Charles Coquet
Papers
4
Total Citations
29
H-Index
3
About
Charles Coquet is a roboticist whose research lies at the intersection of bio-inspired vision, swarm intelligence, and minimalistic aerial navigation. His work addresses fundamental challenges in autonomous drone flight, particularly in GPS-denied environments where weight and computational constraints are severe. Coquet’s most impactful contribution is the development of novel in-flight odometry methods that rely solely on optic flow sensors—a vision-based technique inspired by insect navigation. His 2023 paper on indoor and outdoor odometry using oscillatory trajectories (12 citations) demonstrates how drones can estimate distance traveled with minimal hardware, a breakthrough for lightweight UAV applications. In parallel, Coquet has advanced swarm robotics through the Local Charged Particle Swarm Optimization (LCPSO) algorithm, which enables robotic teams to track dynamic targets under communication constraints. His 2021 paper on this topic (11 citations) elegantly combines flocking principles with particle swarm optimization, offering a scalable solution for environmental monitoring and search-and-rescue missions. By championing minimalistic, bio-inspired approaches, Coquet is pushing the boundaries of what small, resource-limited drones can achieve in real-world, GPS-deprived scenarios.
Research Focus
Key Achievements
Top Papers
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