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Solutions of example constrained motion planning problems for the rolling ball illustrate the theoretical concepts.
Over the years, solutions for motion planning problems were also found in artificial intelligence algorithms, such as neural networks and fuzzy logic.
Sampling-based, search-based, and optimization-based motion planners are just some of the different approaches developed for motion planning problems.
In the last fifteen years, sampling-based planners like the Probabilistic Roadmap Method (prm) have proved to be successful in solving complex motion planning problems.
In this work, a new methodology is proposed, that overcomes the above inefficiency through the simultaneous resolution of design and motion planning problems.
We propose to design new algorithms for motion planning problems using the well-known Domain Subdivision paradigm, coupled with "soft" predicates.
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Finally, we explore the dynamic motion planning problem in the VCS.
The kinematic motion planning problem for non-holonomic robotic systems is studied as an optimal control problem.
This paper addresses the constrained motion planning problem for nonholonomic systems represented by driftless control systems with output.
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 describes a solution to the multirobot motion planning problem based on a decoupled analysis in the space domain and in the time domain.
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