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Operations of transportation in cluttered environments require robust motion planning algorithms.
It recursively searches the regrasp graphs to find sequences of grasps and plans the motion between the grasps using motion planning algorithms.
In our work, the integrated assembly and motion planning problem are solved incrementally and recursively by searching regrasp graphs and invoking motion planning algorithms.
This paper presents two motion planning algorithms designed to solve two different guidance problems: the landing on a small body and its observation.
Theoretical and experimental evaluation of the proposed algorithms have been made and compared to the latest state of the art motion planning algorithms under different challenging environmental conditions and have proven their remarkable improvement in efficiency and convergence rate.
If the motion planning algorithms cannot find a path in the motion level, we roll back to the assembly level and try another candidate assembly sequence or try a different base until a solution is found or failure is reported.
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This paper introduces a novel motion planning algorithm for stochastic dynamic scenarios.
First of all, we address map merging using a motion planning algorithm.
Second, a motion planning algorithm is presented which is particular suited for this task.
In future work, the route could be decided automatically by applying a motion planning algorithm.
For the regularized system a Jacobian motion planning algorithm is designed, called imbalanced.
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