This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan.

A mobile manipulator is capable of traversing a vast area while performing manipulation tasks in confined spaces. However, the high degree of freedom presents a challenge for path planning. In this paper, a hybrid sampling-based path planning method is proposed for mobile manipulators performing pick and place tasks in confined spaces. This method employs a random sampling approach, yet differs from the traditional RRT method. Firstly, a sampling-based configuration generation method for mobile manipulators is proposed, with the objective of generating a valid, collision-free configuration with the end-effector at the desired pose. A path for the end-effector corresponding to the goal configuration is then planned using the RRT method. Secondly, an area-restricted approach that samples in the vicinity of the previous configuration is introduced to generate the next valid configuration. Subsequently, a cost computation rule is devised to identify the optimal subsequent configuration utilizing the trajectory of the end-effector as a guiding principle. Finally, the obtained path is smoothed. Simulations demonstrate that the proposed hybrid sample-based method is an effective solution to the path planning problem for mobile manipulators performing pick and place tasks in narrow spaces.