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Autonomous robot‐based finishing of surfaces with a reduction of the programming effort can be achieved by teaching the desired trajectory locally in the object reference frame. Thus, the flexibility of the programmed task increases and also moving surfaces can be finished. This paper aims to focus on this control concept.

The developed concept relies both on the use of a new slip sensor which is able to measure relative motion between the robot end‐effector and the machined object surface as well as on a continuous slip and force control algorithm. First experimental results were used to validate the concept.

The presented results were promising enough to encourage the application of the proposed concept scheme in connection with the slip sensor in industrial finishing applications.

The first investigations provide a basis for the development of more accurate software solutions in order to optimise the performances of the slip sensor.

The developed slip sensor provides a cheap and flexible solution for measuring relative motion between tool and surface. Combined with the use of a force sensor, the proposed scheme can be introduce more autonomy in industrial application like polishing or deburring.

The paper introduces a novel slip and force control concept for coping with the industry requirement of introducing more automation in the finishing of surfaces. Such a control concept allows on one hand the finishing of moving parts and at the same time increases the flexibility of the programming and reduces the user effort.

Many work conditions require 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 manipulator's behavior planning problem for responding to a dynamic workspace released by door opening.

A dynamic model of the restricted workspace released by an unlocked 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. A self-protective policy decision executed as a belief tree is proposed. To respond to the policy, this study has 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 by observing collision risk levels to trigger them. Finally, this study proposes self-protective motion controllers based on risk time optimization to act to the planned actions.

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

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