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Temporal-Logic-Based Reactive Mission and Motion Planning

Kress-Gazit, H., Fainekos, G.E., Pappas, G.J.·2009·IEEE Transactions on Robotics

Abstract

This paper provides a frameworkto automaticallygenerate a hybrid controller thatguaranteesthat the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided. The desired task specifications, which are expressed in a fragment of linear temporal logic (LTL), can capture complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, which results in a novel paradigm for sensor-based temporal-logic-motion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multirobot specifications in a decentralized manner. Our computational approach is based on first creating discrete controllers satisfying specific LTL formulas. If feasible, the discrete controller is then used to guide the sensor-based composition of continuous controllers, which results in a hybrid controller satisfying the high-level specification but only if the environment is admissible.

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Metadata

Authors
Kress-Gazit, H., Fainekos, G. E., & Pappas, G. J.
Venue
IEEE Transactions on Robotics
Published
2009-12
Volume
25
Issue
6
Pages
1370-1381
ISSN
  • 1552-3098 (ISSN-L)
  • 1941-0468
OpenAlex
W2151958719
Cited by
750
References
0
FWCI
26.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Temporal-Logic-Based Reactive Mission and Motion Planning. https://app.cochat.ai/papers/10.1109/tro.2009.2030225
The paper
Kress-Gazit, H., Fainekos, G. E., & Pappas, G. J. (2009). Temporal-Logic-Based Reactive Mission and Motion Planning. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2009.2030225

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.