Self-protective motion planning for mobile manipulators in a dynamic door-closing workspace


Purpose - Many work conditions require mobile manipulators to open cabinet doors and then gain access to the desired workspace. However, after opening, the unlocked doors can easily close, interrupt a task, and potentially break the operating end-effectors. This paper aims to address a mobile manipulator’s behavior planning problem for responding to a dynamic constrained workspace released by door-opening.

Design/methodology/approach - A dynamic model of the restricted workspace released by an unlocked cabinet door is established. As a whole system to treat, the interactions between the workspace and robot are analyzed by using a partially observable Markov decision process (POMDP). A self-protective policy decision executed as a belief tree is proposed. To respond to the policy decisions, we designed three types of actions: stay on guard in the workspace, using an elbow joint to defense the door, and linear escape out of the workspace for self-protection in the dynamic environment by observing collision risk levels to trigger them. Finally, we propose self-protective motion controllers based on risk time optimization to act to the planned actions.

Findings - The elbow defense action could balance robotic safety and work efficiency by interrupting the end-effector’s work and utilizing the elbow joint to prevent the door-closing in an active collision way. Compared with the stay on guard and linear escape action, the advantage of the elbow defense is having a predictable performance to quick callback the interrupted work after the risk was relieved.

Originality/value - This work provides guidance for the safe operation of a class of robot operations and the upgrade of motion planning.

Industrial Robot
Chuande Liu
Chuande Liu

My current research interests focus on sensory-based manipulation, robotic motion planning, AI-augmented visual servoing and under-actuated robot systems.