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2023 (English)In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 8621-8627Conference paper, Published paper (Refereed)
Abstract [en]
Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatio-temporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly Exploring Random Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Real-Time Planning, Sampling-based Motion Planning, Signal Temporal Logic
National Category
Robotics and automation
Identifiers
urn:nbn:se:kth:diva-350253 (URN)10.1109/IROS55552.2023.10341993 (DOI)001136907802112 ()2-s2.0-85177884865 (Scopus ID)
Conference
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Detroit, United States of America, Oct 1 2023 - Oct 5 2023
Note
Part of ISBN 9781665491907
QC 20240710
2024-07-102024-07-102025-02-09Bibliographically approved