Autonomous navigation is a long-standing field of robotics research, which provides an essential capability for mobile robots to execute a series of tasks on the same environments performed by human everyday. In this chapter, we present a set of algorithms to train and deploy deep networks for autonomous navigation of mobile robots using the Robot Operation System (ROS). We describe three main steps to tackle this problem: i) collecting data in simulation environments using ROS and Gazebo; ii) designing deep network for autonomous navigation, and iii) deploying the learned policy on mobile robots in both simulation and real-world. Theoretically, we present deep learning architectures for robust navigation in normal environments (e.g., man-made houses, roads) and complex environments (e.g., collapsed cities, or natural caves). We further show that the use of visual modalities such as RGB, Lidar, and point cloud is essential to improve the autonomy of mobile robots. Our project website and demonstration video can be found at https://sites.google.com/site/autonomousnavigationros.
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sony AI（索尼人工智能）
This paper presents a self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-specific in-situ system adjustment, e.g. from human experts, or may repeat their mistakes regardless of how many times they have navigated in the same environment. Having the potential to improve with experience, learning-based navigation is highly dependent on access to training resources, e.g. sufficient memory and fast computation, and is prone to forgetting previously learned capability, especially when facing diff…
Autonomous navigation in dynamic environment heavily depends on the environment and its topology. Prior knowledge of the environment is not usually accurate as the environment keeps evolving in time. Since robot is continuously evaluating the environment as it proceeds, deciding the optimal way to traverse the environment to get to the goal, computationally efficient yet mathematically adaptive navigation algorithms are needed. In this paper, a navigation scheme for mobile robot, capable of dealing with time variant environment is proposed. This approach consists of a global planner (A*) and l…
Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions that significantly differ from the way humans move in tight and dynamic spaces, as we do not refrain interacting with the environment around us when necessary. Inspired by this observation, we propose a framework for autonomous robot navigation among movable obstacles (NAMO) that is based on the theory of affordances and contact-implicit motion planning. We consider a realistic scenario in which a mobile service robot …
Mobile robot navigation in total or partially unknown environments is still an open problem. The path planning algorithms lack completeness and/or performance. Thus, there is the need for complete (i.e., the algorithm determines in finite time either a solution or correctly reports that there is none) and performance (i.e., with low computational complexity) oriented algorithms which need to perform efficiently in real scenarios. In this paper we evaluate the efficiency of two versions of the A star algorithm for mobile robot navigation inside indoor environments with the help of two software …
MRReP: Mixed Reality-based Hand-drawn Reference Path Editing Interface for Mobile Robot Navigation
Graduate School of Information Science and Technology, The University of Osaka（大阪大学信息科学与技术研究生院）；D3 Center, The University of Osaka（大阪大学D3中心）；Graduate School of Maritime Sciences, Kobe University（神户大学海事科学研究生院）
MRHaD: Mixed Reality-based Hand-Drawn Map Editing Interface for Mobile Robot Navigation
Graduate School of Information Science and Technology, The University of Osaka（信息科学与技术研究生院，大阪大学）；D3 Center, The University of Osaka（大阪大学D3中心）；Graduate School of Maritime Sciences, Kobe University（海洋科学研究生院， Kobe大学）
The Chinese University of Hong Kong（香港中文大学）；The University of British Columbia（不列颠哥伦比亚大学）；National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）
Robots are increasingly operating in indoor environments designed for and shared with people. However, robots working safely and autonomously in uneven and unstructured environments still face great challenges. Many modern indoor environments are designed with wheelchair accessibility in mind. This presents an opportunity for wheeled robots to navigate through sloped areas while avoiding staircases. In this paper, we present an integrated software and hardware system for autonomous mobile robot navigation in uneven and unstructured indoor environments. This modular and reusable software framew…
Murdoch University（莫道克大学）；Deakin University（迪肯大学）；Jiangxi University of Science and Technology（江西理工大学）
A two-wheeled mobile robot, namely Kian-I, is designed and prototyped in this research. The Kian-I is comparable with Khepera-IV in terms of dimensional specifications, mounted sensors, and performance capabilities and can be used for educational purposes and cost-effective experimental tests. A motion control architecture is designed for Kian-I in this study to facilitate accurate navigation for the robot in an immersive environment. The implemented control structure consists of two main components of the path recommender system and trajectory tracking controller. Given partial knowledge abou…
We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinfo…
Co-training with Ego-centric Video and Demonstration for Robot Navigation Task
Department of Informatics, Graduate School of Informatics, Kyoto University（信息学系，京都大学研究生院）；Spatial Robotics Research Center, Fujitsu Limited（空间机器人研究中心，富士通有限公司）
Division of Science and Engineering, Penn State Abington（宾州州立大学阿宾顿分校科学与工程学院）
This paper investigates different methods to detect obstacles ahead of a robot using a camera in the robot, an aerial camera, and an ultrasound sensor. We also explored various efficient path finding methods for the robot to navigate to the target source. Single and multi-iteration angle-based navigation algorithms were developed. The theta-based path finding algorithms were compared with the Dijkstra Algorithm and their performance were analyzed.
Institute for Intelligent Systems Research and Innovation（智能系统研究与创新研究所）；Deakin University（迪肯大学）
The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLA…
University of Antwerp（安特卫普大学）；Flanders Make Strategic Research Centre（法兰德斯制造战略研究中心）
Navigating spatially varied and dynamic environments is one of the key tasks for autonomous agents. In this paper we present a novel method of navigating a mobile platform with one or multiple 3D-sonar sensors. Moving a mobile platform and subsequently any 3D-sonar sensor on it, will create signature variations over time of the echoed reflections in the sensor readings. An approach is presented to create a predictive model of these signature variations for any motion type. Furthermore, the model is adaptive and works for any position and orientation of one or multiple sonar sensors on a mobile…
This work proposes a novel transformation termed the conformal navigation transformation to achieve collision-free navigation of a robot in a workspace populated with arbitrary polygonal obstacles. The properties of the conformal navigation transformation in the polygonal workspace are investigated in this work as well as its capability to provide a solution to the navigation problem. %The properties of the conformal navigation transformation are investigated, which contribute to the solution of the robot navigation problem in complex polygonal environments. %which facilitates the navigation o…
Daffodil International University（达卡国际大学）；Saint Petersburg Electrotechnical University “LETI”（圣彼得堡国立电工大学）
In the recent decade, electronic technology gets advanced day by day the methodologies too should update. For the purpose of ranging various methods such Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR) and Sonic Navigation and Ranging (SONAR) etc. are used. Later, by adapting the earlier technologies and further modifying the purposes of detection and ranging in navigation, the technology of Sonic Detection and Ranging (SODAR) is used in modern robotics. The SODAR can be defined as a child of SONAR and also a twin of Echo sounder. The echo-sounder is used only for rang…
NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints
College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology（澳门大学系统工程与智能科学与系统联合实验室）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动系统学院）
Gaining spatial awareness of the Operating Room (OR) for surgical robotic systems is a key technology that can enable intelligent applications aiming at improved OR workflow. In this work, we present a method for semantic dense reconstruction of the OR scene using multiple RGB-D cameras attached and registered to the da Vinci Xi surgical system. We developed a novel SLAM approach for robot pose tracking in dynamic OR environments and dense reconstruction of the static OR table object. We validated our techniques in a mock OR by collecting data sequences with corresponding optical tracking traj…
We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outl…
We present a new interface for controlling a navigation robot in novel environments using coordinated gesture and language. We use a TurtleBot3 robot with a LIDAR and a camera, an embodied simulation of what the robot has encountered while exploring, and a cross-platform bridge facilitating generic communication. A human partner can deliver instructions to the robot using spoken English and gestures relative to the simulated environment, to guide the robot through navigation tasks.
Traditional simultaneous localization and mapping (SLAM) methods focus on improvement in the robot's localization under environment and sensor uncertainty. This paper, however, focuses on mitigating the need for exact localization of a mobile robot to pursue autonomous navigation using a sparse set of images. The proposed method consists of a model architecture - RoomNet, for unsupervised learning resulting in a coarse identification of the environment and a separate local navigation policy for local identification and navigation. The former learns and predicts the scene based on the short ter…
University of Birmingham（伯明翰大学）；University of West Attica（西阿提卡大学）
This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator's intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the …
SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Huawei（华为）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Shenzhen Huawei Cloud Computing Technologies Co., Ltd.（华为云计算技术有限公司（深圳））
EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation
University of Bonn（波恩大学）；Center for Robotics at the University of Bonn（波恩大学机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. In this paper, a novel two-step text localization method for Indoor Mobile Robot Navigation is introduced. This method is based on morphological operators and machine learning techniques and can be used in real time environments. The proposed method has two steps. At First, a new set of morphological operators is applied with a particular sequence to extract high contrast areas that have high probability of tex…
University of Science and Technology of China（中国科学技术大学）
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict proper steering actions of a robot from its egocentric local occupancy maps, which accommodate various sensors and fusion algorithms. The trained neural network is then transferred and executed on a real-world mobile robot to guide its local path planning. The new approach is evaluated both qualitatively and quantitatively in simulation and real-worl…
Trường Đại học Công nghệ, Đại học Quốc gia Hà nội（河内国家大学科技大学）
This paper presents the use of multi-sensor measurement system to guide autonomous mobile robot in the house. The system allows the 3D image acquisition to global mapping, and algorithms to reduce the dimensionality of images to 2D global map navigation, trajectory design approach using the Lyapunov function method and avoid obstacles by the potential energy can also be presented. Also, sensor integrated method based on extended Kalman filter allows us to identify the exact location and orientation of the robot in the presence of interference from the environment.
Majority of the existing robot navigation systems, which facilitate the use of laser range finders, sonar sensors or artificial landmarks, has the ability to locate itself in an unknown environment and then build a map of the corresponding environment. Stereo vision, while still being a rapidly developing technique in the field of autonomous mobile robots, are currently less preferable due to its high implementation cost. This paper aims at describing an experimental approach for the building of a stereo vision system that helps the robots to avoid obstacles and navigate through indoor environ…
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；Microsoft（微软）
Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information give…
Integrated YOLOP Perception and Lyapunov-based Control for Autonomous Mobile Robot Navigation on Track
Graduate School of Science and Technology（科学与技术研究生学院）；Master’s Program in Science and Technology（科学技术硕士项目）；Green Science and Engineering Division（绿色科学与工程系）
This paper presents an implementation of autonomous navigation functionality based on Robot Operating System (ROS) on a wheeled differential drive mobile platform called Eddie robot. ROS is a framework that contains many reusable software stacks as well as visualization and debugging tools that provides an ideal environment for any robotic project development. The main contribution of this paper is the description of the customized hardware and software system setup of Eddie robot to work with an autonomous navigation system in ROS called Navigation Stack and to implement one application use c…
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning e…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins University Applied Physics Lab（约翰斯·霍普金斯大学应用物理实验室）；UC Berkeley（加州大学伯克利分校）
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline…
University of Oulu（奥卢大学）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
This paper introduces mathematical models of \sensors\ for mobile robots based on visibility. Serving a purpose similar to the pinhole camera model for computer vision, the introduced models are expected to provide a useful, idealized characterization of task-relevant information that can be inferred from their outputs or observations. Possible tasks include navigation, localization and mapping when a mobile robot is deployed in an unknown environment. These models allow direct comparisons to be made between traditional depth sensors, highlighting cases in which touch sensing may be interchang…
A Koopman Operator-based NMPC Framework for Mobile Robot Navigation under Uncertainty
Department of Electrical and Computer Engineering, University of California, Riverside（加州大学河滨分校电气与计算机工程系）；Polytechnic School of Tunisia（突尼斯多技术学院）；Institute of AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
University of Applied Science Gelsenkirchen（盖尔森基兴应用科学大学）
Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation and continuous control of real mobile robots [13]. Previous approaches lack safety and robustness and/or need a structured environment. In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the…
Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jernes idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, pre-engineered set of behaviours, limiting variability. This paper describes a method for enco…
University of Twente（特温特大学）；Jönköping University（延雪平大学）
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information. The planner is trained using a reward function shaped based on the online knowledge of the map of the training environment, obtained using grid-based Rao-Blackwellized particle filter, in an attempt to enhance the obstacle awareness of the agent. The agent is trained in a complex simulated environment and evaluated in two unseen ones. We show that the policy trained using the intr…
We demonstrated multi-mobile robot navigation based on Visible Light Positioning(VLP) localization. From our experiment, the VLP can accurately locate robots' positions in navigation.
Army Research Laboratory（陆军研究实验室）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sony AI（索尼人工智能研究院）
Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent wo…
Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation probl…
Zen Aerologiks；BMS College of Engineering（BMS工程学院）
The navigation system is at the heart of any mobile robot it comprises of SLAM and path planning units, which is utilized by the robot to generate a map of the environment, localize itself within it and determine an optimal a path to the destination. This paper describes the conceptualization, development, simulation and hardware implementation of GenNav a generic indoor navigation system for any mobile aerial or ground robot. The generalization is brought about by modularizing and creating independence between the software computation and hardware actuation units by providing an alternate sou…
