The optimal design of cable-driven robots
Date
2016
Authors
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Journal ISSN
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Publisher
University of Delaware
Abstract
Cable-driven robots are often considered an attractive solution for a range of applications, however, the advantages of cable-driven robots come at a cost of added complexity to the overall system and increased challenges in the design of the robot. The effect of cable tensions on the robot varies significantly based on robot pose and cable architecture. Additionally, a cable can only apply force in one direction as tension, and this uni-directional actuation complicates the design of cable-driven robots and can result in limited performance. The choice of cable architecture has a significant effect on the capabilities and performance of the robot, with a poor choice of cable routing resulting in significant performance limitations based on operational workspace size and high cable tensions required to perform a desired task.
This work develops a set of methodologies to inform the design of cable-driven robots to identify the best cable architecture. A robot performance analysis methodology is presented to analyze and rank robot designs based on the robot workspace performance and cable tensions. Two optimization methodologies are then presented which leverage established design optimization techniques combined with this robot performance analysis methodology to identify a cable routing which optimizes the robot performance.
The first optimization strategy makes use of research in probability theory and the theory of randomized algorithms to develop a methodology which uses a set of randomly selected robot designs to find a near-optimal solution, and quantifies the accuracy and uncertainties of this result. This approach is advantageous because the uncertainty bounds of the result can be used to inform a determination of the sufficiency of the near-optimal solution based on the particular design problem requirements. The algorithm is relatively simple to implement, and an acceptable solution to the design problem can be found with very little effort or computation. However, for design problems with large design spaces which require a solution very close to the true optimal, this approach can require large sample sets to locate a solution within the desired uncertainty bounds.
A second optimization methodology is developed for robot design problems which have a large, high-dimensional design space and which require a solution close to the true optimal. This strategy uses a statistical guided-search algorithm to explore the design space and converge on the global optimal solution, based on ranking criteria derived from the robot performance analysis methodology. This methodology is well suited to cable-driven robot optimization in that it is applicable to problems with continuous design parameters, and is robust to discontinuous objective functions with many local optima. This approach requires more effort to implement than the first optimization strategy, but is more efficient at exploring large, high dimensional design spaces.
Finally, a methodology is presented to analyze the robustness of the performance of an optimal solution to small errors in the configuration parameters which arise in the implementation of a design. Depending on the expected precision of implementation, a near-optimal design may be preferable to the optimized solution if the performance of near-optimal design is more robust to errors and more reliably implemented into a physical system.