Manuel Boldrer
Papers
7
Total Citations
80
H-Index
5
About
Manuel Boldrer’s research lies at the intersection of multi-robot systems, social navigation, and control theory, with a focus on enabling robots to operate safely and intelligently in human-populated environments. His most cited work, “Socially-Aware Reactive Obstacle Avoidance Strategy Based on Limit Cycle” (30 citations), pioneers a hybrid approach that merges potential field methods with limit cycle dynamics, allowing mobile robots to navigate cluttered, dynamic spaces while respecting social norms. Building on this, his “Multi-agent navigation in human-shared environments” (17 citations) extends these principles to multi-robot teams, ensuring collision-free, socially-aware coordination. Boldrer has also made notable contributions to graph connectivity control, introducing a framework for mixed dynamic multi-task teams that maintain network links while executing diverse objectives (9 citations). His innovative use of time-inverted Kuramoto dynamics for persistent monitoring (9 citations) and target detection (9 citations) offers a distributed, scalable solution for robots patrolling path-like environments. With a total of over 80 citations across his key papers, Boldrer’s work is shaping the future of human-robot interaction, autonomous navigation, and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1Socially-Aware Reactive Obstacle Avoidance Strategy Based on Limit Cycle30 citations · 2020
- 2
- 3
- 4Multiagent Persistent Monitoring via Time-Inverted Kuramoto Dynamics9 citations · 2022
- 5
- 6
- 7Lloyd-based Approach for Robots Navigation in Human-shared environments3 citations · 2020