Papers
11
Total Citations
93
H-Index
6
About
Corrado Possieri is a leading researcher at the intersection of algebraic geometry, control theory, and robotics. His work focuses on developing rigorous mathematical frameworks for robot motion planning, multi-agent coordination, and collision avoidance. Possieri’s major contribution lies in using polynomial vector fields to design dynamical systems that guarantee a given affine variety—a set of points defined by polynomial equations—is both invariant and attractive. This approach, detailed in his highly cited 2014 paper (17 citations), provides provable safety and convergence for robotic path planning. He has extended these ideas to formation control and human-robot interaction, notably in his 2022 work on multi-agent systems (12 citations) and his 2020 paper on online path planning for autonomous mobile robots (AMRs) with human-obstacle avoidance (8 citations). Possieri also bridges theory and practice by integrating algebraic geometry with reinforcement learning, as seen in his 2021 publication (11 citations). His textbook *Algebraic Geometry for Robotics and Control Theory* (2021, 14 citations) serves as a key resource for students and researchers. With over 90 total citations, Possieri’s work is foundational for provably safe and efficient autonomous navigation in complex environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Algebraic Geometry for Robotics and Control Theory14 citations · 2021
- 3Path planning in formation and collision avoidance for multi-agent systems12 citations · 2022
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- 9Patrolling and collision avoidance beyond classical Navigation Functions4 citations · 2018
- 10A dynamical interval Newton method4 citations · 2020