  Robotic weed flaming is a new and environmentally friendly approach to weed
removal in the agricultural field. Using a mobile manipulator equipped with a
flamethrower, we design a new system and algorithm to enable effective weed
flaming, which requires robotic manipulation with a soft and deformable end
effector, as the thermal coverage of the flame is affected by dynamic or
unknown environmental factors such as gravity, wind, atmospheric pressure, fuel
tank pressure, and pose of the nozzle. System development includes overall
design, hardware integration, and software pipeline. To enable precise weed
removal, the greatest challenge is to detect and predict dynamic flame coverage
in real time before motion planning, which is quite different from a
conventional rigid gripper in grasping or a spray gun in painting. Based on the
images from two onboard infrared cameras and the pose information of the
flamethrower nozzle on a mobile manipulator, we propose a new dynamic flame
coverage model. The flame model uses a center-arc curve with a Gaussian
cross-section model to describe the flame coverage in real time. The
experiments have demonstrated the working system and shown that our model and
algorithm can achieve a mean average precision (mAP) of more than 76\% in the
reprojected images during online prediction.
