We address the motion planning (MP) problem in real world robotic exploration and inspection tasks, where robot localization and mapping uncertainties have to be incorporated into the planned motions. The robot considered in this work is a mobile manipulator system, which combines the mobility of the base with the dexterousness of the manipulator. The first part of this work considers localization and mapping uncertainties in the motion planning problem. We propose RRT-SLAM, which uses a rapidly exploring random tree (RRT) in conjunction with a simulated particle based simultaneous localization and mapping (SLAM) algorithm to expand the tree. The simulated SLAM explicitly accounts for localization and mapping uncertainties in the planning stage. Moreover, the RRT itself is represented in the uncertainty-configuration space (UC-space), which is an augmented configuration space with an extra dimension of uncertainty. The collision probability along a planned path is explicitly calculated and is used to select a planned path. We further address the efficiency of the RRT-SLAM in the UC-space. We treat the issue from a data clustering point of view and propose a fractal dimension (FD) based checking criterion for efficient node generation,and demonstrate the positive results in simulations. The second part of this research addresses planning motions for the manipulator with the base staying stationary. Therefore, no sensor observation is available during the motion. We extend the probabilistic roadmap (PRM) algorithm to plan motions. Since the base pose of the manipulator is not precisely known due to the localization uncertainty, the path query of the roadmap becomes a constrained shortest path problem. We prove that this path query is an NP-hard problem, and propose an lazy path query algorithm that judiciously combines a k-shortest path algorithm with a labeling algorithm to achieve efficiency. The RRT-SLAM is tested on an actual PowerBot and the experimental results show the effectiveness and benefits of our integrated approach. We also implemented and tested our integrated planner for the mobile manipulator in simulations. The effectiveness of the combined integrated planner is demonstrated via these simulations.
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