  It is well-known that a deep understanding of co-workers' behavior and
preference is important for collaboration effectiveness. In this work, we
present a method to accomplish smooth human-robot collaboration in close
proximity by taking into account the human's behavior while planning the
robot's trajectory. In particular, we first use an occupancy map to summarize
human's movement preference over time, and such prior information is then
considered in an optimization-based motion planner via two cost items as
introduced in [1]: 1) avoidance of the workspace previously occupied by human,
to eliminate the interruption and to increase the task success rate; 2)
tendency to keep a safe distance between the human and the robot to improve the
safety. In the experiments, we compare the collaboration performance among
planners using different combinations of human-aware cost items, including the
avoidance factor, both the avoidance and safe distance factor, and a baseline
where no human-related factors are considered. The trajectories generated are
tested in both simulated and real-world environments, and the results show that
our method can significantly increase the collaborative task success rates and
is also human-friendly. Our experimental results also show that the cost
functions need to be adjusted in a task specific manner to better reflect
human's preference.
