  Rapidly-exploring random tree (RRT) has been applied for autonomous parking
due to quickly solving high-dimensional motion planning and easily reflecting
constraints. However, planning time increases by the low probability of
extending toward narrow parking spots without collisions. To reduce the
planning time, the target tree algorithm was proposed, substituting a parking
goal in RRT with a set (target tree) of backward parking paths. However, it
consists of circular and straight paths, and an autonomous vehicle cannot park
accurately because of curvature-discontinuity. Moreover, the planning time
increases in complex environments; backward paths can be blocked by obstacles.
Therefore, this paper introduces the continuous-curvature target tree algorithm
for complex parking environments. First, a target tree includes clothoid paths
to address such curvature-discontinuity. Second, to reduce the planning time
further, a cost function is defined to construct a target tree that considers
obstacles. Integrated with optimal-variant RRT and searching for the shortest
path among the reached backward paths, the proposed algorithm obtains a
near-optimal path as the sampling time increases. Experiment results in real
environments show that the vehicle more accurately parks, and
continuous-curvature paths are obtained more quickly and with higher success
rates than those acquired with other sampling-based algorithms.
