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检索条件"丛书名=Springer Tracts in Advanced Robotics"

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A Feature Learning Based Approach for Automated Fruit Yield Estimation 9

A Feature Learning Based Approach for Automated Fruit Yield ...

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Field and Service robotics

作者： Hung, Calvin Underwood, James Nieto, Juan Sukkarieh, Salah Univ Sydney Australian Ctr Field Robot Sydney NSW 2006 Australia

ISBN: (纸本)9783319074887;9783319074870

This paper demonstrates a generalised multi-scale feature learning approach to multi-class segmentation, applied to the estimation of fruit yield on tree-crops. The learning approach makes the algorithm flexible and adaptable to different classification problems, and hence applicable to a wide variety of tree-crop applications. Extensive experiments were performed on a dataset consisting of 8000 colour images collected in an apple orchard. This paper shows that the algorithm was able to segment apples with different sizes and colours in an outdoor environment with natural lighting conditions, with a single model obtained from images captured using a monocular colour camera. The segmentation results are applied to the problem of fruit counting and the results are compared against manual counting. The results show a squared correlation coefficient of R-2 = 0.81.

关键词： Fruits

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Tightly-Coupled Model Aided Visual-Inertial Fusion for Quadrotor Micro Air Vehicles 9

Tightly-Coupled Model Aided Visual-Inertial Fusion for Quadr...

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Field and Service robotics

作者： Abeywardena, Dinuka Dissanayake, Gamini Univ Technol Sydney Ctr Autonomous Syst Sydney NSW Australia

ISBN: (纸本)9783319074887;9783319074870

The main contribution of this paper is a tightly-coupled visual-inertial fusion algorithm for simultaneous localisation and mapping (SLAM) for a quadrotor micro aerial vehicle (MAV). Proposed algorithm is based on an extended Kalman filter that uses a platform specific dynamic model to integrate information from an inertial measurement unit (IMU) and a monocular camera on board the MAV. MAV dynamic model exploits the unique characteristics of the quadrotor, making it possible to generate relatively accurate motion predictions. This, together with an undelayed feature initialisation strategy based on inverse depth parametrisation enables more effective feature tracking and reliable visual SLAM with a small number of features even during rapid manoeuvres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

关键词： Antennas

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Inspection of Penstocks and Featureless Tunnel-like Environments Using Micro UAVs 9

Inspection of Penstocks and Featureless Tunnel-like Environm...

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Field and Service robotics

作者： Ozaslan, Tolga Shen, Shaojie Mulgaonkar, Yash Michael, Nathan Kumar, Vijay Univ Penn Philadelphia PA 19104 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9783319074887;9783319074870

Micro UAVs are receiving a great deal of attention in many diverse applications. In this paper, we are interested in a unique application, surveillance for maintenance of large infrastructure assets such as dams and penstocks, where the goal is to periodically inspect and map the structure to detect features that might indicate the potential for failures. Availability of architecture drawings of these constructions makes the mapping problem easier. However large buildings with featureless geometries pose a significant problem since it is difficult to design a robust localization algorithm for inspection operations. In this paper we show how a small quadrotor equipped with minimal sensors can be used for inspection of tunnel-like environments such as seen in dam penstocks. Penstocks in particular lack features and do not provide adequate structure for robot localization, especially along the tunnel axis. We develop a Rao-Blackwellized particle filter based localization algorithm which uses a derivative of the ICP for integrating laser measurements and IMU for short-to-medium range pose estimation. To our knowledge, this is the only study in the literature focusing on localization and autonomous control of a UAV in 3-D, featureless tunnel-like environments. We show the success of our work with results from real experiments.

关键词： Robot applications

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Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning 11th

Sparse Methods for Efficient Asymptotically Optimal Kinodyna...

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11th Workshop on Algorithmic Foundations of robotics (WAFR)

作者： Li, Yanbo Littlefield, Zakary Bekris, Kostas E. Rutgers State Univ Piscataway NJ 08855 USA

ISBN: (纸本)9783319165950;9783319165943

This work describes STABLE SPARSE RRT (SST), an algorithm that (a) provably provides asymptotic (near-) optimality for kinodynamic planning without access to a steering function, (b) maintains only a sparse set of samples, (c) converges fast to high-quality paths and (d) achieves competitive running time to RRT, which provides only probabilistic completeness. SST addresses the limitation of RRT*, which requires a steering function for asymptotic optimality. This issue has motivated recent variations of RRT*, which either work for a limiting set of systems or exhibit increased computational cost. This paper provides formal arguments for the properties of the proposed algorithm. To the best of the authors' knowledge, this is the first sparse data structure that provides such desirable guarantees for a wide set of systems under a reasonable set of assumptions. Simulations for a variety of benchmarks, including physically simulated ones, confirm the argued properties of the approach.

关键词： Artificial intelligence

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Finding a Needle in an Exponential Haystack: Discrete RRT for Exploration of Implicit Roadmaps in Multi-robot Motion Planning 11th

Finding a Needle in an Exponential Haystack: Discrete RRT fo...

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11th Workshop on Algorithmic Foundations of robotics (WAFR)

作者： Solovey, Kiril Salzman, Oren Halperin, Dan Tel Aviv Univ Blavatnik Sch Comp Sci IL-69978 Tel Aviv Israel

ISBN: (纸本)9783319165950;9783319165943

We present a sampling-based framework for multi-robot motion planning which combines an implicit representation of a roadmap with a novel approach for pathfinding in geometrically embedded graphs tailored for our setting. Our pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated RRT algorithm for the discrete case of a graph, and it enables a rapid exploration of the high-dimensional configuration space by carefully walking through an implicit representation of a tensor product of roadmaps for the individual robots. We demonstrate our approach experimentally on scenarios of up to 60 degrees of freedom where our algorithm is faster by a factor of at least ten when compared to existing algorithms that we are aware of.

关键词： Degrees of freedom (mechanics)

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Asymptotically Optimal Stochastic Motion Planning with Temporal Goals 11th

Asymptotically Optimal Stochastic Motion Planning with Tempo...

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11th Workshop on Algorithmic Foundations of robotics (WAFR)

作者： Luna, Ryan Lahijanian, Morteza Moll, Mark Kavraki, Lydia E. Rice Univ Dept Comp Sci Houston TX USA

ISBN: (纸本)9783319165950;9783319165943

This work presents a planning framework that allows a robot with stochastic action uncertainty to achieve a high-level task given in the form of a temporal logic formula. The objective is to quickly compute a feedback control policy to satisfy the task specification with maximum probability. A top-down framework is proposed that abstracts the motion of a continuous stochastic system to a discrete, bounded-parameter Markov decision process (bmdp), and then computes a control policy over the product of the bmdp abstraction and a dfa representing the temporal logic specification. Analysis of the framework reveals that as the resolution of the bmdp abstraction becomes finer, the policy obtained converges to optimal. Simulations show that high-quality policies to satisfy complex temporal logic specifications can be obtained in seconds, orders of magnitude faster than existing methods.

关键词： Planning under uncertainty Temporal logic planning Stochastic systems Formal control synthesis

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Hierarchical Classification in AUV Imagery 9

Hierarchical Classification in AUV Imagery

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Field and Service robotics

作者： Bewley, M. S. Nourani-Vatani, N. Rao, D. Douillard, B. Pizarro, O. Williams, S. B. Univ Sydney Australian Ctr Field Robot Sydney NSW 2050 Australia

ISBN: (纸本)9783319074887;9783319074870

In recent years, Autonomous Underwater Vehicles (AUVs) have been used extensively to gather imagery and other environmental data for ocean monitoring. Processing of this vast amount of collected imagery to label content is difficult, expensive and time consuming. Because of this, typically only a small subset of images are labelled, and only at a small number of points. In order to make full use of the raw data returned from the AUV, this labelling process needs to be automated. In this work the single species classification problem of [1] is extended to a multi-species classification problem following a taxonomical hierarchy. We demonstrate the application of techniques used in areas such as computer vision, text classification and medical diagnosis to the supervised hierarchical classification of benthic images. After making a comparison to flat multi-class classification, we also discuss critical aspects such as training topology and various prediction and scoring methodologies. An interesting aspect of the presented work is that the ground truth labels are sparse and incomplete, i.e. not all labels go to the leaf node, which brings with it other interesting challenges. We find that the best classification results are obtained using Local Binary Patterns (LBP), training a network of binary classifiers with probabilistic output, and applying "one-vs-rest" classification at each level of the hierarchy for prediction. This work presents a working solution that allows AUV images to be automatically labelled with the most appropriate node in a hierarchy of 19 biological groupings and morphologies. The result is that the output of the AUV system can include a semantic map using the taxonomy prescribed by marine scientists. This has the potential to not only reduce the manual labelling workload, but also to reduce the current dependence that marine scientists have on extrapolating information from a relatively small number of sparsely labelled points.

关键词： Autonomous underwater vehicles

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A Region-Based Strategy for Collaborative Roadmap Construction 11th

A Region-Based Strategy for Collaborative Roadmap Constructi...

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11th Workshop on Algorithmic Foundations of robotics (WAFR)

作者： Denny, Jory Sandstrom, Read Julian, Nicole Amato, Nancy M. Texas A&M Univ Parasol Lab Dept Comp Sci & Engn College Stn TX USA

ISBN: (纸本)9783319165950;9783319165943

Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success inmany scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRMplanner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness. We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.

关键词： Motion planning

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Computing Large Convex Regions of Obstacle-Free Space Through Semidefinite Programming 11th

Computing Large Convex Regions of Obstacle-Free Space Throug...

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11th Workshop on Algorithmic Foundations of robotics (WAFR)

作者： Deits, Robin Tedrake, Russ MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA

ISBN: (纸本)9783319165950;9783319165943

This paper presents iris (Iterative Regional Inflation by Semidefinite programming), a new method for quickly computing large polytopic and ellipsoidal regions of obstacle-free space through a series of convex optimizations. These regions can be used, for example, to efficiently optimize an objective over collision-free positions in space for a robot manipulator. The algorithm alternates between two convex optimizations: (1) a quadratic program that generates a set of hyperplanes to separate a convex region of space from the set of obstacles and (2) a semidefinite program that finds a maximum-volume ellipsoid inside the polytope intersection of the obstaclefree half-spaces defined by those hyperplanes. Both the hyperplanes and the ellipsoid are refined over several iterations to monotonically increase the volume of the inscribed ellipsoid, resulting in a large polytope and ellipsoid of obstacle-free space. Practical applications of the algorithm are presented in 2D and 3D, and extensions to N-dimensional configuration spaces are discussed. Experiments demonstrate that the algorithm has a computation time which is linear in the number of obstacles, and our matlab [18] implementation converges in seconds for environments with millions of obstacles.

关键词： Convex optimization

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Mapping 3D Underwater Environments with Smoothed Submaps 9

Mapping 3D Underwater Environments with Smoothed Submaps

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Field and Service robotics

作者： VanMiddlesworth, Mark Kaess, Michael Hover, Franz Leonard, John J. MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA MIT Dept Mech Engn Cambridge MA 02139 USA

ISBN: (纸本)9783319074887;9783319074870

This paper presents a technique for improved mapping of complex underwater environments. Autonomous underwater vehicles (AUVs) are becoming valuable tools for inspection of underwater infrastructure, and can create 3D maps of their environment using high-frequency profiling sonar. However, the quality of these maps is limited by the drift in the vehicle's navigation system. We have developed a technique for simultaneous localization and mapping (SLAM) by aligning point clouds gathered over a short time scale using the iterative closest point (ICP) algorithm. To improve alignment, we have developed a system for smoothing these "submaps" and removing outliers. We integrate the constraints from submap alignment into a 6-DOF pose graph, which is optimized to estimate the full vehicle trajectory over the duration of the inspection task. We present real-world results using the Bluefin Hovering AUV, as well as analysis of a synthetic data set.

关键词： Autonomous underwater vehicles

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