Explore seamless navigation with LIMO's ROS-based framework, utilizing key packages like move_base and amcl for precise robot positioning and dynamic path planning. With advanced planners like DWA_planner and TEB_planner, LIMO ensures efficient navigation through diverse terrains, while intuitive commands simplify goal setting and real-time monitoring. Offering path inspection capabilities, LIMO enables users to record and refine navigation paths, setting new standards for robotics navigation efficiency and accuracy.
Two laser mapping methods were used above. Then use the map just built to navigate.
The key to navigation is robot positioning and path planning. For these, ROS provides the following two packages.
move_base：achieve the optimal path planning in robot navigation.amcl：achieve robot positioning in a two-dimensional map.On the basis of the above two packages, ROS provides a complete navigation framework.
The robot only needs to publish the necessary sensor information and navigation goal position, and ROS can complete the navigation function. In this framework, the move_base package provides the main operation and interactive interface of navigation. In order to ensure the accuracy of the navigation path, the robot also needs to accurately locate its own position. This part of the function is implemented by the amcl package.
Move_base packageMove_base is a package for path planning in ROS, which is mainly composed of the following two planners.
global_planner). Global path planning is to plan the overall path according to a given goal position and global map. In navigation, Dijkstra or A* algorithm is used for global path planning, and the optimal route from the robot to the goal position is calculated as the robot's global path.local_planner). In practice, robots often cannot strictly follow the global path. So it is necessary to plan the path that the robot should travel in each cycle according to the map information and obstacles that may appear near the robot at any time. So that it conforms to the global optimal path as much as possible.Amcl packageAutonomous positioning means that the robot can calculate its position on the map in any state. ROS provides developers with an adaptive (or kld sampling) Monte Carlo localization (amcl), which is a probabilistic positioning system that locates mobile robots in 2D. It implements an adaptive (or KLD-sampling) Monte Carlo localization, using particle filtering to track the pose of the robot on a known map.
The full name of DWA is Dynamic Window Approaches. The algorithm can search for multiple paths to avoid and travel, select the optimal path based on various evaluation criteria (whether it will hit an obstacle, the time required, etc.), and calculate the linear velocity and angular velocity during the driving cycle to avoid collisions with dynamic obstacles.
The full name of "TEB" is Time Elastic Band Local Planner. This method performs subsequent modifications to the initial trajectory generated by the global path planner to optimize the robot's motion trajectory. It falls under the category of local path planning. During the trajectory optimization process, the algorithm takes into account various optimization goals, which include but are not limited to minimizing overall path length, optimizing trajectory execution time, ensuring a safe distance from obstacles, passing through intermediate path points, and complying with the robot's dynamics, kinematics, and geometric constraints. The "TEB method" explicitly considers the dynamic constraints of time and space during the robot's motion. For instance, it considers limitations on the robot's velocity and acceleration.