Toward Precise Robotic Weed Flaming Using a Mobile Manipulator with a Flamethrower

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.

Coupled Active Perception and Manipulation Planning for a Mobile Manipulator in Precision Agriculture Applications

A mobile manipulator often finds itself in an application where it needs to take a close-up view before performing a manipulation task. Named this as a coupled active perception and manipulation (CAPM) problem, we model the uncertainty in the perception process and devise a key state/task planning approach that considers reachability conditions as task constraints of both perception and manipulation tasks for the mobile platform. By minimizing the expected energy usage in the body key state planning while satisfying task constraints, our algorithm achieves the best balance between the task success rate and energy usage. We have implemented the algorithm and tested it in both simulation and physical experiments. The results have confirmed that our algorithm has a lower energy consumption compared to a two-stage decoupled approach, while still maintaining a success rate of 100\% for the task.