Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Maximo, Marcos"'
Autor:
de Melo, Gabriel Adriano, Maximo, Marcos Ricardo Omena De Albuquerque, Soma, Nei Yoshihiro, de Castro, Paulo Andre Lima
The inner alignment problem, which asserts whether an arbitrary artificial intelligence (AI) model satisfices a non-trivial alignment function of its outputs given its inputs, is undecidable. This is rigorously proved by Rice's theorem, which is also
Externí odkaz:
http://arxiv.org/abs/2408.08995
This study proposes social navigation metrics for autonomous agents in air combat, aiming to facilitate their smooth integration into pilot formations. The absence of such metrics poses challenges to safety and effectiveness in mixed human-autonomous
Externí odkaz:
http://arxiv.org/abs/2405.00073
Autor:
Caregnato-Neto, Angelo, Siebert, Luciano Cavalcante, Zgonnikov, Arkady, Maximo, Marcos Ricardo Omena de Albuquerque, Afonso, Rubens Junqueira Magalhães
One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achieved in developing such models, as well as control techniques that addr
Externí odkaz:
http://arxiv.org/abs/2402.19128
Deep learning algorithms have driven expressive progress in many complex tasks. The loss function is a core component of deep learning techniques, guiding the learning process of neural networks. This paper contributes by introducing a consistency lo
Externí odkaz:
http://arxiv.org/abs/2401.10857
Autor:
Dantas, Joao P. A., Geraldo, Diego, Medeiros, Felipe L. L., Maximo, Marcos R. O. A., Yoneyama, Takashi
Surface-to-Air Missiles (SAMs) are crucial in modern air defense systems. A critical aspect of their effectiveness is the Engagement Zone (EZ), the spatial region within which a SAM can effectively engage and neutralize a target. Notably, the EZ is i
Externí odkaz:
http://arxiv.org/abs/2311.11905
Autor:
Dantas, Joao P. A., Geraldo, Diego, Costa, Andre N., Maximo, Marcos R. O. A., Yoneyama, Takashi
This work explores the use of military simulations in predicting and evaluating the outcomes of potential scenarios. It highlights the evolution of military simulations and the increased capabilities that have arisen due to the advancement of artific
Externí odkaz:
http://arxiv.org/abs/2309.08680
Autor:
Dantas, Joao P. A., Silva, Samara R., Gomes, Vitor C. F., Costa, Andre N., Samersla, Adrisson R., Geraldo, Diego, Maximo, Marcos R. O. A., Yoneyama, Takashi
AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platfo
Externí odkaz:
http://arxiv.org/abs/2310.00001
Autor:
Caregnato-Neto, Angelo, Maximo, Marcos Ricardo Omena de Albuquerque, Afonso, Rubens Junqueira Magalhães
This work addresses the problem of motion planning for a group of nonholonomic robots under Visible Light Communication (VLC) connectivity requirements. In particular, we consider an inspection task performed by a Robot Chain Control System (RCCS), w
Externí odkaz:
http://arxiv.org/abs/2306.15590
Estimating the camera's pose given images of a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and it often relies on geometric approaches that require considerable engine
Externí odkaz:
http://arxiv.org/abs/2305.06121
This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper presents an overview of building an agent representing a high-performance
Externí odkaz:
http://arxiv.org/abs/2304.09669