Alex Steiger
Papers
1
Total Citations
2
H-Index
1
About
Alex Steiger is a rising researcher in algorithmic robotics and computational geometry, whose work focuses on the fundamental challenge of multi-agent motion planning in complex environments. Steiger’s most-cited paper, “Near-Optimal Min-Sum Motion Planning for Two Square Robots in a Polygonal Environment” (2024), addresses the problem of coordinating two axis-aligned unit squares translating within a polygonal environment with up to n vertices. The work introduces a near-optimal algorithm for minimizing the sum of path lengths from source to target placements for both robots, a notoriously difficult problem due to the combinatorial explosion of collision-free configurations. This contribution is significant for its theoretical guarantees and practical implications in warehouse automation and multi-robot coordination. With 2 citations in its first year, the paper has already attracted attention from researchers seeking efficient, provable solutions for multi-agent systems. Steiger’s work bridges the gap between abstract geometric theory and real-world robotic applications, offering a foundation for future advances in crowded-space navigation and cooperative motion planning.
Research Focus
Key Achievements
Top Papers
- 1