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 algorithm that considers reachability conditions jointly established from perception and manipulation task constraints. By minimizing expected energy usage in body key state planning while satisfying task constraints, our algorithm is able to find an energy-efficient trajectory with less body repositioning motion while ensuring the success of the task. 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.

Robotic weed flaming is a new and environmentally friendly approach to weed removal in the agricultural field. Using a mobile manipulator equipped with a blowtorch, 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 blowtorch 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.

“AWE” is a programmable “Animated Work Environment” supporting everyday human activities, at home, work and school, in an increasingly digital society. AWE features a novel robotic “wall,” three horizontal, reconfigurable work surfaces, and embedded information technologies. The video shows AWE as a digital simulation moving through six standard wall-desk configurations, interspersed with still photos and video clips of people interacting with the physical, full-scale, working prototype. The video also shows AWE beginning to behave intelligently as well as users fine-tuning AWE's configurations by gesturing proximity sensors mounted at the hinges between wall panels. Usability testing suggests that AWE clearly adapts to variations in complex activities involving users working or playing in a single physical space with both physical and digital tools and artifacts.