In many industrial applications, the robot is required
to perform a set of repetitive tasks without collision as
quickly as possible to maximize productivity. It is essential to
find an optimal sequence of collision-free motions to visit a set of
repetitive tasks and determine the optimal robot configuration
used to complete each task, which is formulated as the Robotic
Task Sequencing Problem (RTSP). In this paper, we propose
an approach based on a typical decoupling strategy to solve
RTSP efficiently. Firstly, the task execution sequence is obtained
by solving a TSP in task space and candidates of the optimal
configuration for each task are selected from the collision-free
configuration space according to the self-designed optimality
value derived from the similarity to the initial configuration in
configuration space. Then the optimal configuration for each
task is determined by finding the shortest path in a graph
that is constructed according to the task execution sequence
and optimal configuration candidates. Finally, collision-free
motion trajectories required for the robot to complete each
task with the optimal configuration are generated by running
a motion planning algorithm. Through a series of experiments,
we show that our approach outperforms the state-of-the-art
approaches when applied to the RTSP instances in a cluttered
3D environment, with up to 29.6% reduction in computation
time while providing comparable performance.