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About

Thulio Amorim is a rising force in multi-robot systems and autonomous aerial robotics, with a focus on cooperative perception, coverage path planning, and swarm intelligence. His work addresses critical challenges in deploying multi-UAV systems for real-world exploration and surveillance. In his 2021 paper on multi-robot sensor fusion for target tracking, he tackled the persistent problem of occlusion by using constrained visual observations across multiple robots, earning 11 citations. His 2022 work on an improved spanning tree-based algorithm for large-area coverage introduced the IAWSTC method, enabling distributed path planning for UAVs in cluttered environments—a key contribution for autonomous exploration. More recently, his 2024 study on minimalistic 3D self-organized flocking demonstrated how UAV swarms can achieve cohesive motion without external directional cues, advancing desert exploration capabilities. Amorim’s research consistently bridges theory and practice, as seen in his vision-based K-nearest neighbor approach for search-and-landing tasks under energy constraints, developed within the RoboCup Brazil FRTL challenge. With a growing citation record and a clear trajectory toward scalable, resilient multi-robot systems, Amorim is shaping the future of autonomous aerial operations in complex, unstructured environments.