Potential Gap: Using Reactive Policies to Guarantee Safe Navigation
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）
This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the provable properties are lost when applied to more realistic models. We describe a set of algorithm modifications that correct for these errors and enhance robustness to non-ideal models. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect gaps and use them for the synthesis of the APF. Modifications are given for a nonholonomic robot model. Integration of the local planner, called potential gap, into a hierarchical navigation system provides the local goals and trajectories needed for collision-free navigation through unknown environments. Monte Carlo experiments in benchmark worlds confirm the asserted safety and robustness properties by testing under various robot models.
REASAN: Learning Reactive Safe Navigation for Legged Robots
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands（格罗宁根大学伯努利数学、计算机科学与人工智能研究所）；Department of Electrical Engineering, Linköping University, Sweden（利厄普大学电气工程系）；Engineering and Technology Institute Groningen, University of Groningen, The Netherlands（格罗宁根大学格罗宁根工程与技术研究所）
IntentReact: Guiding Reactive Object-Centric Navigation via Topological Intent
School of Information Science and Engineering, Hangzhou Normal University（杭州师范大学信息科学与工程学院）；State Key Laboratory of Industrial Control and Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；Neuromorphic Computing and Robotic Cognition Lab, Zhejiang University（浙江大学神经形态计算与机器人认知实验室）；Department of Automation, Zhejiang University of Technology（浙江工业大学自动化系）
A Collision-Free Sway Damping Model Predictive Controller for Safe and Reactive Forestry Crane Navigation
Automation & Control Institute (ACIN), TU Wien（自动化与控制研究所（ACIN），维也纳技术大学）；Center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH（视觉、自动化与控制中心，奥地利技术研究所）
Deadlock-Free Hybrid RL-MAPF Framework for Zero-Shot Multi-Robot Navigation
Department of Mechanical Engineering University of California Riverside CA USA（加州大学河滨分校机械工程系）；Department of Electrical and Systems Engineering Washington University in St. Louis MO USA（华盛顿大学圣路易斯分校电气与系统工程系）
Reactive and Safety-Aware Path Replanning for Collaborative Applications
Institute of Intelligent Industrial Technologies and Systems, National Research Council of Italy（意大利国家研究理事会智能工业技术与系统研究所）；Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano（米兰理工学院电子、信息与生物工程系）；Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University Munich（慕尼黑技术大学机器人与智能机器研究所）；Mohamed Bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, UAE（阿布扎克马斯达尔城人工智能大学）；Dipartimento di Ingegneria Meccanica e Industriale, University of Brescia（布雷西亚大学机械与工业工程系）
Autonomous bot with ML-based reactive navigation for indoor environment
Vellore Institute of Technology（韦洛尔理工学院）
本地或反应导航是必不可少的自主移动机器人在室内环境中运行。诸如SLAM、计算机视觉的技术需要显著的计算能力，这增加了成本。同样，使用基本的方法使机器人容易受到不一致的行为。本文的目的是开发一种机器人，平衡成本和精度，通过使用机器学习预测最佳避障移动的基础上，从四个超声波传感器的距离输入，战略上安装在前面，左前方，右前方，和后面的机器人。底层硬件由Arduino Uno和Raspberry Pi 3B组成。机器学习模型首先在机器人收集的数据上进行训练。然后，Arduino不断轮询传感器并计算距离值，并且在紧急需要避免的情况下，Arduino会进行适当的机动。在其他情况下，传感器数据使用USB连接发送到Raspberry Pi，机器学习模型生成导航的最佳移动，然后发送到Arduino以相应地驱动电机。该系统安装在2-WD机器人底盘上，并在杂乱的室内环境中进行测试，结果令人印象深刻。
Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics
Swiss Federal School of Technology in Lausanne - EPFL（洛桑联邦理工学院）；University of Tsukuba（筑波大学）；Swiss Federal School of Technology in Zurich - ETH Zurich（苏黎世联邦理工学院）
StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance
The Pennsylvania State University（宾夕法尼亚州立大学）；AlphaZ, Inc.（阿尔法兹公司）；Georgia Institute of Technology（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction
Mechanical Engineering and Mechanics Department at Lehigh University（莱维大学机械工程与力学系）；Department of Mechanical Engineering at University of California Riverside（加州大学河滨分校机械工程系）
Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments
The Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Baptist University（香港浸会大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；College of Information Science and Technology, Eastern Institute of Technology（东方理工学院信息科学与技术学院）
