KEYWORDS: LIDAR, Sensors, Cameras, Environmental sensing, Robotics, Monte Carlo methods, Mobile robots, 3D metrology, Tunable filters, Stereoscopic cameras
This paper presents the implementation of localization algorithms for indoor autonomous mobile robots in known environments. The proposed implementation employs two sensors, an RGB-D camera and a 2D LiDAR to detect the environment and map an occupancy grid that allows the robot to perform autonomous/remote navigation throughout the environment while localizing itself. The implementation uses the data retrieved from the perception sensors and odometry to estimate the position of the robot through the Monte Carlo Localization algorithm. The proposed implementation employs the Robot Operating System (ROS) framework on an NVIDIA Jetson TX2 and the Turtlebot 2. Experimental results were considered using a physical implementation of the mobile robot in an indoor environment.
The present paper explores the implementation of the RRT* path planning algorithm aided with a depth sensor in a physical robot for path planning and re-planning in a partially-known or unknown environment, the robot is capable of omnidirectional motion and aims to move from a starting location to a goal location in different environments. The proposed algorithm allows the robot to move through a map while avoiding collision by detecting unknown obstacles and updating the map for further planning and motion if required. The implementation and experimental results are presented for indoor environments with partial or non-knowledge of the environment in order to achieve autonomous navigation for a holonomic drive robot in an unknown environment using a depth camera as an optical sensing device.
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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