25 research outputs found
Time Window Temporal Logic
This paper introduces time window temporal logic (TWTL), a rich expressivity
language for describing various time bounded specifications. In particular, the
syntax and semantics of TWTL enable the compact representation of serial tasks,
which are typically seen in robotics and control applications. This paper also
discusses the relaxation of TWTL formulae with respect to deadlines of tasks.
Efficient automata-based frameworks to solve synthesis, verification and
learning problems are also presented. The key ingredient to the presented
solution is an algorithm to translate a TWTL formula to an annotated finite
state automaton that encodes all possible temporal relaxations of the
specification. Case studies illustrating the expressivity of the logic and the
proposed algorithms are included
Time window temporal logic
This paper introduces time window temporal logic (TWTL), a rich expressive language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are prevalent in various applications including robotics, sensor systems, and manufacturing systems. This paper also discusses the relaxation of TWTL formulae with respect to the deadlines of the tasks. Efficient automata-based frameworks are presented to solve synthesis, verification and learning problems. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. Some case studies are presented to illustrate the expressivity of the logic and the proposed algorithms
Formulation of control strategies for requirement definition of multi-agent surveillance systems
In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses.
In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.Ph.D
A Message Passing Strategy for Decentralized Connectivity Maintenance in Agent Removal
In a multi-agent system, agents coordinate to achieve global tasks through
local communications. Coordination usually requires sufficient information
flow, which is usually depicted by the connectivity of the communication
network. In a networked system, removal of some agents may cause a
disconnection. In order to maintain connectivity in agent removal, one can
design a robust network topology that tolerates a finite number of agent
losses, and/or develop a control strategy that recovers connectivity. This
paper proposes a decentralized control scheme based on a sequence of
replacements, each of which occurs between an agent and one of its immediate
neighbors. The replacements always end with an agent, whose relocation does not
cause a disconnection. We show that such an agent can be reached by a local
rule utilizing only some local information available in agents' immediate
neighborhoods. As such, the proposed message passing strategy guarantees the
connectivity maintenance in arbitrary agent removal. Furthermore, we
significantly improve the optimality of the proposed scheme by incorporating
-criticality (i.e. the criticality of an agent in its
-neighborhood).Comment: 9 pages, 9 figure
Robust satisfaction of temporal logic specifications via reinforcement learning
We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporally-layered task given as a signal temporal logic formula. We represent the system as a finite-memory Markov decision process with unknown transition probabilities and whose states are built from a partition of the state space. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given specification and to maximize the average expected robustness, i.e. a measure of how strongly the formula is satisfied. Robustness allows us to quantify progress towards satisfying a given specification. We demonstrate via a pair of robot navigation simulation case studies that, due to the quantification of progress towards satisfaction, reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness with a low number of training examples
Q-learning for robust satisfaction of signal temporal logic specifications
This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics. The system is modeled as a Markov decision process, in which the states represent partitions of a continuous space and the transition probabilities are unknown. We formulate two synthesis problems where the desired STL specification is enforced by maximizing the probability of satisfaction, and the expected robustness degree, that is, a measure quantifying the quality of satisfaction. We discuss that Q-learning is not directly applicable to these problems because, based on the quantitative semantics of STL, the probability of satisfaction and expected robustness degree are not in the standard objective form of Q-learning. To resolve this issue, we propose an approximation of STL synthesis problems that can be solved via Q-learning, and we derive some performance bounds for the policies obtained by the approximate approach. The performance of the proposed method is demonstrated via simulations
Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning
We consider the problem of steering a system with unknown, stochastic
dynamics to satisfy a rich, temporally layered task given as a signal temporal
logic formula. We represent the system as a Markov decision process in which
the states are built from a partition of the state space and the transition
probabilities are unknown. We present provably convergent reinforcement
learning algorithms to maximize the probability of satisfying a given formula
and to maximize the average expected robustness, i.e., a measure of how
strongly the formula is satisfied. We demonstrate via a pair of robot
navigation simulation case studies that reinforcement learning with robustness
maximization performs better than probability maximization in terms of both
probability of satisfaction and expected robustness.Comment: 8 pages, 4 figure