Human‐robot collaborative site‐specific sprayer

Berenstein, Ron; Edan, Yael

doi:10.1002/rob.21730

Cited by 42 publications

(25 citation statements)

References 29 publications

(57 reference statements)

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“…The expected number of usability problems for UIv0 was calculated to 42, which is above the average number of usability problems (35) observed in a rather mature interactive system (Nielsen and Landauer, 1993). In addition, a substantial number of problems (9) were rated as 3+ on a severity scale from 1 to 5. The average severity of the identified problems is characterized as medium (3.3).…”

Section: A First User Interface: Uiv0mentioning

confidence: 82%

“…Examples of such difficulties is moving on unstructured terrain, dealing with highly variable fruits that differ in size, color (even at the same plant), and environmental issues like shading and lighting. Autonomous robotic sprayers have been developed for weed control in field applications (Åstrand andBaerveldt, 2002, Kargar et al, 2013), trees in orchards (Endalew et al, 2011, Brown et al, 2008, and vineyards Edan, 2012, Berenstein andEdan, 2017). Selective spraying pesticides towards the targets, using a robot sprayer could reduce up to 30% of the pesticide (spraying material) while detecting and spraying 90% of the grape clusters (Berenstein et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Evaluation of the User Interface for a Semi-Autonomous Agricultural Robot Sprayer

Adamides¹

2020

AOL

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This study presents the heuristic evaluation, as a usability inspection method for Human-Robot Interaction (HRI) systems. First, the methodology to engineer a semi-autonomous agricultural robot sprayer is presented, and then findings from heuristic usability evaluation studies that were carried out on a human-robot interface for a semi-autonomous agricultural vineyard robot sprayer. The following research-based heuristics for the design of robot teleoperation were used: Platform architecture and scalability, Error prevention and recovery, Visual design, Information presentation, Robot state awareness, Interaction effectiveness and efficiency, Robot environment/surroundings awareness, and Cognitive factors. In each evaluation study, usability problems were identified, and specific suggestions were documented for HRI usability improvement. In each design iteration, a smaller number of usability issues were identified.. Results of the final heuristic evaluation showed that the system is at a good level of usability and is expected to provide satisfactory services to its typical users.

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Section: A First User Interface: Uiv0mentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Heuristic Evaluation of the User Interface for a Semi-Autonomous Agricultural Robot Sprayer

Adamides¹

2020

AOL

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“…Analytical and simulation approaches demonstrated that collaboration of human operator and robot can increase detection rates and decrease false alarms when compared to a human operator alone or a fully autonomous system. Implementation on an operational robotic sprayer indicated similar improved performance when a human collaborated with the robot.…”

Section: Background Informationmentioning

confidence: 93%

Design and development of a semi‐autonomous agricultural vineyard sprayer: Human–robot interaction aspects

Adamides

Katsanos

Constantinou

et al. 2017

Journal of Field Robotics

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This article presents the design aspects and development processes to transform a general‐purpose mobile robotic platform into a semi‐autonomous agricultural robot sprayer focusing on user interfaces for teleoperation. The hardware and the software modules that must be installed onto the system are described, with particular emphasis on human–robot interaction. Details of the technology are given focusing on the user interface aspects. Two laboratory experiments and two studies in the field to evaluate the usability of the user interface provide evidence for the increased usability of a prototype robotic system. Specifically, the study aimed to empirically evaluate the type of target selection input device mouse and digital pen outperformed Wiimote in terms of usability. A field experiment evaluated the effect of three design factors: (a) type of screen output, (b) number of views, (c) type of robot control input device. Results showed that participants were significantly more effective but less efficient when they had multiple views, than when they had a single view. PC keyboard was also found to significantly outperform PS3 gamepad in terms of interaction efficiency and perceived usability. Heuristic evaluations of different user interfaces were also performed using research‐based HRI heuristics. Finally, a study on participants’ overall user experience found that the system was evaluated positively on the User Experience Questionnaire scales.

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“…The agricultural robots are quite expensive and not widely used due to safety reasons, mechanical and industrial limitations. The pest sprayer robot lessens the exposure of human workers to pesticides, reducing medical hazards [56][57][58]. A high-resolution camera for a machine vision system along with accurate sensors and increased number of manipulators executing in parallel with human collaboration can progress the agricultural automation industry [59].…”

Section: Unmanned Ground Vehicles (Ugvs)mentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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Human‐robot collaborative site‐specific sprayer

Cited by 42 publications

References 29 publications

Heuristic Evaluation of the User Interface for a Semi-Autonomous Agricultural Robot Sprayer

Heuristic Evaluation of the User Interface for a Semi-Autonomous Agricultural Robot Sprayer

Design and development of a semi‐autonomous agricultural vineyard sprayer: Human–robot interaction aspects

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

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