In recent years, the application of multirotor drones has expanded across various industries, including aerial photography, surveillance, and environmental monitoring. However, the stability of these multirotor drones during flight is often compromised by environmental disturbances and inherent vibrations, which can degrade the quality of imaging systems. A critical component for mitigating these issues is the stabilized gimbal, which supports and stabilizes the camera payload. Despite advancements, existing gimbal systems for multirotor drones often lack versatility and structural stability, with many designs being tailored to specific drone models. This study focuses on optimizing a multirotor drone stabilized gimbal to enhance compatibility with a wide range of multirotor drones while improving vibration damping through structural refinements. By analyzing typical gimbal configurations and employing finite element simulations, I propose an optimized design that addresses limitations in damping ball arrangement and damping plate geometry. The use of composite materials and hyperelastic models for damping elements further enhances the realism of the simulations, ensuring that the multirotor drone gimbal meets rigorous performance standards.
The primary challenges in multirotor drone gimbal design include achieving universal compatibility and maintaining structural integrity under dynamic loads. Traditional gimbals for multirotor drones often feature damping balls distributed in circular patterns, which may not adequately isolate vibrations. Through ANSYS software simulations, I evaluated two typical structures: a circumferentially uniform distribution and a perimeter-based distribution. The results indicated that the uniform distribution offered superior deformation and stress characteristics, with a maximum deformation of 0.03651 mm and a maximum equivalent stress of 31.927 MPa, compared to 0.07949 mm and 36.275 MPa for the perimeter design. This analysis guided the optimization process, where I increased the number of damping balls and modified the damping plate shape to reduce edge stresses and improve load distribution. The optimized multirotor drone gimbal incorporates four corners with three damping ball holes each, an intermediate plate section removed to reduce mass, and eight aluminum alloy connecting columns for attachment to the multirotor drone body. This design aims to provide a robust solution for various multirotor drone applications, ensuring that the gimbal can withstand operational stresses while minimizing weight.
To model the damping plates, I selected carbon fiber epoxy resin composite materials due to their high strength-to-weight ratio and excellent thermal stability. The modeling process in ANSYS involved creating planar sketches, generating geometric surfaces, and applying mesh division with an average quality of 0.86. The layup sequence consisted of nine layers with alternating angles of -90°, 0°, and 90°, using a thickness of 0.167 mm per layer. The composite was defined with carbon fiber as the reinforcement and epoxy resin as the matrix, resulting in a lightweight yet durable structure. The damping balls, made of rubber with a Shore hardness of 40, were modeled using the Mooney-Rivlin hyperelastic model to accurately capture their behavior under deformation. The strain energy potential is given by:
$$ W = c_{10} (\bar{I}_1 – 3) + c_{01} (\bar{I}_2 – 3) + \frac{(J – 1)^2}{D_1} $$
where \( c_{10} = 0.05653 \), \( c_{01} = 0.01413 \), and \( D_1 = 0.01413 \). The connecting columns were constructed from aluminum alloy, chosen for its favorable mechanical properties and low density, which are essential for multirotor drone applications. The three-axis gyroscope assembly, comprising brushless motors, connection arms, tray shafts, and a camera plate, was modeled in SolidWorks, with its mass properties summarized in Table 1. The total mass of the gyroscope and camera was 0.831 kg, which was applied as a point mass in subsequent analyses to simulate real-world loading conditions on the multirotor drone gimbal.
| Component | Mass (g) |
|---|---|
| Brushless Motor | 176 |
| Yaw Axis Arm | 64 |
| Roll Axis Arm | 142 |
| Tray and Shaft | 45 |
| Lens Plate | 22 |
| Camera | 382 |
The assembly of the multirotor drone stabilized gimbal required careful coordination of the composite and metal components to avoid interference. The damping plates, damping balls, and connecting columns were integrated in ANSYS, with contact surfaces defined between the damping balls and plates, and symmetric contacts for the columns. Constraints included fixed supports on the upper ends of the connecting columns, and loads incorporated the gyroscope mass and gravitational acceleration of 9.80665 m/s². This setup ensured that the multirotor drone gimbal could be evaluated under realistic conditions, reflecting the stresses encountered during flight operations of multirotor drones.
Static analysis of the multirotor drone gimbal revealed a maximum deformation of 0.069316 mm at the interface between the brushless motor and the upper damping plate, indicating sufficient stiffness for multirotor drone applications. The equivalent stress distribution showed a peak value of 48.807 MPa on the upper plate, well below the tensile strength of carbon fiber composites, and 0.49978 MPa on the lower plate, which is negligible compared to aluminum alloy’s strength of 265 MPa. These results confirm that the optimized multirotor drone gimbal design meets strength and rigidity requirements, as summarized in Table 2. The mesh division, with 386,955 nodes and 247,158 elements, employed a multi-domain method for damping balls and a sweeping method for columns, ensuring accurate simulation of the multirotor drone gimbal’s response to loads.
| Parameter | Value |
|---|---|
| Maximum Deformation (mm) | 0.069316 |
| Maximum Equivalent Stress – Upper Plate (MPa) | 48.807 |
| Maximum Equivalent Stress – Lower Plate (MPa) | 0.49978 |
Modal analysis was conducted to assess the dynamic performance of the multirotor drone gimbal, particularly its resistance to resonance from motor-induced vibrations. The excitation frequency from the KV390 brushless motors, operating at 22.2 V with a rotational speed of 144.3 r/s, was calculated as 288.6 Hz using the formula for harmonic excitation. The natural frequencies of the multirotor drone gimbal were determined through prestressed modal analysis, with the first ten modes listed in Table 3. The results show that the excitation frequency lies between the 8th and 9th natural frequencies (181.87 Hz and 472.45 Hz), indicating no risk of resonance and affirming the gimbal’s suitability for multirotor drone use. This analysis underscores the importance of structural optimization in avoiding dynamic instabilities that could impair the performance of multirotor drones.
| Mode | Frequency (Hz) |
|---|---|
| 1 | 6.385 |
| 2 | 6.5792 |
| 3 | 86.86 |
| 4 | 158.18 |
| 5 | 180.55 |
| 6 | 180.67 |
| 7 | 180.69 |
| 8 | 181.87 |
| 9 | 472.45 |
| 10 | 473.29 |
In conclusion, the optimized multirotor drone stabilized gimbal design demonstrates significant improvements in versatility and stability through enhanced damping ball configuration and composite material usage. The static and modal analyses validate that the gimbal can withstand operational loads without excessive deformation or resonance, making it a reliable component for various multirotor drone models. Future work could explore advanced control algorithms and further material optimizations to extend the applicability of multirotor drone gimbals in challenging environments. This study highlights the critical role of integrated design and simulation in advancing multirotor drone technology, ensuring that gimbal systems contribute to clearer imaging and broader industrial adoption of multirotor drones.
The optimization process for the multirotor drone gimbal involved iterative simulations to refine the damping elements and structural geometry. By comparing the two typical gimbal structures, I identified that a uniform distribution of damping balls around the center provided better stress distribution and lower deformation. The optimized design incorporates 12 damping balls arranged in a symmetric pattern, which enhances vibration isolation for the multirotor drone. The damping plates were redesigned with cut-outs to reduce mass while maintaining rigidity, a crucial factor for the energy efficiency of multirotor drones. The use of carbon fiber composites not only reduces weight but also improves the durability of the gimbal, which is essential for the long-term reliability of multirotor drone systems. The Mooney-Rivlin model for the rubber damping balls allowed for a more accurate representation of their nonlinear behavior, ensuring that the simulations closely mimic real-world conditions. This approach is vital for predicting the performance of multirotor drone gimbals under various flight scenarios, such as windy conditions or rapid maneuvers.
Furthermore, the assembly and contact settings in ANSYS ensured that all components interacted correctly, with binding contacts between damping balls and plates and symmetric contacts for the columns. The load application included the entire mass of the three-axis gyroscope and camera, modeled as a point mass on the upper damping plate, along with the gravitational force. This comprehensive setup enabled a thorough evaluation of the multirotor drone gimbal’s structural integrity. The results from the static analysis confirm that the maximum stresses are within safe limits, and the modal analysis shows that the gimbal’s natural frequencies are sufficiently separated from the excitation frequencies of typical multirotor drone motors. This separation prevents resonance, which is critical for maintaining image stability in multirotor drone applications. The successful integration of these elements underscores the importance of a holistic design approach for multirotor drone gimbals, where material selection, geometric optimization, and dynamic analysis are combined to achieve optimal performance.
The implications of this optimized multirotor drone gimbal design extend beyond aerial photography to fields like agriculture, search and rescue, and infrastructure inspection, where stable imaging is paramount. By improving compatibility and stability, this gimbal can be adapted to a wide range of multirotor drones, reducing the need for custom solutions and lowering costs. The methodologies employed here, including finite element analysis and hyperelastic modeling, can serve as a blueprint for future developments in multirotor drone technology. As multirotor drones continue to evolve, the demand for robust and versatile gimbal systems will grow, making such optimizations increasingly relevant. This study contributes to that progress by providing a validated design that enhances the functionality and reliability of multirotor drones in diverse operational contexts.