Trajectory planning is crucial in multi-robot systems, particularly in environments with numerous obstacles. While extensive research has been conducted in this field, the challenge of coordinating multiple robots to flow collectively from one side of the map to the other-such as in crossing missions through obstacle-rich spaces-has received limited attention. This paper focuses on this directional traversal scenario by introducing a real-time scheduling scheme that enables multi-robot systems to move from edge to edge, emulating the smooth and efficient flow of water. Inspired by network flow optimization, our scheme decomposes the environment into a flow-based network structure, enabling the efficient allocation of robots to paths based on real-time congestion levels. The proposed scheduling planner operates on top of existing collision avoidance algorithms, aiming to minimize overall traversal time by balancing detours and waiting times. Simulation results demonstrate the effectiveness of the proposed scheme in achieving fast and coordinated traversal. Furthermore, real-world flight tests with ten drones validate its practical feasibility. This work contributes a flow-inspired, real-time scheduling planner tailored for directional multi-robot traversal in complex, obstacle-rich environments.
Code: https://github.com/chengji253/FlowPlanner
Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles
Jilin University（吉林大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Tongji University（同济大学）；NKIARI；Nankai University（南开大学）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE（教育部知识驱动人机智能工程研究中心）
Direct Bézier-Based Trajectory Planner for Improved Local Exploration of Unknown Environments
Center for Research on Complex Automated Systems (CASY)（复杂自动化系统研究中心（CASY））；Department of Electrical, Electronic and Information Engineering (DEI)（电气、电子与信息工程系（DEI））；University of Bologna（博洛尼亚大学）
HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting
Academy for Engineering & Technology, Fudan University（复旦大学工程与技术学院）；Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院 cyber系统与控制研究所）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
TaBSA -- A framework for training and benchmarking algorithms scheduling tasks for mobile robots working in dynamic environments
Warsaw University of Technology, Institute of Control；Institute of Robotics；Machine Intelligence, Pozna\'n University of Technology, Pl. Marii Sklodowskiej-Curie 5, PL 60-965 Pozna\'n, Poland
FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion
Hong Kong Baptist University（香港浸会大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；Hubei University（湖北大学）；Waytous Inc.（北京踏歌智行科技有限公司）；Indiana University-Purdue University Indianapolis (IUPUI)（印第安纳大学与普渡大学印第安纳波利斯联合分校）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Two Techniques That Enhance the Performance of Multi-robot Prioritized Path Planning
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（卢蒙巴大学））；Federal Research Center ``Computer Science and Control'' of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；National Research University Higher School of Economics（国立研究型大学高等经济学院）
