Eventos Anais de eventos
COBEM 2021
26th International Congress of Mechanical Engineering
Dimensional optimization of a passive gripper for UAV cargo transportation
Submission Author:
Rodrigo Cerqueira Campos , SC , Brazil
Co-Authors:
Rodrigo Cerqueira Campos, Estevan Hideki Murai, Henrique Simas
Presenter: Rodrigo Cerqueira Campos
doi://10.26678/ABCM.COBEM2021.COB2021-1869
Abstract
The use of unmanned aerial vehicles (UAVs) is increasingly popular among different applications, mainly due to their mechanical simplicity and relative low cost. Even though many obstacles still must be overcome, the employment of UAVs on logistics and transportation of packages has experienced a substantial growth over the last few years. The development of new navigation and control technologies turned the usage of UAVs a viable, cost-effective and sustainable delivering option. Although the technology itself already offers the advantage of speed, flexibility and ease in delivering goods, the interest in the development of gripping systems that allow picking up cargo, either remotely or autonomously, is also high. Recently, different designs for autonomous landing platforms with safe positioning and fixation have been proposed. This paper aims to carry out the dimensional optimization of a passive UAV gripping device. The mechanism relies on the weigth of the UAV as actuator, in order to securely grab the cargo, and springs that assists the opening motion. First, the kinematic modelling of the mechanism based on Natural Coordinates (NC) was performed. This model resulted in a non-linear system of equations, which was solved through the Newton-Raphson numerical method. The kinematic model was implemented in an algorithm developed in Python, and the results were validated through a CAD model. The optimization process must take into account the full range of motion of the mechanism. Hence, a succcessive displacement analysis is performed, in order to fully determine the motion of the device. Finally, the dimensional optimization is accomplished adopting the Genetic Algorithm (GA) process. The cost function is defined based on the desired path for the end-effector, while the penalties are set based on the allowable workspace. The GA algorithm was implemented in Python and the results are evaluated in comparison to a CAD model.
Keywords
UAV, gripper, natural coordinates, dimensional optimization, genetic algorithm
