32 research outputs found
Control of Probabilistic Systems under Dynamic, Partially Known Environments with Temporal Logic Specifications
We consider the synthesis of control policies for probabilistic systems,
modeled by Markov decision processes, operating in partially known environments
with temporal logic specifications. The environment is modeled by a set of
Markov chains. Each Markov chain describes the behavior of the environment in
each mode. The mode of the environment, however, is not known to the system.
Two control objectives are considered: maximizing the expected probability and
maximizing the worst-case probability that the system satisfies a given
specification
Incremental Control Synthesis in Probabilistic Environments with Temporal Logic Constraints
In this paper, we present a method for optimal control synthesis of a plant
that interacts with a set of agents in a graph-like environment. The control
specification is given as a temporal logic statement about some properties that
hold at the vertices of the environment. The plant is assumed to be
deterministic, while the agents are probabilistic Markov models. The goal is to
control the plant such that the probability of satisfying a syntactically
co-safe Linear Temporal Logic formula is maximized. We propose a
computationally efficient incremental approach based on the fact that temporal
logic verification is computationally cheaper than synthesis. We present a
case-study where we compare our approach to the classical non-incremental
approach in terms of computation time and memory usage.Comment: Extended version of the CDC 2012 pape
Synthesis of provably correct controllers for autonomous vehicles in urban environments
This paper considers automatic synthesis of provably correct controllers for autonomous vehicles operating in an urban environment populated with static obstacles and live traffic. We express traffic rules such as collision avoidance, vehicle separation, speed limit, lane following, passing, merging and intersection precedence requirements in a formal specification language. Embedded control software synthesis is then applied to generate a controller that ensures that the vehicle obeys this set of traffic rules in any road and traffic conditions that satisfy certain assumptions
Distributed Traffic Signal Control for Maximum Network Throughput
We propose a distributed algorithm for controlling traffic signals. Our
algorithm is adapted from backpressure routing, which has been mainly applied
to communication and power networks. We formally prove that our algorithm
ensures global optimality as it leads to maximum network throughput even though
the controller is constructed and implemented in a completely distributed
manner. Simulation results show that our algorithm significantly outperforms
SCATS, an adaptive traffic signal control system that is being used in many
cities
Back-pressure traffic signal control with unknown routing rates
The control of a network of signalized intersections is considered. Previous
works proposed a feedback control belonging to the family of the so-called
back-pressure controls that ensures provably maximum stability given
pre-specified routing probabilities. However, this optimal back-pressure
controller (BP*) requires routing rates and a measure of the number of vehicles
queuing at a node for each possible routing decision. It is an idealistic
assumption for our application since vehicles (going straight, turning
left/right) are all gathered in the same lane apart from the proximity of the
intersection and cameras can only give estimations of the aggregated queue
length. In this paper, we present a back-pressure traffic signal controller
(BP) that does not require routing rates, it requires only aggregated queue
lengths estimation (without direction information) and loop detectors at the
stop line for each possible direction. A theoretical result on the Lyapunov
drift in heavy load conditions under BP control is provided and tends to
indicate that BP should have good stability properties. Simulations confirm
this and show that BP stabilizes the queuing network in a significant part of
the capacity region.Comment: accepted for presentation at IFAC 2014, 6 pages. arXiv admin note:
text overlap with arXiv:1309.648
Incremental Temporal Logic Synthesis of Control Policies for Robots Interacting with Dynamic Agents
We consider the synthesis of control policies from temporal logic
specifications for robots that interact with multiple dynamic environment
agents. Each environment agent is modeled by a Markov chain whereas the robot
is modeled by a finite transition system (in the deterministic case) or Markov
decision process (in the stochastic case). Existing results in probabilistic
verification are adapted to solve the synthesis problem. To partially address
the state explosion issue, we propose an incremental approach where only a
small subset of environment agents is incorporated in the synthesis procedure
initially and more agents are successively added until we hit the constraints
on computational resources. Our algorithm runs in an anytime fashion where the
probability that the robot satisfies its specification increases as the
algorithm progresses
Online Horizon Selection in Receding Horizon Temporal Logic Planning
Temporal logics have proven effective for correct-by-construction synthesis of controllers for a wide range of applications. Receding horizon frameworks mitigate the computational intractability of reactive synthesis for temporal logic, but have thus far been limited by pursuing a single sequence of short horizon problems to the current goal. We propose a receding horizon algorithm for reactive synthesis that automatically determines a path to the currently pursued goal at runtime, in response to a nondeterministic environment. This is achieved by allowing each short horizon to have multiple local goals, and determining which local goal to pursue based on the current global goal, currently perceived environment and a pre-computed invariant dependent on each global goal. We demonstrate the utility of this additional flexibility in grant-response tasks, using a search-and-rescue example. Moreover, we show that these goal-dependent invariants mitigate the conservativeness of the receding horizon approach
Situational reasoning for road driving in an urban environment
Robot navigation in urban environments requires situational reasoning.
Given the complexity of the environment and the behavior specified by traffic
rules, it is necessary to recognize the current situation to impose the correct
traffic rules. In an attempt to manage the complexity of the situational reasoning
subsystem, this paper describes a finite state machine model to govern the situational
reasoning process. The logic state machine and its interaction with the
planning system are discussed. The approach was implemented on Alice, Team
Caltech’s entry into the 2007 DARPA Urban Challenge. Results from the qualifying
rounds are discussed. The approach is validated and the shortcomings of
the implementation are identified