Most of the existing path-following navigation algorithms cannot guarantee global convergence to desired paths or enable following self-intersected desired paths due to the existence of singular points where navigation algorithms return unreliable or even no solutions. One typical example arises in vector-field guided path-following (VF-PF) navigation algorithms. These algorithms are based on a vector field, and the singular points are exactly where the vector field diminishes. In this paper, we show that it is mathematically impossible for conventional VF-PF algorithms to achieve global convergence to desired paths that are self-intersected or even just simple closed (precisely, homeomorphic to the unit circle). Motivated by this new impossibility result, we propose a novel method to transform self-intersected or simple closed desired paths to non-self-intersected and unbounded (precisely, homeomorphic to the real line) counterparts in a higher-dimensional space. Corresponding to this new desired path, we construct a singularity-free guiding vector field on a higher-dimensional space. The integral curves of this new guiding vector field is thus exploited to enable global convergence to the higher-dimensional desired path, and therefore the projection of the integral curves on a lower-dimensional subspace converge to the physical (lower-dimensional) desired path. Rigorous theoretical analysis is carried out for the theoretical results using dynamical systems theory. In addition, we show both by theoretical analysis and numerical simulations that our proposed method is an extension combining conventional VF-PF algorithms and trajectory tracking algorithms. Finally, to show the practical value of our proposed approach for complex engineering systems, we conduct outdoor experiments with a fixed-wing airplane in windy environment to follow both 2D and 3D desired paths.
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）
Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on robots, guiding them along the shortest path where the agents are likely to succeed. Here we use …
This paper presents the development of a Simultaneous Localization and Mapping (SLAM) based Autonomous Navigation system. The motivation for this study was to find a solution for navigating interior spaces autonomously. Interior navigation is challenging as it can be forever evolving. Solving this issue is necessary for multitude of services, like cleaning, the health industry, and in manufacturing industries. The focus of this paper is the description of the SLAM-based software architecture developed for this proposed autonomous system. A potential application of this system, oriented to a sm…
Language and Sketching: An LLM-driven Interactive Multimodal Multitask Robot Navigation Framework
ShanghaiTech University（上海科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Shanghai for Science and Technology（上海理工大学）；The University of Manchester（曼彻斯特大学）；University College London（伦敦大学学院）
Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation
School of Information, Renmin University of China（中国人民大学信息学院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算先进创新中心）；Horizon Robotics
SwordRiding: A Unified Navigation Framework for Quadrotors in Unknown Complex Environments via Online Guiding Vector Fields
Pengcheng Laboratory, Shenzhen, Guangdong, China（鹏城实验室，深圳，广东，中国）；School of Automation, Beijing Institute of Technology, Beijing, China（自动化学院，北京理工大学，北京，中国）；School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程学院，哈尔滨工业大学）；National Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）；Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, Hong Kong, China（机械与自动化工程系，香港中文大学，香港，中国）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
A robot guide dog has compelling advantages over animal guide dogs for its cost-effectiveness, potential for mass production, and low maintenance burden. However, despite the long history of guide dog robot research, previous studies were conducted with little or no consideration of how the guide dog handler and the guide dog work as a team for navigation. To develop a robotic guiding system that is genuinely beneficial to blind or visually impaired individuals, we performed qualitative research, including interviews with guide dog handlers and trainers and first-hand blindfold walking experie…
NaviDiffusor: Cost-Guided Diffusion Model for Visual Navigation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
We propose coordinating guiding vector fields to achieve two tasks simultaneously with a team of robots: first, the guidance and navigation of multiple robots to possibly different paths or surfaces typically embedded in 2D or 3D; second, their motion coordination while tracking their prescribed paths or surfaces. The motion coordination is defined by desired parametric displacements between robots on the path or surface. Such a desired displacement is achieved by controlling the virtual coordinates, which correspond to the path or surface's parameters, between guiding vector fields. Rigorous …
RINGO: Real-time Navigation with a Guiding Trajectory for Aerial Manipulators in Unknown Environments
Institute of Robotics and Automatic Information System（机器人与自动信息系统研究所）；College of Artificial Intelligence（人工智能学院）；Tianjin Key Laboratory of Intelligent Robotics（天津市智能机器人重点实验室）；Nankai University（南开大学）；Institute of Intelligence Technology and Robotic Systems（智能技术与机器人系统研究所）；Shenzhen Research Institute of Nankai University（南开大学深圳研究院）
Memory-Maze: Scenario Driven Visual Language Navigation Benchmark for Guiding Blind People
Waseda University（早稻田大学）；Miraikan - The National Museum of Emerging Science and Innovation（Mirai Kan - 国家新兴科学与创新博物馆）；Carnegie Mellon University（卡内基梅隆大学）
Shanghai Jiao Tong University（上海交通大学）；Flight Automatic Control Research Institute（飞行自动控制研究所）
Inertial navigation and attitude initialization in polar areas become a hot topic in recent years in the navigation community, as the widely-used navigation mechanization of the local level frame encounters the inherent singularity when the latitude approaches 90 degrees. Great endeavors have been devoted to devising novel navigation mechanizations such as the grid or transversal frames. This paper highlights the fact that the common Earth-frame mechanization is sufficiently good to well handle the singularity problem in polar areas. Simulation results are reported to demonstrate the singulari…
Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation
Huazhong University of Science and Technology（华中科技大学）；Hunan University（湖南大学）；University of Groningen（格罗宁根大学）；Eindhoven University of Technology（埃因霍温理工大学）
Microsoft Research（微软研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use…
Decoupling Physical Speed from Path Parameterization in Singularity-Free Guiding Vector Fields
Hunan University（湖南大学）；Hunan Normal University（湖南师范大学）；National University of Defense Technology（国防科技大学）；Information Support Force Engineering University（信息支援部队工程大学）
Drones Guiding Drones: Cooperative Navigation of a Less-Equipped Micro Aerial Vehicle in Cluttered Environments
Multi-robot Systems Group, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（多机器人系统组，控制系，电气工程系，捷克技术大学）
In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to u…
Versatile Distributed Maneuvering with Generalized Formations using Guiding Vector Fields
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；College of Information Science and Engineering, Hunan Normal University（信息科学与工程学院，湖南师范大学）；School of Robotics, Hunan University（机器人学院，湖南大学）
University of Toronto（多伦多大学）；McGill University（麦吉尔大学）；University of South Carolina（南卡罗来纳大学）
We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an online procedure that robustly computes a retraction of its boundaries. In this article, we first present the online construction of a topological map and the implementation of a control law for guiding the robot to the nearest unexplored area, first presented in [1]. The proposed method operates by allowing the robot to localize itself on a partially constructed map, calculate a path to unexplored parts of the environment (frontiers), compute a r…
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Guiding robots, in the form of canes or cars, have recently been explored to assist blind and low vision (BLV) people. Such robots can provide full or partial autonomy when guiding. However, the pros and cons of different forms and autonomy for guiding robots remain unknown. We sought to fill this gap. We designed autonomy-switchable guiding robotic cane and car. We conducted a controlled lab-study (N=12) and a field study (N=9) on BLV. Results showed that full autonomy received better walking performance and subjective ratings in the controlled study, whereas participants used more partial au…
Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development of RL and automated planning has been largely isolated due to their different computational modalities. Focusing on improving RL agents' learning efficiency, we develop Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to avoid exploring less-relevant states. The action knowledge is used for gene…
Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is not only required to intelligently interact with humans, but also to navigate safely among crowds. Thus, especially highly dynamic environments pose a grand challenge due to the volatile behavior patterns and unpredictable movements of humans. In this paper, we propose a Deep-Reinforcement-Learning-b…
Guiding Vector Field Generation via Score-based Diffusion Model
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；WINDY Lab, Department of Artificial Intelligence, Westlake University（西湖大学人工智能系WINDY实验室）
We present a biologically inspired design for swarm foraging based on ant's pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs no infrastructure in place. Additionally, the system only requires one-hop communication over the robot network, we do not make any assumptions about the connectivity of the communication graph and the transmission of information and computation is scalable versus the number of agents. This is done by letting the agents i…
osmAG-LLM: Zero-Shot Open-Vocabulary Object Navigation via Semantic Maps and Large Language Models Reasoning
Key Laboratory of Intelligent Perception and Human-Machine Collaboration – ShanghaiTech University, Ministry of Education, China（智能感知与人机协作重点实验室——上海科技大学，教育部，中国）；University of Bonn（波恩大学）；Lamarr Institute for ML and AI（Lamarr 人工智能与机器学习研究所）
In this paper, we address the problem of autonomous multi-robot mapping, exploration and navigation in unknown, GPS-denied indoor or urban environments using a swarm of robots equipped with directional sensors with limited sensing capabilities and limited computational resources. The robots have no a priori knowledge of the environment and need to rapidly explore and construct a map in a distributed manner using existing landmarks, the presence of which can be detected using onboard senors, although little to no metric information (distance or bearing to the landmarks) is available. In order t…
The Hong Kong University of Science and Technology（香港科技大学）；Albert Ludwig University of Freiburg（阿尔伯特·路德维希 Freiburg 大学）
We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consu…
