Environment feedback t = 0 t = 1 t = 2 t = 3 t = 4 t = H … Action Collision with wall Collision with Furniture No Collision Different Platforms time Training entirely in simulation Test in real world Fig. 1. We propose the Collision Avoidance via Deep Reinforcement Learning algorithm for indoor flight which is entirely trained in a simulated CAD environment. Left: CAD 2 RL uses single image inputs from a monocular camera, is exclusively trained in simulation, and does not see any real images at training time. Training is performed using a Monte Carlo policy evaluation method, which performs rollouts for multiple actions from each initial state and trains a deep network to predict long-horizon collision probabilities of each action. Right: CAD 2 RL generalizes to real indoor flight. Abstract-Deep reinforcement learning has emerged as a promising and powerful technique for automatically acquiring control policies that can process raw sensory inputs, such as images, and perform complex behaviors. However, extending deep RL to real-world robotic tasks has proven challenging, particularly in safety-critical domains such as autonomous flight, where a trial-and-error learning process is often impractical. In this paper, we explore the following question: can we train vision-based navigation policies entirely in simulation, and then transfer them into the real world to achieve real-world flight without a single real training image? We propose a learning method that we call CAD 2 RL, which can be used to perform collision-free indoor flight in the real world while being trained entirely on 3D CAD models. Our method uses single RGB images from a monocular camera, without needing to explicitly reconstruct the 3D geometry of the environment or perform explicit motion planning. Our learned collision avoidance policy is represented by a deep convolutional neural network that directly processes raw monocular images and outputs velocity commands. This policy is trained entirely on simulated images, with a Monte Carlo policy evaluation algorithm that directly optimizes the network's ability to produce collision-free flight. By highly randomizing the rendering settings for our simulated training set, we show that we can train a policy that generalizes to the real world, without requiring the simulator to be particularly realistic or high-fidelity. We evaluate our method by flying a real quadrotor through indoor environments, and further evaluate the design choices in our simulator through a series of ablation studies on depth prediction. For supplementary video see: https://youtu.be/nXBWmzFrj5s
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Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.
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How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see: https://youtu.be/u151hJaGKUo
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We consider the task of driving a remote control car at high speeds through unstructured outdoor environments. We present an approach in which supervised learning is first used to estimate depths from single monoc-ular images. The learning algorithm can be trained either on real camera images labeled with ground-truth distances to the closest obstacles , or on a training set consisting of synthetic graphics images. The resulting algorithm is able to learn monocular vision cues that accurately estimate the relative depths of obstacles in a scene. Reinforcement learn-ing/policy search is then applied within a simulator that renders synthetic scenes. This learns a control policy that selects a steering direction as a function of the vision system's output. We present results evaluating the predictive ability of the algorithm both on held out test data, and in actual autonomous driving experiments.
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本文介绍了我们的方法,使配备有单目相机的无人机四旋翼能够自主地避免与未构造和未知的室内环境中的障碍物碰撞。与地面车辆机器人中的障碍物相比,无人机导航带来了额外的挑战,因为无人机运动不再局限于明确的室内地面或街道环境。室内和室外环境中的水平结构,如装饰物品,家具,吊扇,标志牌,树枝等,也成为与地面车辆机器人不同的相关障碍。因此,为地面机器人开发的避障方法显然不适用于无人机导航。使用单眼图像用于无人机避障的当前控制方法严重依赖于环境信息。这些控制器不能完全保留和利用有关决策制定的周围环境的广泛可用信息。我们提出了一种基于深度强化学习的UAVobstacle避免(OA)和自主探索方法,它能够完全相同。我们方法中的关键思想是部分可观测性的概念以及无人机如何保留有关环境结构的相关信息,以便做出更好的未来导航决策。我们的OA技术使用具有时间关注度的递归神经网络,并且与先前的工作相比,在没有碰撞的导航期间覆盖的距离方面提供更好的结果。此外,我们的技术具有很高的推理力(机器人应用中的关键因素),并且能够高效节能,因为它最大限度地减少了无人机的振荡运动并减少了功耗。
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Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions.
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In this paper, we present our latest achievements towards the goal of autonomous flights of an MAV in unknown environments, only having a monocular camera as exteroceptive sensor. As MAVs are highly agile, it is not sufficient to directly use the visual input for position control at the framerates that can be achieved with small onboard computers. Our contributions in this work are twofold. First, we present a solution to overcome the issue of having a low frequent onboard visual pose update versus the high agility of an MAV. This is solved by filtering visual information with inputs from inertial sensors. Second, as our system is based on monocular vision, we present a solution to estimate the metric visual scale aid of an air pressure sensor. All computation is running onboard and is tightly integrated on the MAV to avoid jitter and latencies. This framework enables stable flights indoors and outdoors even under windy conditions.
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Automatic obstacle detection and avoidance is a key component for the success of micro-aerial vehicles (MAVs) in the future. As the payload of MAVs is highly constrained, cameras are attractive sensors because they are both lightweight and provide rich information about the environment. In this paper, we present an approach that allows a quadrotor with a single monocular camera to locally generate collision-free waypoints. We acquire a small set of images while the quadrotor is hovering from which we compute a dense depth map. Based on this depth map, we render a 2D scan and generate a suitable waypoint for navigation. In our experiments, we found that the pose variation during hovering is already sufficient to obtain suitable depth maps. The computation takes less than one second which renders our approach applicable for obstacle avoidance in real-time. We demonstrate the validity of our approach in challenging environments where we navigate a Parrot Ardrone quadrotor successfully through narrow passages including doors, boxes, and people.
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Mapping a river's geometry provides valuable information to help understand the topology and health of an environment and deduce other attributes such as which types of surface vessels could traverse the river. While many rivers can be mapped from satellite imagery, smaller rivers that pass through dense vegetation are occluded. We develop a micro air vehicle (MAV) that operates beneath the tree line, detects and maps the river, and plans paths around three-dimensional (3D) obstacles (such as overhanging tree branches) to navigate rivers purely with onboard sensing, with no GPS and no prior map. We present the two enabling algorithms for exploration and for 3D motion planning. We extract high-level goal-points using a novel exploration algorithm that uses multiple layers of information to maximize the length of the river that is explored during a mission. We also present an efficient modification to the SPARTAN (Sparse Tangential Network) algorithm called SPARTAN-lite, which exploits geodesic properties on smooth manifolds of a tangential surface around obstacles to plan rapidly through free space. Using limited onboard resources, the exploration and planning algorithms together compute trajectories through complex unstructured and unknown terrain, a capability rarely demonstrated by flying vehicles operating over rivers or over ground. We evaluate our approach against commonly employed algorithms and compare guidance decisions made by our system to those made by a human piloting a boat carrying our system over multiple kilometers. We also present fully autonomous flights on riverine environments generating 3D maps over several hundred-meter stretches of tight winding rivers. C 2015 Wiley Periodicals, Inc.
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RGB-D cameras provide both a color image and per-pixel depth estimates. The richness of their data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent state-of-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on unreliable wireless links. We evaluate the effectiveness of our system for stabilizing and controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.
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This paper presents our solution for enabling a quadrotor helicopter to autonomously navigate, explore and locate objects of interest in unstructured and unknown indoor environments. We describe the design and operation of our quadrotor helicopter, before presenting the software architecture and individual algorithms necessary for executing the mission. Experimental results are presented demonstrating the quadrotor's ability to operate autonomously in indoor environments. Finally, we address some of the logistical and risk management issues pertaining to our entry in the competition.
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High speed, low latency obstacle avoidance is essential for enabling Micro Aerial Vehicles (MAVs) to function in cluttered and dynamic environments. While other systems exist that do high-level mapping and 3D path planning for obstacle avoidance, most of these systems require high-powered CPUs on-board or off-board control from a ground station. We present a novel entirely on-board approach, leveraging a lightweight low power stereo vision system on FPGA. Our approach runs at a frame rate of 60 frames a second on VGA-sized images and minimizes latency between image acquisition and performing reactive maneuvers, allowing MAVs to fly more safely and robustly in complex environments. We also suggest our system as a lightweight safety layer for systems undertaking more complex tasks, like mapping the environment. Finally, we show our algorithm implemented on a lightweight , very computationally constrained platform, and demonstrate obstacle avoidance in a variety of environments.
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The ability to transfer knowledge gained in previous tasks into new contexts is one of the most important mechanisms of human learning. Despite this, adapting autonomous behavior to be reused in partially similar settings is still an open problem in current robotics research. In this paper, we take a small step in this direction and propose a generic framework for learning transferable motion policies. Our goal is to solve a learning problem in a target domain by utilizing the training data in a different but related source domain. We present this in the context of an autonomous MAV flight using monocular reactive control, and demonstrate the efficacy of our proposed approach through extensive real-world flight experiments in outdoor cluttered environments.
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Obstacle avoidance from monocular images is a challenging problem for robots. Though multi-view structure-from-motion could build 3D maps, it is not robust in texture-less environments. Some learning based methods exploit human demonstration to predict a steering command directly from a single image. However, this method is usually biased towards certain tasks or demonstration scenarios and also biased by human understanding. In this paper, we propose a new method to predict a trajectory from images. We train our system on more diverse NYUv2 dataset. The ground truth trajectory is computed from the designed cost functions automatically. The Convolutional Neural Network perception is divided into two stages: first, predict depth map and surface normal from RGB images, which are two important geometric properties related to 3D obstacle representation. Second, predict the trajectory from the depth and normal. Results show that our intermediate perception increases the accuracy by 20% than the direct prediction. Our model generalizes well to other public indoor datasets and is also demonstrated for robot flights in simulation and experiments.
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This paper demonstrates the use of a single forward facing camera for obstacle avoidance on a quadrotor. We train a CNN for estimating depth from a single image. The depth map is then fed to a behaviour arbitration based control algorithm that steers the quadrotor away from obstacles. We conduct experiments with simulated and real drones in a variety of environments.
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Learning from demonstration for motion planning is an ongoing research topic. In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot. To our best knowledge, this work presents the first approach that learns a target-oriented end-to-end navigation model for a robotic platform. The supervised model training is based on expert demonstrations generated in simulation with an existing motion planner. We demonstrate that the learned navigation model is directly transferable to previously unseen virtual and, more interestingly, real-world environments. It can safely navigate the robot through obstacle-cluttered environments to reach the provided targets. We present an extensive qualitative and quantitative evaluation of the neural network-based motion planner, and compare it to a grid-based global approach, both in simulation and in real-world experiments.
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在人类活动示范训练的深度神经网络赋予了在路上进行自驾车的能力。然而,在诸如校园设施之类的行人富裕环境中导航仍然具有挑战性 - 需要经常对机器人进行干预并且从早期步骤控制机器人导致错误。因此,艰巨的负担放在学习框架设计和数据采集上。在本文中,我们提出了一种新的干预学习数据集聚(DAgger)算法,以克服在行人富裕环境中应用模仿学习导航所带来的局限性。我们的新学习算法实现了错误回溯功能,能够有效地学习fromexpert干预。结合我们的新学习算法与深度卷积神经网络和分层嵌套的策略选择机制,我们展示了我们的机器人能够将像素直接映射到控制命令并在现实世界中成功导航,而无需对行人行为或世界模型进行明确建模。
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无人驾驶飞行器(UAV)最近迅速发展,以促进广泛的创新应用,这些应用可以从根本上改变设计的道路物理系统(CPS)。 CPS是现代一代的系统,具有计算和物理潜力之间的协同合作,可以通过几种新机制与人类进行交互。在CPS应用中使用无人机的主要优势在于其独特的功能,包括其移动性,动态性,轻松部署,自适应高度,灵活性,可调节性以及随时随地有效评估现实世界的功能。此外,从技术角度来看,预计无人机将成为先进CPS发展的重要元素。因此,在本次调查中,我们的目标是确定用于CPS应用的多无人机系统的最基本和最重要的设计挑战。我们突出了关键和多功能方面,涵盖目标和基础设施对象的覆盖和跟踪,节能导航和使用机器学习的细粒度CPS应用程序的图像分析。还研究了关键原型和测试平台,以展示这些实用技术如何促进CPS应用。我们提出并提出最先进的算法,用定量和定性方法解决设计挑战,并将这些挑战与重要的CPS应用相结合,以便对每种应用的挑战得出深刻的结论。最后,我们总结了可能影响这些领域未来研究的潜在新方向和想法。
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This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system-denied and highly dynamic environments with poor a priori information. C 2008 Wiley Periodicals, Inc.
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(a) Aerial view of test environment (b) Vision-based driving, view from onboard camera (c) Side view of vehicle Fig. 1. Conditional imitation learning allows an autonomous vehicle trained end-to-end to be directed by high-level commands. (a) We train and evaluate robotic vehicles in the physical world (top) and in simulated urban environments (bottom). (b) The vehicles drive based on video from a forward-facing onboard camera. At the time these images were taken, the vehicle was given the command "turn right at the next intersection". (c) The trained controller handles sensorimotor coordination (staying on the road, avoiding collisions) and follows the provided commands. Abstract-Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an up-coming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands.
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