LATIDIA · Robótica

Control de optimización de la política proximal del actor crítico de la red neuronal de punta para la navegación autónoma de UAV a través de aberturas restringidas en infraestructuras y edificios civiles

arXiv:2609.23643v1 Anuncio Tipo: nuevo Resumen: La navegación autónoma de vehículos aéreos no tripulados en entornos tridimensionales restringidos ha sido un desafío en el dominio de la robótica. La aplicación de la navegación autónoma u

La noticia

arXiv:2609.23643v1 Announce Type: new Abstract: Autonomous navigation of unmanned aerial vehicles in constrained three-dimensional environments has been a challenge in the robotics domain. The application of autonomous unmanned aerial vehicles in civil infrastructure inspection involves the use of such vehicles in bridge inspection, tunnel inspection, and structural inspection. The use of deep reinforcement learning in the autonomous navigation of unmanned aerial vehicles has been successful in constrained environments. However, the computational cost of the algorithm limits the application of the algorithm in the autonomous navigation of unmanned aerial vehicles. This paper proposes the use of the spiking neural network-based Proximal Policy Optimization algorithm in the autonomous navigation of unmanned aerial