Papers
114
Total Citations
5,587
H-Index
39
About
Kostas Alexis is a leading robotics researcher whose work sits at the intersection of autonomous navigation, aerial robotics, and intelligent path planning. Best known for pioneering receding horizon "next-best-view" exploration algorithms, his 2016 paper on the topic has garnered over 600 citations and fundamentally shaped how robots autonomously map unknown environments. His contributions span structural inspection path planning, uncertainty-aware exploration, and model predictive control for unmanned aerial vehicles, reflecting a career dedicated to making robots smarter, safer, and more capable in real-world deployments. Alexis has made particularly significant strides in subterranean robotics, co-leading Team CERBERUS to victory in the prestigious DARPA Subterranean Challenge — a landmark achievement recognized through a widely cited 2022 paper with 225 citations. His graph-based exploration planning methods for underground environments, developed alongside aerial and legged robotic systems, address some of the most demanding challenges in autonomous navigation. His survey on SLAM in extreme environments further cements his role as a thought leader in the field. With over 2,400 combined citations across his top works, Alexis represents one of the most impactful voices in modern autonomous robotics research.
Research Focus
Key Achievements
Top Papers
- 1Receding Horizon "Next-Best-View" Planner for 3D Exploration601 citations · 2016
- 2Receding horizon path planning for 3D exploration and surface inspection257 citations · 2016
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- 4CERBERUS in the DARPA Subterranean Challenge225 citations · 2022
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- 7Uncertainty-aware receding horizon exploration and mapping using aerial robots183 citations · 2017
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- 10Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge176 citations · 2023