| The human-following task is a fundamental function in human-robot collaboration. It requires a robot to recognize and locate a target person, plan a path, and avoid obstacles. To enhance the applicability of the human-following task in various scenarios, it should not rely on a prior map. This paper introduces a human-following method that meets these requirements. The human-following task comprises two main components: the target person identification and localization, and the navigation task. For the identification and localization of the target person, this paper proposes an approach that integrates data from a camera, a LiDAR, and a UWB anchor. For path planning and obstacle avoidance, a modified TEB algorithm is introduced. Experimental results demonstrate that the proposed target person identification and localization method can recognize the target person among multiple individuals, determine whether the target person is lost, and publish the target person’s position at a frequency of 20Hz. Compared to the UWB-only method, where only UWB is used to locate the target person, the proposed method in this paper reduces the localization error by 50.11%. Compared to the method proposed by Fu Yong, the effective localization data increased by 3.65%. The modified TEB algorithm does not rely on a prior map. And it can effectively handle various challenging conditions. Such as crowded environments, multiple obstacles, the target person being occluded, and the target person moving out of the robot’s field of view. The complete experimental videos are available for viewing on https://www.bilibili.com/video/BV1eN4y1z7Uq/. |