Armando Segovia

About

Armando Segovia is a pioneer in mobile robotics, with a career focused on solving the fundamental challenges of autonomous navigation and sensor integration. His research centers on geometric approaches to multi-sensor data fusion, path planning, and kinematic control for mobile robots operating in unstructured environments. Segovia’s most influential work, “Fusion of multi-sensor data: a geometric approach” (2002, 30 citations), introduced a novel bounded-error estimation method that replaced traditional statistical techniques, enabling more robust robot localization in polygonal spaces. This contribution has been foundational for researchers seeking deterministic solutions to sensor fusion. Earlier, his comprehensive survey “Comparative study of the different methods of path generation for a mobile robot in a free environment” (1991, 23 citations) provided a critical taxonomy of path planning strategies, guiding subsequent work in the field. Segovia also advanced practical applications through the RoMo-SAPIENS project, developing methods to extract environmental cues from spot images for non-holonomic robot navigation. His work on kinematic design and control (1999) further demonstrates his commitment to bridging theory and real-world deployment. With a career spanning from foundational surveys to geometric fusion techniques, Segovia has left a lasting imprint on how mobile robots perceive, plan, and move.

Research Focus

Key Achievements

Top Papers

1. 1
   Fusion of multi-sensor data: a geometric approach

   30 citations · 2002
2. 2
   Comparative study of the different methods of path generation for a mobile robot in a free environment

   23 citations · 1991
3. 3
   Path Finding From A Spot Image For A Mobile Robot

   3 citations · 2005
4. 4
   Kinematic design and control of a mobile robot

   2 citations · 1999

Key Collaborators

Contact & Links