# UWB-Based Human-Following System with Obstacle and Crevasse Avoidance for Polar-Exploration Robots
## 1 Introduction  
This paper introduces an advanced UWB (Ultra Wide Band)-based human-following technology designed for polar-exploration robots, emphasizing its utility in collaborative efforts with human explorers. The proposed human-following algorithm incorporates collision-avoidance mechanisms to ensure the safety of both the explorers and the robots during their missions. Human-following technology alleviates the burden on workers collaborating with robots and enhances operational efficiency, making it one of the crucial services offered by many commercial robots [1; 2; 3]. This technology proves particularly useful when workers need to keep robots in close proximity while performing tasks such as loading/unloading and exploration. Such services are especially valuable in environments like warehouses, where robots must move continuously to locate items, or in military operations [4], where constant monitoring of the surroundings in unknown environments is required. Explorers conducting activities in polar environments face situations remarkably similar to those encountered by workers in logistics centers or soldiers performing reconnaissance with mobile robots. During polar exploration, robots can assist by transporting heavy cargo or by carrying various sensors and exploration equipment, thereby supporting the operation of exploration tools. Consequently, research on polar-exploration robots continues to advance [5; 6; 7; 8; 9]. In collaborative polar exploration with robots, explorers must attentively observe their surroundings and prepare for unforeseen hazards to ensure their safety and the success of the mission. Therefore, they cannot focus solely on operating the robots. Hence, human-following technology is essential for guaranteeing the safety of explorers and maintaining the continuity of exploration activities. Human-following technology is primarily divided into human detection and tracking. Various sensors such as LiDAR (Light Detection and Ranging), vision cameras, and RGB-D sensors have been employed to detect and locate humans. Methods using 2D LiDAR (laser scanners) to recognize human legs have been utilized for human detection [10; 11]. However, these methods can fail to detect the legs in complex environments with numerouscolumnar structures. The use of RGB-D cameras, which provide both RGB images and depth information, has improved human-detection performance [1; 12; 13; 14]. Nevertheless, due to the limited field of view (FoV) of RGB-D cameras, the detection range is narrow, which can result in the target moving out of the detection range when the robot's rotation speed is low. Additionally, even if the FoV issue is resolved, the use of RGB-D or similar imaging technologies faces challenges in outdoor environments with strong sunlight and adverse weather conditions, making it difficult to consistently obtain high-quality images. Therefore, this paper introduces the use of UWB devices for user following in polar-exploration robots, which must operate in harsh climatic conditions [15; 16]. UWB devices estimate the distance between devices using broadband communication signals and determine the operator's position through triangulation. The UWB-based human-detection method can recognize the user's location irrespective of weather conditions by utilizing radio waves. Additionally, unlike vision or LiDAR-based human-following systems, the radio wave-based approach allows for accurate position estimation even when the user is obscured by obstacles and is in a non-line-of-sight (NLoS) scenario. After recognizing the user, the robot moves in the direction of the target and performs control to maintain a safe distance. Typically, human-following robots implement a control system that adjusts the angular velocity to maintain line-of-sight (LoS) with the operator and adjusts the linear velocity to maintain distance from the operator, as in [1; 16]. Even though this method is easy to implement, it results in collisions with obstacles since the robot only relies on information about the operator.