Joint Search of Optimal Topology and Trajectory for Planar Linkages
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Abstract (arXiv)
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, refinedwhich 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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2021.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesiscited by 7
- 2025MechaFormer: Sequence Learning for Kinematic Mechanism Design Automationcited by 1
- 2025Creative synthesis of kinematic mechanismscited by 1
- 2026LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesiscited by 0
- 2026LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis7 Jan 2026
- 2025Creative synthesis of kinematic mechanisms30 Sep 2025
- 2025MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation12 Aug 2025
- 2024LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis31 May 2024