Authors:
Marc Fabregat-Jaén
1
;
Adrián Peidró
1
;
María Flores
1
;
Luis Payá
1
;
2
and
Óscar Reinoso
2
;
1
Affiliations:
1
Instituto de Investigación en Ingeniería de Elche, Universidad Miguel Hernández, Avda. Universidad s/n, Elche, Spain
;
2
ValgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Camí de Vera s/n, Valencia, Spain
Keyword(s):
Redundancy Resolution, RRT, MultiFM-RRT, Feasibility Maps, Redundant Manipulators.
Abstract:
This paper presents MultiFM-RRT, a novel algorithm for redundancy resolution in kinematically redundant manipulators based on the exploration of multiple Feasibility Maps (FMs). They encode all valid configurations of redundant variables for a prescribed task trajectory, enabling global optimization and constraint satisfaction. Unlike traditional velocity-based methods, which are limited to local solutions, and grid-based methods, which are computationally intensive, MultiFM-RRT leverages the Rapidly-exploring Random Tree (RRT) framework to efficiently explore the space of feasible solutions across multiple feasibility maps. The algorithm incorporates singularity maps to enable transitions between different aspects, ensuring comprehensive coverage of the solution space. By computing feasibility maps online and guiding exploration with probabilistic sampling of goal and singularity sets, MultiFM-RRT achieves a balance between computational efficiency and global optimality. The propose
d approach is demonstrated on a Stewart parallel platform, illustrating its ability to generate feasible, constraint-satisfying trajectories while efficiently handling redundancy.
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