Rapidly-exploring random tree (RRT) has been studied for autonomous parking as it quickly finds an initial path and is easily scalable in complex environments. However, the planning time increases by searching for the path in narrow parking spots. To reduce the planning time, the target tree algorithm, which substitutes a parking goal in RRT with a set (target tree) of backward parking paths, was proposed. However, as it consists of circular and straight paths, it deteriorates parking accuracy because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the TargetTree-RRT* algorithm for complex environments. First, a target tree is designed using clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to initialize a proper target tree that considers obstacles. By integrating with optimal-variant RRT and searching for the shortest path, the proposed TargetTree-RRT* algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments showed that the vehicle parked more accurately, and continuous-curvature paths were obtained more quickly and with higher success rates than those acquired using other sampling-based and other types of planning algorithms. Note to Practitioners —This work was motivated by the need to develop a fast and practical path planning algorithm for autonomous parking, not only in simple environments but also in complex environments. Sampling-based planning algorithms have been studied in this area, with the advantages of rapid planning of an initial path and easily reflecting vehicle’s constraints. Nevertheless, in cluttered environments, it needs considerable time to obtain a path for the autonomous vehicle easy for tracking and precise parking. To address the aforementioned issue, this article presents the TargetTree-RRT* algorithm. It finds a path by replacing a parking goal with a set of pre-defined continuous-curvature paths considering cluttered environments. TargetTree-RRT* achieves better performance for real parking scenarios and rapidly obtains a near-optimal parking path, compared to other parking path planners. The proposed algorithm is not limited to autonomous vehicles. It can be applied to other unmanned vehicles or robots, such as autonomous underwater vehicles (AUVs). In any application where the goal of path planning can be replaced with predefined standardized paths, the proposed algorithm can be extended.

Robots with high Degrees of Freedom (DoF) such as humanoids and mobile manipulators are expected to perform multiple tasks simultaneously. Hierarchical Quadratic Programming (HQP) can effectively compute a solution for strictly prioritized tasks. However, the continuity of control input is not guaranteed when the priorities of the tasks are modified during operation. This paper proposes a continuous task transition method for HQP based controller to insert, remove, and swap arbitrary tasks without discontinuity. Smooth task transition is assured because our approach uses activation parameters of the new and existing tasks without modifying control structure.The proposed approach is applied to various task transition scenarios including joint limit, singularity, and obstacle avoidance to guarantee the stable execution of the robot. The proposed control scheme was implemented on a 7-DoF robotic arm, and its performance was demonstrated by the continuity of control input during various task transition scenarios.