Papers
7
Total Citations
106
H-Index
5
About
Daniel Mox is a leading researcher at the intersection of robotics, multi-agent systems, and communication networks, whose work addresses one of the fundamental challenges in field robotics: enabling reliable coordination among robot teams in communication-denied environments. His major contributions center on developing data-driven and information-theoretic approaches to optimize network connectivity and communication for multi-robot systems. Mox’s most cited work, “ROS-NetSim” (44 citations), provides a critical simulation framework that bridges robotic and network simulators, enabling realistic testing of Perception-Action-Communication loops. His pioneering research on information-theoretic source seeking strategies for plume tracking in turbulent fields (22 citations) demonstrates how robots can leverage sporadic sensor data to locate biochemical contaminants—work with significant environmental monitoring and disaster response applications. Mox has also advanced the state of the art in learning connectivity-maximizing network configurations (17 citations), offering scalable solutions for real-time multi-agent coordination. His recent work on opportunistic communication and mobile infrastructure on demand (MID) represents a paradigm shift, where dedicated robot teams dynamically create wireless networks to support task-oriented robots. Through these contributions, Mox is shaping the future of resilient, communication-aware robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Learning Connectivity-Maximizing Network Configurations17 citations · 2022
- 4Learning Connectivity for Data Distribution in Robot Teams10 citations · 2021
- 5
- 6Information Based Exploration with Panoramas and Angle Occupancy Grids3 citations · 2018
- 7Opportunistic Communication in Robot Teams2 citations · 2024