Cristino de Souza — accepted AI-conference papers

2025

Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial Systems

Luca Morando, Sanket A. Salunkhe, Nishanth Bobbili, Jeffrey Mao, Luca Masci, Cristino de Souza +2

ICRA 2025

Efficient real-time trajectory planning and control for fixed-wing unmanned aerial vehicles is challenging due to their non-holonomic nature, complex dynamics, and the additional uncertainties introduced by unknown aerodynamic effects. In this paper, we present a fast and efficient real-time traject

Cited by 1 Source ↗Scholar ↗

2021

Decentralized Multi-Agent Pursuit Using Deep Reinforcement Learning

Cristino de Souza, Rhys Newbury, Akansel Cosgun, Pedro Castillo, Boris Vidolov, Dana Kulic

RA-L 2021

Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omnidirectional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given

Cited by 136 Source ↗Scholar ↗