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Path planning for mobile robot aims to solve the problem of creating a collision‐free path from the start state to the goal state in the given space, which is a key supporting technology for unmanned work. In order to solve the problems of the asymptotic optimal rapidly extended random trees star (RRT*) algorithm, such as its slow convergence rate, the low efficiency of planning and the high cost of path, an improved motion planner (Fast‐RRT*) was proposed based on hybrid sampling strategy and choose parent based on backtracking. Firstly, the goal bias strategy and constraint sampling are combined in the sampling stage to reduce the blindness of sampling. Secondly, to obtain a path with lower cost than the RRT* algorithm, the ancestor of the nearest node is considered until the initial state in the process of choose parent for new node. To ensure the feasibility of the path, the path is smoothed by cubic B‐spline curve. The effectiveness of Fast‐RRT* algorithm was verified based on MATLAB and V‐rep platform. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper focuses on motion planning for mobile manipulators that can be used to plan both the base and the manipulator. An Optimized Hierarchical Mobile Manipulator Planner (OHMP) is proposed, which consists of two phases: two‐dimensional mobile base path planning and three‐dimensional manipulator trajectory planning. In the two‐dimensional mobile base path planning phase, a hybrid sampling strategy combining bridge test and uniform sampling is adopted to optimize Probabilistic Roadmap‐based basic planning and improve the efficiency of overall planning. In the three‐dimensional manipulator trajectory planning phase, if the current manipulator configuration is in a collision state, then a secure configuration is searched, and the trajectory between them is planned. Given start and goal configurations, the OHMP can efficiently produce a collision‐free path in complex environments. The estimates are backed up by the experiment result. It proves the validity and rationality of the planner, and it is effective for motion planning in complex static environments. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]