  This paper presents a search-based partial motion planner to generate
dynamically feasible trajectories for car-like robots in highly dynamic
environments. The planner searches for smooth, safe, and near-time-optimal
trajectories by exploring a state graph built on motion primitives, which are
generated by discretizing the time dimension and the control space. To enable
fast online planning, we first propose an efficient path searching algorithm
based on the aggregation and pruning of motion primitives. We then propose a
fast collision checking algorithm that takes into account the motions of moving
obstacles. The algorithm linearizes relative motions between the robot and
obstacles and then checks collisions by comparing a point-line distance.
Benefiting from the fast searching and collision checking algorithms, the
planner can effectively and safely explore the state-time space to generate
near-time-optimal solutions. The results through extensive experiments show
that the proposed method can generate feasible trajectories within milliseconds
while maintaining a higher success rate than up-to-date methods, which
significantly demonstrates its advantages.
