pith. sign in

arxiv: 2204.06367 · v2 · pith:SNJAZEFHnew · submitted 2022-04-13 · 📡 eess.SY · cs.RO· cs.SY

Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary Variables

classification 📡 eess.SY cs.ROcs.SY
keywords binaryvariablesencodingmicpapproachcomplexencodedlogic
0
0 comments X
read the original abstract

Signal Temporal Logic (STL) provides a convenient way of encoding complex control objectives for robotic and cyber-physical systems. The state-of-the-art in trajectory synthesis for STL is based on Mixed-Integer Convex Programming (MICP). The MICP approach is sound and complete, but has limited scalability due to exponential complexity in the number of binary variables. In this letter, we propose a more efficient MICP encoding for STL. Our new encoding is based on the insight that disjunction can be encoded using a logarithmic number of binary variables and conjunction can be encoded without binary variables. We demonstrate in simulation examples that our proposed approach significantly outperforms the state-of-the-art for long and complex specifications. Open-source software is available at https://stlpy.readthedocs.io.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Constrained Decoding for Safe Robot Navigation Foundation Models

    cs.RO 2025-09 unverdicted novelty 7.0

    SafeDec uses constrained decoding to ensure autoregressive robot navigation foundation models generate actions that provably satisfy STL safety specifications under assumed dynamics.

  2. Robustness-Based Synthesis for Time Window Temporal Logic Specifications via Mixed-Integer Linear Programming

    cs.RO 2026-06 unverdicted novelty 6.0

    Encodes TWTL robustness as MILP constraints for open-loop and closed-loop MPC synthesis of controls for linear systems, with a task-adaptive horizon.