Door Opening Strategy for Mobile Manipulator with Constrained Configuration
We address the task of robotic door opening in office environments. This task is important for providing indoor mobility for collaborative mobile ma-nipulators. In our work, we mainly focus on the use of high-level control opportunities and identification of the door parameters from visual and lidar data. We develop a solution, which includes handle recognition, handle twisting, and opening. The position of the handle is identified from stereo images by a neural-network-based method. We divide the opening procedure into two stages: first, handle twisting and slightly opening, and second, wide opening. The first stage is implemented via high-level task-space control of the robotic arm, while the platform is static. The position of the door axis is identified during the slight opening by fitting lidar data to the kinematic model. At the second stage, both the platform and the arm are active. The trajectory of the platform is defined by the model predictive planner in such a way that it avoids pushing the arm into a singular configuration, while the manipulator is operated via high-level impedance control. In our experi-ments, a mobile manipulator composed from the wheeled platform and the robotic arm was able to open office doors using the proposed approach.