  Industrial manipulators have extensively collaborated with human operators to
execute tasks, e.g., disassembly of end-of-use products, in intelligent
remanufacturing. A safety task execution requires real-time path planning for
the manipulator's end-effector to autonomously avoid human operators. This is
even more challenging when the end-effector needs to follow a planned path
while avoiding the collision between the manipulator body and human operators,
which is usually computationally expensive and limits real-time application.
This paper proposes an efficient hybrid motion planning algorithm that consists
of an A$^*$ algorithm and an online manipulator reconfiguration mechanism
(OMRM) to tackle such challenges in task and configuration spaces respectively.
The A$^*$ algorithm is first leveraged to plan the shortest collision-free path
of the end-effector in task space. When the manipulator body is risky to the
human operator, our OMRM then selects an alternative joint configuration with
minimum reconfiguration effort from a database to assist the manipulator to
follow the planned path and avoid the human operator simultaneously. The
database of manipulator reconfiguration establishes the relationship between
the task and configuration space offline using forward kinematics, and is able
to provide multiple reconfiguration candidates for a desired end-effector's
position. The proposed new hybrid algorithm plans safe manipulator motion
during the whole task execution. Extensive numerical and experimental studies,
as well as comparison studies between the proposed one and the state-of-the-art
ones, have been conducted to validate the proposed motion planning algorithm.
