Automated configuration design framework for payload integration in unmanned aerial vehicles

• Published in: Engineering optimization Vol. 54; no. 12; pp. 2017 - 2033
• Main Authors: Raina, Ayush, Nakka, Sanket, Bansal, Mukul, Desai, K. A., Shah, S. V., Venkatesan, C.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.12.2022; Taylor & Francis Ltd
• Subjects: Automation; Configuration management; Cost function; Design automation; design optimization; extended pattern search algorithm (EPSA); Finite element method; Mathematical models; Optimization; Pattern search; Payload integration; Placement; Search algorithms; Stability; support vector machine (SVM); Support vector machines; UAV; Unmanned aerial vehicles
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2021.1971212

Unmanned aerial vehicles (UAVs) have became appreciably compact and lightweight over the years, imposing stricter geometric and stability constraints. The new-generation UAVs are required to carry numerous sensors and peripheral components for a variety of tasks. These components are placed in accordance with the requirements for use and stability conditions, which complicates the determination of an optimal layout configuration. This article presents the design automation framework for determining favourable sensor placement locations under specific stability constraints. The proposed methodology is a unique end-to-end framework that determines convenient locations for sensor placement, considering the finite element model of UAVs and payload-related parameters as inputs. The design automation routine proposes an integrated approach using the support vector machine and extended pattern search algorithms to identify the design space and relevant cost function. The efficacy of the proposed framework is examined by leveraging finite element solvers to optimize payload placements on a simulated UAV model.