99,086 research outputs found
Synthesizing a Lego Forklift Controller in GR(1): A Case Study
Reactive synthesis is an automated procedure to obtain a
correct-by-construction reactive system from a given specification. GR(1) is a
well-known fragment of linear temporal logic (LTL) where synthesis is possible
using a polynomial symbolic algorithm. We conducted a case study to learn about
the challenges that software engineers may face when using GR(1) synthesis for
the development of a reactive robotic system. In the case study we developed
two variants of a forklift controller, deployed on a Lego robot. The case study
employs LTL specification patterns as an extension of the GR(1) specification
language, an examination of two specification variants for execution
scheduling, traceability from the synthesized controller to constraints in the
specification, and generated counter strategies to support understanding
reasons for unrealizability. We present the specifications we developed, our
observations, and challenges faced during the case study.Comment: In Proceedings SYNT 2015, arXiv:1602.0078
A Distributed Scheduling Algorithm to Provide Quality-of-Service in Multihop Wireless Networks
Control of multihop Wireless networks in a distributed manner while providing
end-to-end delay requirements for different flows, is a challenging problem.
Using the notions of Draining Time and Discrete Review from the theory of fluid
limits of queues, an algorithm that meets delay requirements to various flows
in a network is constructed. The algorithm involves an optimization which is
implemented in a cyclic distributed manner across nodes by using the technique
of iterative gradient ascent, with minimal information exchange between nodes.
The algorithm uses time varying weights to give priority to flows. The
performance of the algorithm is studied in a network with interference modelled
by independent sets
TarTar: A Timed Automata Repair Tool
We present TarTar, an automatic repair analysis tool that, given a timed
diagnostic trace (TDT) obtained during the model checking of a timed automaton
model, suggests possible syntactic repairs of the analyzed model. The suggested
repairs include modified values for clock bounds in location invariants and
transition guards, adding or removing clock resets, etc. The proposed repairs
are guaranteed to eliminate executability of the given TDT, while preserving
the overall functional behavior of the system. We give insights into the design
and architecture of TarTar, and show that it can successfully repair 69% of the
seeded errors in system models taken from a diverse suite of case studies.Comment: 15 pages, 7 figure
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
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