Incorporating artificial skin signals in the constraint-based reactive control of human–robot collaborative manipulation tasks

The purpose of this paper is to develop a control strategy for human–robot collaborative manipulation tasks that can deal with proximity signals from 373 interconnected cells of an artificial skin.

The robot and the operator accomplish an industrial task while interacting in a shared workspace. The robot controller detects and avoids collisions based on the information from the artificial skin. Conflicting constraints can be handled by prioritizing between hard and soft constraints or by weighing the different constraints.

Weak soft constraints (low weight) are specified to command the robot to move along a nominal path with constant velocity. Stronger soft constraints (higher weight) prevent collisions by means of either moving the end effector backward along the path or circumventing an obstacle. The proposed approach is validated experimentally.

As a first contribution, this paper proposes a discrete optimization algorithm activates an a priori selected maximum number of cells. The algorithm selects the appropriate distribution based on the amplitude of each signal and the spatial distribution of the proximity measurements. A second contribution is the specification of a human–robot collaborative application as an optimization problem using eTaSL (expression graph-based task specification language), which provides reactive control.