Co-Design Optimisation of Morphing Topology and Control of Winged Drones (2309.13948v3)

Abstract: The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone's conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements, such as energy consumption and mission completion time. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and mission time, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.

• The paper introduces a co-design framework that optimizes morphing topology and control parameters using NSGA-II, achieving up to 74% energy savings.
• It employs a parametric multibody model and trajectory optimization to ensure agile maneuverability and reduced mission times.
• Results demonstrate that the morphing drone design outperforms fixed-wing benchmarks, offering promising applications in adaptive aerial robotics.

Co-Design Optimization of Morphing Topology and Control of Winged Drones

The paper "Co-Design Optimization of Morphing Topology and Control of Winged Drones" advances the field of drone technology by proposing a co-design methodology that synthesizes drone topology, actuation strategies, morphing tactics, and control parameters. The paper seeks to mitigate design complexities associated with morphing drones, particularly those requiring intricate coupling between topology and control, by offering a structured approach to optimize these interdependent components concurrently.

Methodology

The proposed methodology hinges on a multi-objective constraint-based optimization framework powered by NSGA-II, a genetic algorithm known for solving complex design problems involved in multi-criteria decision-making contexts. The method encapsulates the drone design in a parametric multibody model where aerodynamic forces play a critical role, allowing for the comprehensive optimization of both structural and control parameters for various flight missions.

Each prospective drone configuration is rigorously evaluated across a series of predefined scenarios using trajectory optimization to ensure adherence to mission-specific constraints and objectives, such as minimal energy consumption and reduced mission times. Emphasis is placed on the configuration’s ability to execute agile maneuvers through cluttered environments, thereby testing the drone's morphing capabilities and programmed trajectory optimization under diverse initial conditions and constraints.

Numerical Results and Observations

The paper provides detailed computational experiments comparing various design outcomes of morphing drones with a fixed-wing commercial platform, specifically the H-King Bixler3. In terms of energy efficiency and mission completion time, the morphing drones consistently outperformed the Bixler3. Notably, the co-designed drones exhibited a reduction in energy consumption by 37% to 74% and accomplished missions 22% to 33% faster.

The paper identified common characteristics among optimal drone configurations: lower aspect ratios and a combination of multi-DoF joint actuations, particularly in sweep and incidence. Higher thrust capacities and specific propeller configurations were also favored for agile designs. This insightful delineation of co-design parameters demonstrates potential pathways for developing more customizable and efficient drone applications based on mission profiles.

Theoretical and Practical Implications

Theoretically, this paper contributes to the understanding of co-design methodologies by integrating dynamic modeling and control system considerations with structural topology optimization. The parametric approach and its reliance on trajectory optimization signify a robust prototype for evaluating potential drone designs in realistic environments before physical prototyping.

Practically, the adoption of this co-design framework could catalyze the development of adaptive drones capable of operating effectively in varying environments and under fluctuating operational constraints. By emphasizing system-wide efficiency, the proposed method highlights pathways for future applications in sectors demanding high-energy efficiency and agile maneuverability, including but not limited to ecological monitoring, aerial reconnaissance, and urban logistics.

Future Directions

The paper outlines several avenues for future research. One critical area is the enhancement of aerodynamic modeling by addressing limitations at higher angles of attack and considering interactions among drone components. Furthermore, the integration of a dynamic simulator could provide deeper insights into disturbance rejection responses under varying wind conditions.

Expanding the design space to include additional components like tail configurations and battery selection or incorporating conventional control surfaces like ailerons and rudders would further augment the applicability of this co-design methodology, allowing for a fine-grained exploration of trade-offs between actuator count and control sophistication.

In conclusion, the work presented in this paper offers a significant contribution to the drone industry by proposing a comprehensive co-design framework that optimizes the multifaceted aspects of morphing drones, promising enhanced efficiency, and expanded operational capabilities in the evolving landscape of aerial robotics.