In this paper, we investigate the utility of head-mounted display (HMD) interfaces for navigation of mobile robots. We focus on the selection of waypoint positions for the robot, whilst maintaining an egocentric view of the robot's environment. Inspired by virtual reality (VR) gaming, we propose a target selection method that uses the 6 degrees-of-freedom tracked controllers of a commercial VR headset. This allows an operator to point to the desired target position, in the vicinity of the robot, which the robot then autonomously navigates towards. A user study (37 participants) was conducted to examine the efficacy of this control strategy when compared to direct control, both with and without a communication delay. The results of the experiment showed that participants were able to learn how to use the novel system quickly, and the majority of participants reported a preference for waypoint control. Across all recorded metrics (task performance, operator workload and usability) the proposed waypoint control interface was not significantly affected by the communication delay, in contrast to direct control. The simulated experiment indicated that a real-world implementation of the proposed interface could be effective, but also highlighted the need to manage the negative effects of HMDs - particularly VR sickness.
Navigation of mobile robots within crowded environments is an essential task in various use cases, such as delivery, health care, or logistics. Deep Reinforcement Learning (DRL) emerged as an alternative method to replace overly conservative approaches and promises more efficient and flexible navigation. However, Deep Reinforcement Learning is limited to local navigation due to its myopic nature. Previous research works proposed various ways to combine Deep Reinforcement Learning with conventional methods but a common problem is the complexity of highly dynamic environments due to the unpredic…
Robert Bosch Center for Cyber-Physical Systems（罗伯特·博世信息物理系统研究中心）；Department of Aerospace Engineering（航空航天工程系）；Indian Institute of Science（印度科学学院）
Aerial package transportation often requires complex spatial and temporal specifications to be satisfied in order to ensure safe and timely delivery from one point to another. It is usually efficient to transport versatile payloads using multiple UAVs that can work collaboratively to achieve the desired task. The complex temporal specifications can be handled coherently by applying Signal Temporal Logic (STL) to dynamical systems. This paper addresses the problem of waypoint navigation of a multi-UAV payload system under temporal specifications using higher-order time-varying control barrier f…
SWIFT-Nav: Stability-Aware Waypoint-Level TD3 with Fuzzy Arbitration for UAV Navigation in Cluttered Environments
School of Computer Science and Engineering, University of New South Wales（计算机科学与工程学院，新南威尔士大学）；Escuela de Ingeniería, Universidad Central de Chile（工程学院，智利中央大学）
This paper aims to improve the path quality and computational efficiency of kinodynamic planners used for vehicular systems. It proposes a learning framework for identifying promising controls during the expansion process of sampling-based motion planners for systems with dynamics. Offline, the learning process is trained to return the highest-quality control that reaches a local goal state (i.e., a waypoint) in the absence of obstacles from an input difference vector between its current state and a local goal state. The data generation scheme provides bounds on the target dispersion and uses …
In this work, we address the problem of computation time for trajectory generation in quadrotors. Most trajectory generation methods for waypoint navigation of quadrotors, for example minimum snap/jerk and minimum-time, are structured as bi-level optimizations. The first level involves allocating time across all input waypoints and the second step is to minimize the snap/jerk of the trajectory under that time allocation. Such an optimization can be computationally expensive to solve. In our approach we treat trajectory generation as a supervised learning problem between a sequential set of inp…
This paper proposes an SVM Enhanced Trajectory Planner for dynamic scenes, typically those encountered in on road settings. Frenet frame based trajectory generation is popular in the context of autonomous driving both in research and industry. We incorporate a safety based maximal margin criteria using a SVM layer that generates control points that are maximally separated from all dynamic obstacles in the scene. A kinematically consistent trajectory generator then computes a path through these waypoints. We showcase through simulations as well as real world experiments on a self driving car th…
SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction and Navigation Using Skip Stage Swin Transformer
Department of Computer Science, Indonesia University of Education（印度尼西亚教育大学计算机科学系）；Research Center for Smart Mechatronics, National Research and Innovation Agency（国家研究与创新局智能机电研究中心）；Department of Computer Science and Electronics, Gadjah Mada University（加查玛达大学计算机科学与电子系）
Zhejiang University（浙江大学）；Zhejiang University of Technology（浙江工业大学）；Northwestern Polytechnical University（西北工业大学）
Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track…
Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary…
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a more robust technique to distinguish and track dynamic obstacles from static ones with only point cloud input. Then, to achieve dynamic avoidance, we propose the forbidden pyramids method to solve the desired vehicle velocity with an efficient sampling-based method in iteration. The motion primitives are generated by solving a nonlinear optimization problem with the constraint of desired velocity and the waypoint. Furthermor…
Singapore University of Technology and Design（新加坡科技设计大学）；Indian Institute of Science（印度科学学院）
A solution to the waypoint navigation problem for fixed wing micro air vehicles (MAV) is addressed in this paper, in the framework of integrated guidance and control (IGC). IGC yields a single step solution to the waypoint navigation problem, unlike conventional multiple loop design. The pure proportional navigation (PPN) guidance law is integrated with the MAV dynamics. A multivariable static output feedback (SOF) controller is designed for the linear state space model formulated in the IGC framework. The waypoint navigation algorithm handles the minimum turn radius constraint of the MAV. The…
Institute of Computer Science II – Computer Graphics, University of Bonn（波恩大学计算机科学II系——计算机图形学）；Institute of Computer Science VI – Autonomous Intelligent Systems, University of Bonn（波恩大学计算机科学VI系——自主智能系统）
Applications like disaster management and industrial inspection often require experts to enter contaminated places. To circumvent the need for physical presence, it is desirable to generate a fully immersive individual live teleoperation experience. However, standard video-based approaches suffer from a limited degree of immersion and situation awareness due to the restriction to the camera view, which impacts the navigation. In this paper, we present a novel VR-based practical system for immersive robot teleoperation and scene exploration. While being operated through the scene, a robot captu…
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, the integration of Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obst…
In this paper, we present an autonomous navigation system for goal-driven exploration of unknown environments through deep reinforcement learning (DRL). Points of interest (POI) for possible navigation directions are obtained from the environment and an optimal waypoint is selected, based on the available data. Following the waypoints, the robot is guided towards the global goal and the local optimum problem of reactive navigation is mitigated. Then, a motion policy for local navigation is learned through a DRL framework in a simulation. We develop a navigation system where this learned policy…
This report proposes a combined optimal control and perception framework for Micro Aerial Vehicle (MAV) autonomous navigation in novel indoor enclosed environments, relying exclusively on on-board sensor data. We use privileged information from a simulator to generate optimal waypoints in 3D space that our perception system learns to imitate. The trained learning based perception module in turn is able to generate similar obstacle avoiding waypoints from sensor data (RGB + IMU) alone. We demonstrate the efficacy of the framework across novel scenes in the iGibson simulation environment.
Oregon State University（俄勒冈州立大学）；Facebook AI Research（脸书人工智能研究院）；Cornell University（康奈尔大学）；Georgia Institute of Technology（佐治亚理工学院）
Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory. Building on the recently released VLN-CE setting for instruction following in continuous environments, we develop a class of language-conditioned waypoint prediction networks to examine this question. We vary the expressivity of these models to explore a spectrum between low-level actions and continuous waypoint prediction. We measure task performance and…
UT Austin（德克萨斯大学奥斯汀分校）；Facebook AI Research（脸书人工智能研究院）
In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e.g., a phone ringing in another room). Existing models learn to act at a fixed granularity of agent motion and rely on simple recurrent aggregations of the audio observations. We introduce a reinforcement learning approach to audio-visual navigation with two key novel elements: 1) waypoints that are dynamically set and learned end-to-end within the navigation policy, and 2) an acoustic memory that provides a structured, spatially grounded r…
Simon Fraser University（西蒙弗雷泽大学）；IIT Kanpur（印度理工学院坎普尔分校）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle 'off the path' scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent's location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we prop…
Simon Fraser University（西蒙弗雷泽大学）；University of California, Berkeley（加州大学伯克利分校）
In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed. Specifically, a Convolutional Neural Network (CNN) predicts a waypoint that is used by the dynamics model for planning and tracking a trajectory to the waypoint. However, the CNN inevitably makes prediction errors which often lead to collisions in cluttered and tight spaces. In this paper, we present a novel Hamilton-Jacobi (HJ) reachability-based method to generate supervision for the CNN for waypoint prediction in an unseen environment. By modeling CNN predi…
University of Adelaide（阿德莱德大学）；The Australian National University（澳大利亚国立大学）
Most existing works in vision-and-language navigation (VLN) focus on either discrete or continuous environments, training agents that cannot generalize across the two. The fundamental difference between the two setups is that discrete navigation assumes prior knowledge of the connectivity graph of the environment, so that the agent can effectively transfer the problem of navigation with low-level controls to jumping from node to node with high-level actions by grounding to an image of a navigable direction. To bridge the discrete-to-continuous gap, we propose a predictor to generate a set of c…
Robust Mobile Robot Path Planning via LLM-Based Dynamic Waypoint Generation
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）；School of Computer Science and Mathematics, Liverpool John Moores University（利物浦约翰摩尔斯大学计算机科学与数学学院）
MetricNet: Recovering Metric Scale in Generative Navigation Policies
Artificial Intelligence and Robotics Lab, Department of Computer Science and Artificial Intelligence, University of Technology Nuremberg（技术大学纽伦堡计算机科学与人工智能系人工智能与机器人实验室）；Inria, Ecole Normale Supérieure, CNRS, PSL Research University（法国国家信息与自动化技术研究院、巴黎高等师范学校、国家科学研究中心、巴黎-萨克勒研究大学）
Immersive Explainability: Visualizing Robot Navigation Decisions through XAI Semantic Scene Projections in Virtual Reality
University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）；Center for Robotics（机器人中心）；University of Mainz（美因茨大学）；Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所）
Deep Reinforcement Learning (DRL) is hugely successful due to the availability of realistic simulated environments. However, performance degradation during simulation to real-world transfer still remains a challenging problem for the policies trained in simulated environments. To close this sim-to-real gap, we present a novel hybrid architecture that utilizes an intermediate output from a fully trained attention DRL policy as a navigation cost map for outdoor navigation. Our attention DRL network incorporates a robot-centric elevation map, IMU data, the robot's pose, previous actions, and goal…
Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environments
College of Engineering, Peking University（北京大学工学院）；Defense Innovation Institute, Academy of Military Sciences（军事科学院国防创新研究院）；Tianjin Artificial Intelligence Innovation Center（天津人工智能创新中心）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen University Town (HIT Campus)（深圳大学城（哈工大校区））
University of Toronto（多伦多大学）；McGill University（麦吉尔大学）
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose, safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by traini…
Aalto University（阿尔托大学）；The University of Queensland（昆士兰大学）；Queensland Brain Institute（昆士兰脑研究所）；Queensland University of Technology（昆士兰科技大学）；University of Oulu（奥卢大学）
With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior. However, preparing a VE still requires a high level of technical expertise in computer graphics and virtual reality, posing a significant hurdle to embracing the emerging technology. To address this issue, this paper presents Delayed Feedback based Immersive Navigation Environment (DeFINE), a framework that allows for easy creation and administration of navigation tasks within customizable VEs via intuitive graphica…
