IEEE Robotics & Automation Magazine - September 2021 - 43

rows in a single step, it is robust against outliers like weeds
growing between the crop rows and yields accurate results
during turning, i.e., when the robot is not necessarily aligned
with the crop rows.
We integrated the output from our pattern Hough transform
into the localization module of the BoniRob. The localization
was based on an extended Kalman filter. We fused the
odometry and IMU measurements for the prediction. In the
correction step, we aligned the detected crop row pattern
with a GPS-referenced map of crop rows to correct the pose
estimate of the robot relative to the field. Because the crop
row pattern provides only lateral and orientation information,
i.e., no correction along the crop rows, we corrected the
longitudinal position estimate using GPS signals.
We implemented a global planner based on a state lattice
planner to ensure that the BoniRob found a path to any
reachable pose in the field. The BoniRob can change its track
width by adjusting the angles of the lever arms to which the
wheels are attached, as shown in Figure 2(a). Thus, whether it
can pass through a narrow gap or over an obstacle depends
on the wheel positions [see Figure 14(d)]. We developed a
path planner that considers the lever angles explicitly [35] by
including the arm angles in the state space and adding actions
that allow the planner to change them. Adding the arm angles
greatly increases the size of the state space, which makes planning
with commonly used search algorithms inefficient.
Thus, we introduced a novel method to represent the robot
state with a reduced cardinality; that is, we tracked valid armangle
intervals instead of single-arm angles in the robot state.
Data
Features
y
l3
l2
l1
l0
(a)
Our local planner translates a pose path from the global
planner into velocities while considering steering constraints.
Any robot with slow-turning, independently steerable wheels,
such as the BoniRob, has certain steering constraints. The
most prominent constraints are limited steering velocity, noncontinuous
steering, or wheel-angle instabilities when the
center of rotation is on a wheel. To avoid violations of these
steering constraints, we present a new approach that incorporates
steering constraints when generating velocity rollouts
[see Figure 14(f)] [36]. Our approach leverages the
correspondence between the wheel angles and the instantaneous
center of rotation (ICR) of the robot. After projecting
the steering constraints into the ICR space, we
computed a valid ICR path that satisfies the constraints.
From this ICR path, we calculated valid velocity sequences
that the robot can execute smoothly. Real-world experiments
show that our local planner improves efficiency and
leads to smoother execution.
UAV-UGV Mission Coordination
To unlock the potential of the Flourish robotics system, the
ability to run coordinated missions between the robots is
essential. Because both robots share information via Wi-Fi,
this information needs to be kept at a minimal level, and the
coordination needs to be ensured even when communication
is lost. The only data exchanged are the UAV and UGV
poses, the coordinates of the areas of interest, the requests
from one robot to the other, and their status messages.
Because of the lossy communication, exchanging requests
c
r1
θ
o
(b)
x
(c)
s
(d)
(e)
(f)
Figure 14. An overview of the UGV navigation system. (a) The pattern Hough transform detects crop rows in lidar or camera data
using the extracted plant features. (b) A pattern is defined as a set of parallel and equidistant lines (red) with orientation ,i offset o,
and spacing s. (c) The result of the pattern Hough transform (red) on pointed cabbage (10cm).
.
(d) The valid arm-angle intervals
when moving close to an obstacle. (e) The velocity rollouts in the local planner. The rollouts are color-coded with their respective
costs. (f) The ICR constraints derived from the hardware constraints (red) and the maximum steering velocity (green).
SEPTEMBER 2021 * IEEE ROBOTICS & AUTOMATION MAGAZINE *
43
Camera
Lidar

IEEE Robotics & Automation Magazine - September 2021

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - September 2021

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