Marker Based Row Alignment Control for an Agricultural Scouting Robot

Abstract

In recent years, autonomous robots have been gradually introduced into various agricultural operations to address the ever-increasing labor shortage problem. Accurate navigation from one row to another is one of the many challenging tasks for an autonomous robot scouting in semi-structured agricultural fields. In this study, a marker-based row alignment control is proposed for the cross-bed motion of a scouting robot in strawberry fields. Specifically, a feature-based computer vision algorithm is used to detect primitive markers placed at the end of each planting bed. Then the image coordinates of detected markers are used to guide the robot to move away from one row and then align with the next one. The proposed method is low cost and robust with respect to varying lighting conditions, and has been validated in a local strawberry farm.

Minimum‐time row transition control of a vision‐guided agricultural robot

“…The robot transits between rows at headlands, only relying on RGB cameras. As discussed earlier, our previous row transition (or cross‐bed) controllers follow a nonoptimal square trajectory (Freese & Xu, 2019; Li et al, 2020). Additionally, sands in a typical strawberry field are loose and tend to build up under the wheels when the robot is turning, causing both slippage and inconsistent turns.…”

“…As discussed in Freese and Xu (2019) and Li et al, (2020), navigation in a typical strawberry field consist of two phases, over‐bed and cross‐bed. Controllers corresponding to these two phases serve distinctive purposes using feedback information from different sets of sensors.…”

“…For a large portion of farmlands that have row crops, robot motion control normally consists of row‐wise translations (or over‐bed) and row transitions at headland (or cross‐bed) (Freese & Xu, 2019; Underwood et al, 2017). There is a rich literature on row‐wise translation in different types of farmlands (Ball et al, 2016; de Paula Veronese et al, 2016; Dong et al, 2011; Freese & Xu, 2019; Higuti et al, 2019; Le et al, 2020; Nagasaka et al, 2009; Schwendner et al, 2014; Underwood et al, 2015, 2017; van Henten et al, 2002; Winterhalter et al, 2021; Xiong et al, 2020); nevertheless, studies on row transition controls are limited (Ball et al, 2016; Freese & Xu, 2019; Le et al, 2020; Li et al, 2020; Nagasaka et al, 2009; Underwood et al, 2017; Xiong et al, 2020). In this paper, we present a real‐time subspace optimal control method that significantly improves the row transition performance of a field robot in a strawberry field.…”

“…Our previous work (Freese & Xu, 2019) uses the color pattern of a typical row‐based field to transit between rows, which is not robust in varying lighting conditions and often results in alignment errors when the rows are not perfectly straight. In Li et al (2020), the visual marker‐based cross‐bed motion is studied, which can mitigate the influence of changing lighting conditions. These two existing control schemes (Freese & Xu, 2019; Li et al, 2020) are not optimal.…”

“…In Li et al (2020), the visual marker‐based cross‐bed motion is studied, which can mitigate the influence of changing lighting conditions. These two existing control schemes (Freese & Xu, 2019; Li et al, 2020) are not optimal. It is worth noting that visual fiducial systems that provide measurements by visual markers have been widely used in robotics (Fiala, 2005; Kalaitzakis et al, 2021; Olson, 2011).…”