ROSep 8, 2021

Joint Search of Optimal Topology and Trajectory for Planar Linkages

arXiv:2109.03392v39 citations
AI Analysis

This provides a semi-automatic method for designing low-cost, modular robots and mechanical structures, reducing trial-and-error for engineers, though it is incremental as it builds on existing optimization techniques.

The paper tackles the problem of designing planar linkages that convert a single actuator's motion into a specified end-effector trajectory, proposing a hybrid algorithm combining MICP and MINLP that finds optimal linkages for 10-14 rigid links within a couple of hours, significantly outperforming simulated annealing in optimality.

We present an algorithm to compute planar linkage topology and geometry, given a user-specified end-effector trajectory. Planar linkage structures convert rotational or prismatic motions of a single actuator into an arbitrarily complex periodic motion, \refined{which is an important component when building low-cost, modular robots, mechanical toys, and foldable structures in our daily lives (chairs, bikes, and shelves). The design of such structures require trial and error even for experienced engineers. Our research provides semi-automatic methods for exploring novel designs given high-level specifications and constraints.} We formulate this problem as a non-smooth numerical optimization with quadratic objective functions and non-convex quadratic constraints involving mixed-integer decision variables (MIQCQP). We propose and compare three approximate algorithms to solve this problem: mixed-integer conic-programming (MICP), mixed-integer nonlinear programming (MINLP), and simulated annealing (SA). We evaluated these algorithms searching for planar linkages involving $10-14$ rigid links. Our results show that the best performance can be achieved by combining MICP and MINLP, leading to a hybrid algorithm capable of finding the planar linkages within a couple of hours on a desktop machine, which significantly outperforms the SA baseline in terms of optimality. We highlight the effectiveness of our optimized planar linkages by using them as legs of a walking robot.

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