Thomas Hanne
Papers
9
Total Citations
71
H-Index
6
About
Thomas Hanne is a researcher whose work sits at the intersection of swarm robotics, path planning, and simulation fidelity. His primary contributions focus on bridging the critical gap between simulated and real-world robot behavior—a challenge known as the "reality gap." In his highly cited 2022 paper on robotic path planning using Q-learning, Hanne demonstrated how reinforcement learning can outperform classical algorithms, earning 20 citations. His 2021 work on hybrid intelligent systems (10 citations) further showcases his commitment to integrating diverse computational approaches. Hanne’s research on Kilobot swarms is particularly notable; his 2018 and 2021 studies systematically compared real robot implementations with computer simulations, revealing how physics engines and algorithmic choices influence the reality gap. These papers, each garnering 8-9 citations, provide essential guidance for researchers seeking reliable simulation-to-reality transfer. Beyond swarm robotics, Hanne has explored novel backup path planning with ant colony optimization (2017, 7 citations) and even introduced emotion-influenced path planning (2017, 4 citations), where robots adjust behavior based on affective states. His work on optimizing multi-robot sumo fights using genetic algorithms (2019, 3 citations) demonstrates his versatility in applying evolutionary methods to competitive robotics. Through these contributions, Hanne has helped shape how roboticists validate simulations and design more robust, adaptable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Hybrid Intelligent Systems10 citations · 2021
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- 6A novel backup path planning approach with ACO7 citations · 2017
- 7Emotion Influenced Robotic Path Planning4 citations · 2017
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