Carl Hildebrandt

University of Virginia

Papers

1

Total Citations

10

H-Index

1

About

Carl Hildebrandt’s research lies at the intersection of robotics, control systems, and safety-critical validation, with a focus on uncovering hidden vulnerabilities in autonomous mobile robots. His most-cited work, “Feasible and stressful trajectory generation for mobile robots” (2020, 10 citations), introduces a novel framework for generating trajectories that are both physically feasible and deliberately stressful—designed to expose faults that standard nominal testing would miss. This contribution is pivotal for the reliability of autonomous systems, as it addresses the challenge of exploring the vast input space of mobile robots to reveal failure modes under edge-case conditions. By bridging trajectory feasibility with adversarial stress testing, Hildebrandt’s work enhances the robustness of robot validation pipelines, making it a valuable resource for researchers and engineers in robotics and autonomous systems. His approach has implications for safety-critical applications, from warehouse automation to autonomous driving, and underscores the importance of rigorous, scenario-based testing in real-world deployments.

Research Focus

Key Achievements

1
H-Index
1
Papers
10
Total Citations
10
Avg Citations/Paper
🏆 Most Cited Paper
Feasible and stressful trajectory generation for mobile robots
10 citations · 2020
📈 Most Prolific Year: 2020 (1 Papers)
🤝 Key Collaborators: 3
🏛 Institutions: University of Virginia

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 10 days ago