Papers
7
Total Citations
63
H-Index
4
About
Stefan Edelkamp is a leading researcher in intelligent robotics, with a focus on multi-goal motion planning under real-world constraints. His work bridges the gap between high-level task planning and low-level motion control, enabling robots to operate efficiently in complex environments. Edelkamp’s key contributions include integrating temporal reasoning with sampling-based motion planning, allowing robots to meet time windows while navigating dynamic obstacles—a critical capability for logistics, inspection, and surveillance tasks. He has also pioneered energy-aware planning, using Monte Carlo search to optimize routes with recharging stations, and developed efficient inspection strategies through clustered traveling salesman tours. His research on prize-collecting motion planning and multi-robot task allocation with capacities and time windows has advanced the field of autonomous logistics. With over 60 citations across his most-cited works, Edelkamp’s impact is evident in the practical applicability of his algorithms. Notable achievements include his work on watchman routes for robot inspection and location-routing task-motion planning, which address real-world challenges like load management and collision-free navigation. For students and researchers, Edelkamp’s work offers a blueprint for designing robots that are not only intelligent but also resource-aware and deadline-driven.
Research Focus
Key Achievements
Top Papers
- 1
- 2Energy-Aware Multi-Goal Motion Planning Guided by Monte Carlo Search16 citations · 2020
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- 5Multi-robot Multi-goal Motion Planning with Time and Resources3 citations · 2019
- 6Watchman Routes for Robot Inspection2 citations · 2019
- 7