Eike Rehder

Papers

Total Citations

H-Index

About

Eike Rehder’s research lies at the intersection of computer vision and robotics, with a focus on environment perception and cooperative motion planning. His early work on stair detection using stereo vision (32 citations) advanced how robots perceive and navigate complex, multi-level environments—a critical capability for autonomous systems operating in human-centric spaces. Rehder’s approach leveraged range data to reliably identify ascending stairs, directly improving robotic mobility in buildings. More recently, he has tackled the challenge of cooperative motion planning for non-holonomic agents. By extending Value Iteration Networks (VINs) to handle multiple, interconnected robots with kinematic constraints, his 2017 work (4 citations) introduced a neural network-based framework for solving cooperative planning tasks. This contribution is particularly notable for bridging deep reinforcement learning and multi-robot coordination, offering a scalable path toward safer, more efficient autonomous navigation. Though his citation counts are modest, Rehder’s work demonstrates a clear trajectory: from foundational perception algorithms to sophisticated, learning-based planning—each step pushing robots closer to seamless operation in real-world, dynamic environments.