# UWB-Based Human-Following System with Obstacle and Crevasse Avoidance for Polar-Exploration Robots
## 1 Introduction
When operating human-following robots with this configuration, the operator must constantly monitor the robot's path to avoid collisions, requiring the operator to guide the robot around obstacles or navigate around corners in hallways. This unnatural path increases the cognitive load on the operator, which is particularly problematic in the harsh conditions of polar environments where the operator cannot afford to focus solely on the robot's movement. Furthermore, in glacial areas of the polar regions, even if the leading person navigates safely, sudden weight changes can cause the ground to collapse or reveal dangerous areas like crevasses, posing unexpected risks to the robot (see Figure 1). Therefore, it is essential for the robot to have its own obstacle-avoidance capabilities while following the user. Therefore, efforts have been made to develop techniques that allow robots to follow humans while avoiding collisions with obstacles. In [18], an algorithm was proposed for recognizing and tracking humans through laser sensors while avoiding collisions in environments such as corridors. However, this approach is designed for structured indoor environments with predefined maps that include orthogonal corridors. In contrast, the  
Figure 1: The crevasse fall-risk situation of the rear trailer following the movement of the leading trailer [17]. unstructured environments encountered by polar robots, characterized by unknown and irregular obstacles without orthogonal corridors or predefined maps, render this approach impractical. In [12], a method was proposed for human tracking while avoiding obstacles by creating a force field between the robot, the human, and known obstacles using their global positions. Utilizing global positions facilitates easier representation of the locations of obstacles and humans, as well as simpler algorithm implementation. Nevertheless, in outdoor environments, sudden obstacles may appear whose global positions and shapes are not known. Additionally, when numerous point cloud data acquired through environmental sensing sensors such as LiDAR are applied, the magnitude of the reactive forces can increase, potentially leading to unnatural robot movements. The approach has also not been validated for the application of large-scale sensor data in complex real-world environments. Accordingly, this paper proposes an explorer-following system with obstacle-avoidance capabilities to facilitate the collaboration between exploration robots and explorers operating in harsh polar weather conditions. The target exploration robot, KAREX (Korea Antarctic Robot Explorer) [8; 19; 20; 21], is equipped with UWB anchors, allowing it to detect the explorer carrying a UWB tag and determine their relative position. Based on the acquired relative position of the explorer, the system derives a target position and generates real-time local paths to move toward the target position. In the UWB anchor-tag setup, each anchor provides distance information to the tag carried by the explorer. This distance information includes signal noise, which is refined using a low-pass filter. After filtering, triangulation is employed to estimate the relative position of the tag with respect to the robot. Based on the estimated relative position of the tag, the target position is determined to maintain the desired distance between the robot and the explorer. KAREX utilizes multiple mounted LiDAR sensors to detect obstacles from the surrounding environment and mark them on a local obstacle map. After obstacle marking, path planning aims to follow the target position. For the human-following functionality, the existing local obstacle map-building method [22] is adapted to suit this purpose. When employing a human-following function, the robot navigates through areas that the human has traversed without collisions, allowing the assumption that no enclosed areas exist nearby. This assumption is applied to the local obstacle map.