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.
HighlightsHumans who supervise autonomous agricultural machines require some type of warning to perceive abnormal conditions in the machine or its environment.Visual and tactile warnings were the most suitable warning methods for in-field and close-to-field remote supervision.This study will help improve the performance of remote supervisors and minimize unexpected incidents or liabilities during operation of autonomous machines.Abstract. As agricultural machinery moves toward full autonomy, human supervisors will need to monitor the autonomous machines during operation and minimize system failures or malfunctions. However, to intervene in an emergency, the supervisor must first recognize the emergency in a timely manner. Existing warning devices rely on the human visual, auditory, and tactile senses. However, these warning methods vary in their ability to attract attention. Hence, it is important to determine which warning method is best suited to draw the attention of a remote supervisor of an autonomous machine in an emergency. To achieve this objective, participants were recruited and asked to interact with a simulation of an autonomous sprayer. Seven warning methods (presented alone or in combinations of visual, auditory, and tactile sensory cues) and four remote supervision scenarios (in-field, close-to-field, farm office, outside the farmland) were considered in this study. The findings revealed that a combination of tactile and visual methods was most suitable for in-field and close-to-field remote supervision, in comparison to the other warning methods. However, there was insufficient evidence to recommend the best warning methods for supervisors at the farm office or outside the farmland. This study will help improve the performance of remote supervisors and minimize unexpected incidents during field operations with autonomous agricultural machines. Keywords: Agricultural machines, Remote supervision, User-centered design, Warning system.
The concept of the driverless tractor has been discussed in the scientific literature for decades and several tractor manufacturers now have prototypes being field-tested. Although farmers will not be required to be physically present on these machines, it is envisioned that they will remain a part of the human-automation system. The overall efficiency and safety to be attained by autonomous agricultural machines (AAMs) will be correlated with the effectiveness of information sharing between the AAM and the farmer through what might be aptly called an automation interface. In this supervisory scenario, the farmer would be able to both receive status information and send instructions. In essence, supervisory control of an AAM is similar to the current scenario where farmers physically present on their machines obtain status information from displays integrated into the machine and from general sensory information that is available due to their proximity to the operating machine. Therefore, there is reason to expect that real-time sensory information would be valuable to the farmer when remotely supervising an AAM through an automation interface. This chapter will provide an overview of recent research that has been conducted on the role of real-time sensory information to the task of remotely supervising an AAM.
The scientific literature provides a description of various models depicting autonomous agricultural machines working to complete typical field operations. Many of the models involve some form of automation interface that is used by the machine owner to supervise the operation of the machine from a remote location. The objective of this study was to interview experts in the design of autonomous agricultural machines (university researchers, entrepreneurs, and leaders in the agricultural machinery sector) to ascertain their opinions about future autonomous agricultural machines, particularly related to how such machines will be supervised by the machine’s owner. Of the four remote supervision concepts described by participants (within the field, close to the field, from the farm office, and outside the farm site), the close-to-the-field remote supervision concept was determined to be the most viable concept. Designers were divided on the idea of providing real-time live video on the automation interface, however, most of them believed that having live video would reassure the farmer that everything was going well. Desktop computer, tablet and phone were the main devices recommended as tools for remote supervision (i.e., the hardware on which to display the automation interface), with tablet perhaps being the preferred alternative.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.