Improve Computing Efficiency and Motion Safety by Analyzing Environment With Graphics

作     者：Zhang, Qianyi Wu, Shichao Jia, Yuhang Xu, Yuang Liu, Jingtai 

作者机构：Nankai Univ Inst Robot & Automat Informat Syst Tianjin 300350 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING》 (IEEE Trans. Autom. Sci. Eng.)

年 卷 期：2024年第21卷第3期

页      面：4613-4626页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Key Research and Development Project [2019YFB1310604] National Natural Science Foundation of China 

主　　题：Trajectory Robots Planning Safety Dynamics Collision avoidance Robot sensing systems Motion planning computer graphics timed elastic band (TEB) homology class of trajectories 

摘      要：Exploring topologically distinctive trajectories provides more options for robot motion planning. Since computing time grows greatly with environment complexity, improving exploration efficiency and picking the optimal trajectory in complex environments are critical issues. To this end, this paper proposes a Graphic-and Timed-Elastic-Band-based approach (GraphicTEB) with spatial completeness and high computing efficiency. The environment is analyzed utilizing computer graphics, where obstacles are extracted as nodes and their relationships are built as edges. Three contributions are presented. 1) By assembling directed detours formed by nodes and segmented paths formed by edges, a generalized path consisting of nodes and edges derives various normal paths efficiently. 2) By multiplying two vectors starting from the obstacle point closest to the waypoint and the boundary point farthest from the waypoint, an novel obstacle gradient is introduced to guide safer optimization. 3) By assigning edges with asymmetric Gaussian model, a trajectory evaluation strategy is designed to reflect the motion tendency and motion uncertainty of dynamic obstacles. Qualitative and quantitative simulations demonstrate that the proposed GraphicTEB achieves spatial completeness, higher scene pass rate, and fastest computing efficiency. Experiments are implemented in long corridor and broad room scenarios, where the robot goes through gaps safely, finds trajectories quickly, and passes pedestrians politely Note to Practitioners-The motivation stems from the fact that our daily cruising robot occasionally gets trapped in a corridor with piled obstacles or in a complex dynamic crowd due to the lack of a reliable trajectory. The solution is to search for more topologically distinctive trajectories and pick the optimal one. Considering that existing open-source approaches are either incomplete or highly time-consuming, a method for clustering and searching trajectories in the obstacle-occupied r