Temporal-Logic-Based Reactive Mission and Motion Planning

By Hadas Kress-Gazit, Georgios Fainekos, George Pappas. IEEE Transactions on robotics, 2009

829 citations recorded by the source corpus.

This paper provides a framework to automatically generate a hybrid controller that guarantees that 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/642ef69cae7eb6eff384bae489ddea045e8fee38

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Citations in the corpus

KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, background): “, 2010; Kaelbling and Lozano-Perez, 2011), and the generation of robot controllers from logical constraints (Kress-Gazit et al., 2009).”
Safe Control under Uncertainty (2015, background): “, and has shown promising results in robotics applications [32, 23, 25, 43, 16].”
Let's talk: Autonomous conflict resolution for robots carrying out individual high-level tasks in a shared workspace (2015, methodology): “The algorithm is demonstrated with simulated and physical robots in the lab.”
Environment-Independent Task Specifications via GLTL (2017, background): “The results include open-loop and reactive control of deterministic, stochastic or non-deterministic finite-state models as well as continuous state models through appropriate finite-state abstractions (Wongpiromsarn et al., 2012; Kress-Gazit et al., 2009; Liu et al., 2013; Wolff et al., 2012; Ding ”
Control Barrier Functions for Signal Temporal Logic Tasks (2019, methodology): “It holds that b(x(0), 0) ≥ 0, b1(x, t1) = 5 − ‖x − [ 10 0 ]T ‖ = h1(x), and b2(x, t′) ≤ h2(x) for all t′ ∈ [5, 15].”
Safe Control under Uncertainty with Probabilistic Signal Temporal Logic (2016, background): “Given x0, and a finite length input sequence: uH = u0, u1, . . . , uH−1, the finite horizon run or trajectory of the system following the dynamics in equation (2) is ξH(x0,uH) = (x0, u0), (x1, u1), . . . , (xH , uH).”
Language-Conditioned Imitation Learning for Robot Manipulation Tasks (2020, background): “Another popular methodology for providing such semantic information is to use formal speciﬁcation languages such as temporal logic [19, 28].”
Autonomous driving in urban environments: approaches, lessons and challenges (2010, methodology): “…navigator that speciﬁes a goal point and a motion planner that ﬁnds a feasible path to the goal using a unique sampled-based approach called closed-loop rapidly exploring random tree (Kuwata et al. 2009), while the Cornell and Caltech teams used optimization based path planners (Hardy et al. 2008).”