| 1. |
- Ahtiainen, Juhana, et al.
(författare)
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Normal Distributions Transform Traversability Maps : LIDAR-Only Approach for Traversability Mapping in Outdoor Environments
- 2017
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Ingår i: Journal of Field Robotics. - : John Wiley & Sons. - 1556-4959 .- 1556-4967. ; 34:3, s. 600-621
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Tidskriftsartikel (refereegranskat)abstract
- Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.
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| 2. |
- Almqvist, Håkan, 1984-, et al.
(författare)
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Learning to detect misaligned point clouds
- 2018
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Ingår i: Journal of Field Robotics. - : John Wiley & Sons. - 1556-4959 .- 1556-4967. ; 35:5, s. 662-677
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Tidskriftsartikel (refereegranskat)abstract
- Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.
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| 3. |
- Arad, Boaz, et al.
(författare)
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Development of a sweet pepper harvesting robot
- 2020
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Ingår i: Journal of Field Robotics. - : John Wiley & Sons. - 1556-4959 .- 1556-4967. ; 37:6, s. 1027-1039
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB-D camera, high-end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end-user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4-week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.
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| 4. |
- Balakirsky, Stephen, et al.
(författare)
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Towards Heterogeneous Robot Teams for Disaster Mitigation : Results and Performance Metrics from Robocup Rescue
- 2007
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Ingår i: Journal of Field Robotics. - : Wiley. - 1556-4959 .- 1556-4967. ; 24:11, s. 943-967
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Tidskriftsartikel (refereegranskat)abstract
- Urban Search And Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi-robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCup Rescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real-world validation results on the engine and algorithms are offered.
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| 5. |
- Bhat, Sriharsha, 1991-, et al.
(författare)
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Nonlinear model predictive control for hydrobatics : Experiments with an underactuated AUV
- 2023
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Ingår i: Journal of Field Robotics. - : Wiley. - 1556-4959 .- 1556-4967. ; 40:7, s. 1840-1859
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Tidskriftsartikel (refereegranskat)abstract
- Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.
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| 8. |
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| 9. |
- Folkesson, John, 1960-, et al.
(författare)
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Graphical SLAM for Outdoor Applications
- 2007
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Ingår i: Journal of Field Robotics. - : John Wiley & Sons. - 1556-4959 .- 1556-4967. ; 24:1-2, s. 51-70
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Tidskriftsartikel (refereegranskat)abstract
- Application of SLAM outdoors is challenged by complexity, handling of non-linearities and flexible integration of a diverse set of features. A graphical approach to SLAM is introduced that enables flexible data-association. The method allows for handling of non-linearities. The method also enables easy introduction of global constraints. Computational issues can be addressed as a graph reduction problem. A complete framework for graphical based SLAM is presented. The framework is demonstrated for a number of outdoor experiments using an ATRV robot equipped with a SICK laser scanner and a CrossBow Inertial Unit. The experiments include handling of large outdoor environments with loop closing. The presented system operates at 5Hz on a 800 MHz computer.
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| 10. |
- Grelsson, Bertil, et al.
(författare)
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GPS-level accurate camera localization with HorizonNet
- 2020
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Ingår i: Journal of Field Robotics. - : WILEY. - 1556-4959 .- 1556-4967. ; 37:6, s. 951-971
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degrees panoramic image around the USV. We design a convolutional neural network (CNN) architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model data in the Fourier domain using a minimum output sum of squared error correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in field trials conducted over 3 days in the archipelago. Our approach provides excellent results by achieving robust position estimates with global positioning system (GPS)-level accuracy in previously unvisited test areas.
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