Vision and Learning for Deliberative Monocular Cluttered Flight

Dey, Debadeepta; Shankar, Kumar Shaurya; Zeng, Sam; Mehta, Rupesh; Agcayazi, M. Talha; Eriksen, Christopher; Daftry, Shreyansh; Hebert, Martial; Bagnell, J. Andrew

doi:10.48550/arxiv.1411.6326

Cited by 4 publications

(7 citation statements)

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“…Monocular based vision detection systems have been proposed to bypass both stereo vision limitations and geometric assumptions. Since monocular vision does not allow accurate and robust distance geometric measurement, often machine learning based solutions have been proposed [7], [8]. Since learning methods are limited by the training set samples and these methods have been trained using datasets with ground truths collected through stereo vision or laser rigs, these solutions still have limitations on range and accuracy as stereo systems.…”

Section: Introductionmentioning

confidence: 99%

Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

Mancini

Costante

Valigi

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer enough to allow for safe avoidance and path planning. Current solutions often make assumption on the motion of the vehicle that limit their applicability, or work at very limited ranges due to intrinsic constraints. We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (∼ 300Hz), without making assumptions on the type of motion. We achieve these results using a Deep Neural Network approach trained on real and synthetic images and trading some depth accuracy for fast, robust and consistent operation. We show how photo-realistic synthetic images are able to solve the problem of training set dimension and variety typical of machine learning approaches, and how our system is robust to massive blurring of test images.

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Section: Introductionmentioning

confidence: 99%

Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

Mancini

Costante

Valigi

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The motion planning aspect of our approach draws inspiration from the vast body of work that is focused on computing motion primitives in the form of trajectory libraries. For example, trajectory libraries have been used in diverse applications such as humanoid balance control [Liu and Atkeson, 2009], autonomous ground vehicle navigation [Sermanet et al, 2008], grasping [Berenson et al, 2007] [Dey et al, 2011, and UAV navigation [Dey et al, 2014] [Barry, 2016]. The Maneuver Automaton [Frazzoli et al, 2005] attempts to capture the formal properties of trajectory libraries as a hybrid automaton, thus providing a unifying theoretical framework.…”

Section: Motion Planningmentioning

confidence: 99%

Funnel libraries for real-time robust feedback motion planning

Majumdar

Tedrake

2017

The International Journal of Robotics Research

331

270

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We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real-time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances.We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (∼12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, the resulting hardware demonstrations on a fixed-wing airplane constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in realtime in environments with complex geometric constraints.The key computational engine we leverage is sums-of-squares (SOS) programming. While SOS programming allows us to apply our approach to systems of relatively high dimensionality (up to approximately 10-15 dimensional state spaces), scaling our approach to higher dimensional systems such as humanoid robots requires a different set of computational tools. In this thesis, we demonstrate how DSOS and SDSOS programming, which are recently introduced alternatives to SOS programming, can be employed to achieve this improved scalability and handle control systems with as many as 30-50 state dimensions. My conversations with him on measure theory, functional analysis, and algebraic topology were extremely enriching and provided me with a set of technical tools that will no doubt be very valuable going forwards. I am grateful to Hongkai for giving me the chance to explore problems in grasping and manipulation with him. Hongkai's incredible capacity to work long hours have been an inspiration throughout my time as a graduate student and have pushed me to work that extra bit longer myself. I have lost count of the number of times a quick late night conversation with him has helped solve a problem that I was stuck on.I would also like to thank the other members of the Robot Locomotion Group for inspiration, advice, help, and entertainment during my time here. It has been an honor to work with people who will no doubt lead our field in years to come. Ian Manchester, Zack Jackowski, Michael Levashov, John Roberts,...

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“…Current research on autonomous navigation for UAV can be divided into two groups based on whether path planning or waypoint navigation is the main objective [4], [15]. Path planning requires understanding the environment ahead and it is usually achievable by pre-mapping the environment or specifying a navigational area as the UAV flight takes place [16]- [19], which means the UAV can operate at a constant speed for a set duration in a specific direction [20], [21]. Within the existing literature, various end-to-end learningbased approaches have been employed to derive a set of navigational parameters from a given image, allowing for obstacle avoidance [16], [18], [20], [22].…”

Section: Related Workmentioning

confidence: 99%

“…Path planning requires understanding the environment ahead and it is usually achievable by pre-mapping the environment or specifying a navigational area as the UAV flight takes place [16]- [19], which means the UAV can operate at a constant speed for a set duration in a specific direction [20], [21]. Within the existing literature, various end-to-end learningbased approaches have been employed to derive a set of navigational parameters from a given image, allowing for obstacle avoidance [16], [18], [20], [22]. Additionally, the recent advances made in multi-task systems partially focusing on depth estimation [23]- [26] can also be potentially beneficial towards a successful obstacle avoidance and path planning approach.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

Maciel-Pearson

Akçay

Atapour-Abarghouei

et al. 2019

IEEE Robot. Autom. Lett.

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Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this paper, we propose an End-to-End Multi-Task Regression-based Learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e. GPS). Training and testing are performed using a software in the loop pipeline which allows for a detailed evaluation against state-of-the-art pose estimation techniques.Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.

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Vision and Learning for Deliberative Monocular Cluttered Flight

Cited by 4 publications

References 0 publications

Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

Funnel libraries for real-time robust feedback motion planning

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

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