Led the design and implementation of a fleet monitoring and maintenance system, integrating multiple cloud-based platforms to deliver dynamic dashboards, intelligent alerting, and automated software and configuration updates.

Redesigned the robot's software architecture by redistributing components across containers that group related tasks, enhancing development times, task distribution, dependency management, and debugging.

Enabled communication among nodes on multiple computers within the robot to distribute the computational load.

Led the migration of the robot software from ROS1 to ROS2, leveraging the latest community developments and DDS robustness and flexibility.

Established clear standards for all system components, including managed and composable nodes, behavior tree implementations, and auto-recovery logic.

Created the dependencies graph and status manager to properly handle crashing and auto-recovery of the system.

Integrated the new architecture with the company's cloud & MQTT based fleet manager to schedule and manage high-level and multi-robot tasks.

Created a development environment based on real-time synchronization, enabling coding, debugging, and logging across local and remote robots, simulators, and hosts.

Integrated multi-sensor fusion (LIDAR, RGBD cameras, proximity sensors) to provide 3D perception for the robot.

Created Nav2 stack C++ plugins to implement custom planners, controllers, and behavior trees nodes.

Integrated VSLAM algorithms and 3D-based navigation methods incorporating dynamic footprints.

Converted multiple Python ROS nodes to C++, significantly enhancing the performance of the entire system.

Implemented a hardware abstraction layers and ROS/ROS2 controllers for accessing motorized wheels, robotic arms, sensors, LEDs, battery information, and more.

Developed monitoring ROS/ROS2 nodes to continuously report the robot's status to cloud-based real-time dashboards.

Member of the Robotics Development Research Group (GED) of the Universidad Santo Tomás, leader of the Artificial Vision research line.

Courses imparted:

• Artificial Vision
• Introduction to Artificial Intelligence
• Machine Learning
• Operating Systems

Tutor of the following topics:

• Digital Systems
• Hardware Description Languages
• Programming