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In various automated machines currently used in agriculture and field applications, the operator still retains responsibility for numerous other tasks the machines perform. Even with completely autonomous machines of the future, ergonomics will continue to play an important role for the overall system performance. This chapter on human-machine interactions, therefore, covers four distinct topics around ergonomics and how humans interact with machines. In the first section, readers can expect to gain an understanding of human-machine interaction associated with agricultural machines. This discussion will be followed by some description of the tools for assessing human-machine interaction. The third section will discuss the progression of technologies that have been used to support the operator of an agricultural machine up to and including fully autonomous agricultural machines. In the final section, future challenges associated with remote supervision of autonomous agricultural machines will be discussed.
Danny Mann. Human-Machine Interactions. Agriculture Automation and Control 2021, 387 -414.
AMA StyleDanny Mann. Human-Machine Interactions. Agriculture Automation and Control. 2021; ():387-414.
Chicago/Turabian StyleDanny Mann. 2021. "Human-Machine Interactions." Agriculture Automation and Control , no. : 387-414.
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.
Uduak Edet; Daniel Mann. Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines. Applied Sciences 2020, 10, 2794 .
AMA StyleUduak Edet, Daniel Mann. Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines. Applied Sciences. 2020; 10 (8):2794.
Chicago/Turabian StyleUduak Edet; Daniel Mann. 2020. "Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines." Applied Sciences 10, no. 8: 2794.
Wheelchair users who live in cold climates are faced with daily difficulties related to personal independence and societal inclusion as their assistive devices are unable to overcome the physical barriers created by snow and ice. The purpose of the research was to evaluate four commercially available casters to determine which caster performed best on snow-covered surfaces. Performance measures included: travel time, force transfer through the palms of the hands, number of propulsive movements, static resistance to movement, kinetic resistance to movement, and caster penetration into the packed snow. On a snow-covered incline, the FreeWheel™ caster enabled travel time to be decreased by 10 s, requiring 3 fewer propulsive movements and 60% of the amount of force to propel the wheelchair compared with solid casters. Static and kinetic resistance tests did not differentiate the four caster types. Penetration into packed snow was reduced from 11.9 mm to approximately 1 mm by changing from solid casters to the FreeWheel™ or Wheelblades™ caster types on flat surfaces. Similar results were observed on a snow-covered incline for the Wheelblades™, however, the FreeWheel™ penetrated approximately 8 mm. Considering the entire body of evidence, the FreeWheel™ performed the best on snow-covered surfaces.
M. Berthelette; D. D. Mann; J. Ripat; C. M. Glazebrook. Assessing manual wheelchair caster design for mobility in winter conditions. Assistive Technology 2018, 32, 31 -37.
AMA StyleM. Berthelette, D. D. Mann, J. Ripat, C. M. Glazebrook. Assessing manual wheelchair caster design for mobility in winter conditions. Assistive Technology. 2018; 32 (1):31-37.
Chicago/Turabian StyleM. Berthelette; D. D. Mann; J. Ripat; C. M. Glazebrook. 2018. "Assessing manual wheelchair caster design for mobility in winter conditions." Assistive Technology 32, no. 1: 31-37.