Kai Goebel

About

Kai Goebel is a leading researcher in autonomous systems, prognostics, and health management, with a focus on enabling intelligent decision-making for unmanned vehicles. His major contributions lie in integrating real-time system health predictions into mission planning, allowing autonomous platforms to adapt to faults and degradation. For instance, his work on recursive receding horizon planning for unmanned vehicles (45 citations) optimizes paths in nonuniform environments, while his research on battery state-of-charge prognostics (25 citations) enhances mission planning by considering system health. Goebel also developed a mobile robot testbed (18 citations) to validate prognostics-enabled autonomous decision-making, bridging theory and practice. His adaptive load-allocation framework (10 citations) addresses risk management by dynamically redistributing tasks to mitigate failures. With a career dedicated to improving system resilience, Goebel’s work has significant implications for aerospace, robotics, and energy systems, making him a pivotal figure in the field of intelligent autonomous operations.

Research Focus

Key Achievements

Top Papers

1. 1
   A Recursive Receding Horizon Planning for Unmanned Vehicles

   45 citations · 2014
2. 2
   Autonomous Vehicle Battery State-of-Charge Prognostics Enhanced Mission Planning

   25 citations · 2020
3. 3
   A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making

   18 citations · 2011
4. 4
   Adaptive Load-Allocation for Prognosis-Based Risk Management

   10 citations · 2011

Key Collaborators

Contact & Links