Hi, I'm Raymundo.

This paper presents a modular ROS 2 architecture for autonomous mapping in mobile robots operating within unknown and dynamic indoor environments. The proposed system integrates multimodal scan fusion, a static–dynamic obstacle-filtering module, SLAM-based map construction, frontier-based exploration, and Nav2-based navigation, coordinated by a program manager that governs the mission from distributed launch through coordinated termination. Beyond this supervised software system, the architecture is organized around a modular ROS 2 exploration-management framework that extends beyond frontier detection to govern candidate evaluation, navigation-goal supervision, failure recovery, and mission completion. Requiring only an occupancy-grid map, robot-pose information through TF, and a Nav2-compatible navigation interface, the framework may be adopted by compatible ROS 2 mobile-robot stacks through external configuration, without modification of its underlying implementation. The complete system is validated in simulation and on the physical CALL-M mobile manipulator.

This work presents a modular ROS 2 framework for separating static environmental structure from dynamic obstacles in LaserScan-based robotic mapping and navigation. The framework processes unified multimodal range observations to distinguish persistent structures from transient measurements, providing a static scan for SLAM-based map construction and a dynamic scan for real-time obstacle avoidance. In addition, dynamic observations may be tracked to estimate obstacle motion while preserving compatibility with conventional ROS 2 LaserScan interfaces. The approach is designed as a reusable preprocessing layer for mobile robots operating in dynamic indoor environments, improving map consistency while retaining moving obstacles for navigation and collision avoidance.

Autonomous door opening is a key capability for service robots. It requires integration of perception, navigation, localization, and manipulation. This paper presents a semi-autonomous door-opening pipeline for a mobile manipulator. The system requires manual initialization of the robot pose in RViz; it autonomously detects and approaches the door, estimates the 3D handle position, aligns the mobile base, performs the pre-grasp positioning, and executes handle manipulation and door opening. The system was evaluated on a real mobile manipulator in 20 trials: 10 nominal operation trials, 5 trials with varying initial robot-to-door distance, and 5 trials varying initial end-effector height. In nominal condition, door perception, navigation, 3D localization, alignment, and pre-grasp positioning succeeded 10/10, while complete door opening succeeded 8/10. The system successfully handled the initial robot-to-door distance in four of five trials. However, changes in the initial end-effector height reduced performance, completing only one of five trials. Failures occurred mainly in the contact and interaction between the handle and the gripper. Physical handle manipulation is the main limitation of the current system.