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https://doi.org/10.1007/978-94...
Part of book or chapter of book . 2013 . Peer-reviewed
License: Springer Nature TDM
Data sources: Crossref
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An Improved Force Distribution Algorithm for Over-Constrained Cable-Driven Parallel Robots
Authors: Andreas Pott;
An Improved Force Distribution Algorithm for Over-Constrained Cable-Driven Parallel Robots
Abstract
In this paper we present an improved method to compute force distributions for cable-driven parallel robots. We modify the closed-from solution such that the region where a solution is found is extended almost to the theoretical maximum, i.e. the wrench-feasible workspace. At the same time continuity along trajectories as well as real-time efficiency are maintained. The algorithm’s complexity and thus the computational burden scales linearly in the number of redundant cables. Therefore, the algorithm can also be used for highly redundant cable robots. The proposed algorithm is compared to known methods and computational results are presented based on the IPAnema prototype.
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).73 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
73
Top 1%
Top 10%
Top 10%