Papers
8
Total Citations
71
H-Index
5
About
Sebastian Mai is a leading researcher in swarm robotics and multi-agent pathfinding (MAPF), whose work bridges the gap between theoretical optimization and real-world robotic deployment. His most influential contribution, "On the Scalable Multi-Objective Multi-Agent Pathfinding Problem" (27 citations), addresses the critical challenge of finding optimal, collision-free paths for multiple agents in industrial and robotic applications, pushing beyond single-objective approaches to handle real-world trade-offs. Mai has also pioneered energy-aware mission planning for micro-UAVs, developing a generic component-based energy model (10 citations) that enables more reliable and adaptive drone operations. His research extends to swarm intelligence, where he introduced the Driving Swarm framework (8 citations) for reproducible multi-robot experiments and developed novel methods like Simultaneous Localisation and Optimisation (4 citations) that allow robots to search collectively without perfect position knowledge. Through multi-objective roadmap optimization and collective decision-making for conflict resolution, Mai has systematically advanced the scalability and practicality of autonomous multi-robot systems, making his work essential reading for researchers tackling the complexities of coordinated robotic navigation.
Research Focus
Key Achievements
Top Papers
- 1On the Scalable Multi-Objective Multi-Agent Pathfinding Problem27 citations · 2020
- 2
- 3
- 4Modeling Pathfinding for Swarm Robotics8 citations · 2020
- 5
- 6Multi-Objective Roadmap Optimization for Multiagent Navigation4 citations · 2022
- 7Simultaneous Localisation and Optimisation for Swarm Robotics4 citations · 2018
- 8