimization is included via a quadratic programming allocator,

$$\min_{\delta} J(\delta) = \|W_u(B\delta - u_{v,d})\|^2 + \gamma \|W_\delta(\delta - \delta_p)\|^2$$

with $W_u$ prioritizing force/moment tracking and $W_\delta$ penalizing energy use, especially favoring aileron control (less energy cost, motor savings up to 20–30% at 20 m/s cruise) (Quan et al., 2023).

4. Perception, Navigation, and Autonomous Landing

To enable fully autonomous operation, lightweight QuadPlanes are equipped with embedded edge-AI systems for real-time perception and visual-inertial navigation. Sensor suites typically include downward-facing RGB and RGB-D cameras, IMU, GNSS (for backup), and airspeed sensors, all rigidly mounted close to the CG. Onboard computing platforms (NVIDIA Jetson Orin Nano, 10–15 W) run TensorRT-accelerated neural detection (typically YOLOv5-nano backbone, $~2.5$ M parameters) and Visual SLAM pipelines.

Landing detection employs a multi-task loss combining IoU, categorical cross-entropy, and objectness scores. Synthetic and real datasets (CARLA UE5, 5 K samples; 500 real helipad images) are extensively augmented for domain robustness. Visual-inertial odometry (VIO) integrates sensor data to provide full 6-DoF pose, leveraging sliding-window graph optimization (GTSAM), with feature extraction (FAST, ORB), KLT tracking, and backend reprojection error minimization.

System latencies are measured at $\sim$33 ms per frame ($\sim$30 FPS), with GPU utilization $\sim$65% and end-to-end guidance response $<$85 ms. Precision and recall for landing pad detection are 0.92 and 0.88, respectively; attitude error is maintained at $\sigma < 1.5^\circ$ during flight tests (Rasul et al., 10 Dec 2025).

5. Wind Tunnel Characterization and Experimental Insights

Wind tunnel testing of the QuadPlane platform provides validated models for aerodynamic coefficients and thrust mapping under hybrid (rotor + wing) operation (Mathur et al., 2023). Tests delineate plane, quadrotor, and hybrid flight regimes, capturing net aerodynamic loads and moments across hover, transition, and cruise. Dynamic thrust is modeled via cubic fits to ESC command pulse width ($\nu$) at multiple propeller pitch angles and airspeeds.

Table 3. Dynamic Thrust Coefficients, Forward Thrust ($\alpha_p$ = $0^\circ$)

$V_a$ (m/s)	$a_0$	$a_1$	$a_2$ ($\times10^{-5}$)	$a_3$ ($\times10^{-8}$)
0	54.43	–0.123	8.63	–1.81
5	48.58	–0.107	7.34	–1.50
11	44.04	–0.0937	6.21	–1.24
15	44.61	–0.0909	5.73	–1.10

In hybrid mode, rotor-wake interference increases drag by 20–40% compared to plane mode, and load fluctuations are elevated (standard deviations up to 40% higher). At low throttle, forward propellers cannot overcome drag and windmills; at high cruise speeds, rotor-induced drag dominates over wing form drag.

Design recommendations: use finely streamlined, small-diameter booms to limit upstream disturbance; optimize forward-vertical thrust balance with dedicated propellers; align rotors with wing center-of-pressure; enhance elevator authority; streamline motor pods to lower drag coefficients ($C_{D_{P_f}}$ $\sim$0.30) (Mathur et al., 2023).

6. Energy Efficiency and Design Trade-offs

Absence of tilting/transitional mechanisms reduces weight and complexity, supporting endurance and stability. The unified controller architecture enables cooperative control, reallocating roll and yaw tasks between ailerons and rotors, which minimizes throttle excursions and dynamic losses ($P\propto \dot{\sigma}$). Empirical studies show low-power aileron usage in cruise achieves up to 20–30% motor power savings at 20 m/s (Quan et al., 2023).

A trade-off arises at low airspeeds or in hover: aileron control authority is negligible, necessitating increased rotor input for attitude maintenance. Wing installation angle $\kappa$ and rotor tilt $\eta$ are tuned to balance hover and cruise requirements. Wing area $S$ and aspect ratio are adjusted to accommodate low-speed maneuvering in confined domains versus maximizing overall endurance.

A plausible implication is that, for operational environments with variable wind or high drag (e.g., urban or cluttered domains), optimizing control allocation to minimize active rotor duty cycles is critical for extending mission duration while mitigating thermal and mechanical loading.

7. Performance Metrics, Robustness, and Operational Insights

Flight testing yields quantitative metrics for attitude stabilization ($<1.5^\circ$ RMS error), altitude hold (RMSE 0.12 m), localization drift (0.07 m/s), and total latency (pose+detect→command $\sim$85 ms) for perception-driven landing (Rasul et al., 10 Dec 2025). Robustness strategies include temporal consistency filtering for perception (three-frame majority vote), VIO fallback to optical-flow dead-reckoning in feature-poor settings, rigid sensor mounts with vibration damping, and power/thermal management to preserve real-time inference rates under load.

Documented failure modes: false helipad detection under shadows, VIO loss over low-texture terrain, and wing vibration. Mitigation steps (mechanical damping, sensor fusion, thermal throttling) are integrated into the design and control stack.

The modular synthesis of airframe, aerodynamic characterization, unified controller design, and embedded perception establishes lightweight QuadPlane systems as practical, scalable platforms for autonomous aerial tasks, particularly in domains requiring VTOL, confined-space operation, and energy-aware mission planning (Quan et al., 2023, Rasul et al., 10 Dec 2025, Mathur et al., 2023).