This paper presents the implementation of a mobile service robot with a manipulator and a navigation stack to interact and move through an environment providing a delivery type service. The implementation uses a LiDAR sensor and an RGB-D camera to navigate and detect objects that can be picked up by the manipulator and delivered to a target location. The robot navigation stack includes mapping, localization, obstacle avoidance, and trajectory planning for robust autonomous navigation across an office environment. The manipulator uses the RGB-D camera to recognize specific objects that can be picked up. Experimental results are presented to validate the implementation and robustness.
This paper presents the implementation of mapping, localization, and navigation algorithms for a mobile service robot in an unknown environment. The implementation uses a 3D LiDAR sensor to detect the environment and map an occupancy grid that allows global localization and navigation through the environment. The robot estimates the current position through the Monte Carlo localization algorithm with LiDAR sensor and odometry data. The navigation stack uses inflation to determine if the service robot can safely navigate through the environment, and avoid obstacles. Experimental results were considered using a simulated robot in an indoor environment without given prior knowledge of obstacles presented in the environment.
This paper presents a proposed algorithm with the implementation of the A* algorithm for path planning in a partially known environment. By using a differential mobile robot, the navigation is accomplished with a LiDAR sensor that detects any potential changes in the environment. The proposed algorithm estimates a safety path-planning trajectory from the origin of the robot to a target coordinate given by the user. If the robot encounters an unknown obstacle that does not belong to the known environment it will update the map, and recalculate the trajectory, executing it and proceed with the new path. Experimental results were considered in an indoors cluttered environment given by unknown obstacles, and partially known maps.
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