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Stentz, Anthony; Dima, Cristian; Wellington, Carl; Herman, Herman; Stager, David

doi:10.1023/a:1015634322857

Cited by 111 publications

(14 citation statements)

References 16 publications

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“…Forschungsarbeiten zu autonomen Traktoren, beispielsweise in Stentz et al (2002) und Oksanen (2015) sind mittlerweile in produktnahe Entwicklungen übergegangen. Firmen wie Case IHC (2017) und New Holland (2017 haben in Zusammenarbeit mit der Firma ASI entsprechende Lösungen gezeigt.…”

Section: Automatisierung Bisheriger Traktorkonzepte Mit Oder Ohne Kabineunclassified

Mit autonomen Landmaschinen zu neuen Pflanzenbausystemen

Minßen

Urso

Gaus³

et al. 2015

ATZ Offhighway

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Section: Automatisierung Bisheriger Traktorkonzepte Mit Oder Ohne Kabineunclassified

Mit autonomen Landmaschinen zu neuen Pflanzenbausystemen

Minßen

Urso

Gaus³

et al. 2015

ATZ Offhighway

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“…Since then, several systems have been proposed for detecting and avoiding obstacles (Cho and Lee, 2000;Stentz et al, 2002;Griepentrog et al, 2009;Moorehead et al, 2012;Emmi et al, 2014;Ball et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

et al. 2018

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Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multimodal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.

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“…Previous studies [7][8][9] have suggested that video footage is essential to supervisors when monitoring a machine remotely. The authors of [10] proposed a system architecture to enable control of an autonomous tractor that included a "coordinating process", which was to be handled by a coordinator located remotely in a farm office.…”

Section: Introductionmentioning

confidence: 99%

Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines

Edet

Mann

2020

Applied Sciences

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A study to determine the visual requirements for a remote supervisor of an autonomous sprayer was conducted. Observation of a sprayer operator identified 9 distinct “look zones” that occupied his visual attention, with 39% of his time spent viewing the look zone ahead of the sprayer. While observation of the sprayer operator was being completed, additional GoPro cameras were used to record video of the sprayer in operation from 10 distinct perspectives (some look zones were visible from the operator’s seat, but other look zones were selected to display other regions of the sprayer that might be of interest to a sprayer operator). In a subsequent laboratory study, 29 experienced sprayer operators were recruited to view and comment on video clips selected from the video footage collected during the initial ride-along. Only the two views from the perspective of the operator’s seat were rated highly as providing important information even though participants were able to identify relevant information from all ten of the video clips. Generally, participants used the video clips to obtain information about the boom status, the location and movement of the sprayer within the field, the weather conditions (especially the wind), obstacles to be avoided, crop conditions, and field conditions. Sprayer operators with more than 15 years of experience provided more insightful descriptions of the video clips than their less experienced peers. Designers can influence which features the user will perceive by positioning the camera such that those specific features are prominent in the camera’s field of view. Overall, experienced sprayer operators preferred the concept of presenting visual information on an automation interface using live video rather than presenting that same information using some type of graphical display using icons or symbols.

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Untitled

Cited by 111 publications

References 16 publications

Mit autonomen Landmaschinen zu neuen Pflanzenbausystemen

Mit autonomen Landmaschinen zu neuen Pflanzenbausystemen

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines

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