Autonomous Social Navigation Sample Clauses

Autonomous Social Navigation. This module is in charge of providing to the system the capabilities of self-localization and autonomous navigation dealing with dynamic and static obstacles around in a social fashion. An accurate and robust localization system is fundamental in order to navigate in the proposed scenarios. The localization module is based on a Xxxxx-Xxxxx algorithm (particle filter) [4,5] that uses maps created offline with a Simultaneous Localization and Mapping (SLAM) approach, and has been present in the system since the first version. The main inputs for the localization and mapping algorithms are the Xsens sensor, used to estimate angular velocities and linear accelerations, the wheel encoders from the Giraff platform, and the laser rangefinders. The module also implements the different navigation macro-actions required by the Behavior Manager through ROS actionlib interfaces. In the version 3.0, a new high-level macro-action of “walk side-by-side” has been added as well as the behavior of yielding to people in narrow places as doorways. ● Navigate to a waypoint. The robot is able to go to an specified goal point dealing with dynamic and static obstacles around in a social fashion. Once the navigation system receives a waypoint, a global path planner calculates a path from the current robot position to the goal based on the map of the area. In particular, an Optimal-RRT planner [6] is employed to calculate the path. This planner builds a path by means of a cost function which is obtained from a social cost library (see Social Cost Function Library subsection). The social cost library calculates the cost as a linear combination of navigation features, that take into account the people in the vicinity of the robot, and a set of weights which have been learnt from data. This allows the planner to obtain a social path to the goal. ● Navigate to interaction target. The navigation to an interaction target requires that the robot approaches the person in a social fashion, taking into acount the orientation of the person. A model of the task in feature space has been learned from demonstrations and encoded as a Gaussian Mixture Model (GMM) [7,8]. Then, this GMM is employed to bias the sampling of the Optimal-RRT planner [6]. ● Wait. In the cases that the robot is blocked because it is not able to find a path, the system will simply wait for a certain time before trying again to find a path. ● Walk side-by-side. Another aspect considered in the Social Navigation module is the...
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