Optimal Temporal Logic Control for Deterministic Transition Systems With Probabilistic Penalties

Svoreňová, Mária; Černá, Ivana; Belta, Călin

doi:10.1109/tac.2014.2381451

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This however converts the problem into a graph search problem over the product automaton. In the work of Svorenova et al [11], the notion of probabilities is taken in addition, over which an exhaustive search finds the optimal solution while considering the probabilities. In another recent work, Ulusoy et al [12] used product automaton to plan for multiple robots optimally.…”

Section: Related Workmentioning

confidence: 99%

“…This trajectory and constraints are put into Eqs. (10) and (11) to get the optimal path. In other words, instead of directly computing the robot sequences τ r , the optimizer calculates the task sequences τ task ψ at one place and simultaneously fuses task sequences τ task ψ to produce the robot sequences τ r , at the other place.…”

Section: Solving By Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-robot mission planning using evolutionary computation with incremental task addition

Kala

2021

Intel Serv Robotics

View full text Add to dashboard Cite

Mission planning asks the robot to solve complex missions, requiring the robot to execute several actions such that a complex goal condition (called mission) is satisfied. The missions can be specified by using Linear Temporal Logic or any custom domain-specific language. Here, we consider service robots in the home or office environments with multiple users, each specifying a task that together makes a mission. Conventional mission planning techniques assume that the complete mission specification is known in advance, while mission planning is incremental wherein tasks keep getting added and deleted. The exponential computational complexity of the current model verification-based approaches is not a viable solution. The paper presents an evolutionary framework for incrementally solving the problem of mission planning for the problems for which a polynomial-time verification system exists for the tasks. The optimal solution is represented in an encoding-specific format that is compiled to form a linear trajectory of the robots. Optimization is done as the robot operates. Thus, the robot at any time has a partial solution to every task that has already been executed that (along with the robot assigned to the task) cannot be altered, while a part representing the next steps of the robot to be optimized. This acts as a constraint in the continuous optimization. The proposed approach uses Dynamic Programming to integrate the solutions of tasks and as a distance function in the diversity preserving evolutionary computation since the mission constantly changes. Comparisons are made with a baseline evolutionary computation without Dynamic Programming, a non-incremental version of the proposed algorithm, and several greedy strategies. The proposed algorithm is seen to perform better than all other methods. Further, the algorithm is implemented and demonstrated on a Pioneer LX robot.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Solving By Decompositionmentioning

confidence: 99%

Multi-robot mission planning using evolutionary computation with incremental task addition

Kala

2021

Intel Serv Robotics

View full text Add to dashboard Cite

show abstract

“…Jing et al (21) considered a related version of this problem, with particular application to robot motion planning in adversarial environments. Svorenova et al (22) showed that this problem can also be solved for the case in which the deterministic transition system incurs time-varying penalties modeled as Markov chains. The cost was the expected average cumulative penalty incurred between consecutive satisfactions of a desired property, and the specification was a general LTL formula.…”

Section: Finite Systemsmentioning

confidence: 99%

Formal Methods for Control Synthesis: An Optimization Perspective

Belta

Sadraddini

2019

Annu. Rev. Control Robot. Auton. Syst.

Self Cite

View full text Add to dashboard Cite

In control theory, complicated dynamics such as systems of (nonlinear) differential equations are controlled mostly to achieve stability. This fundamental property, which can be with respect to a desired operating point or a prescribed trajectory, is often linked with optimality, which requires minimizing a certain cost along the trajectories of a stable system. In formal verification (model checking), simple systems, such as finite-state transition graphs that model computer programs or digital circuits, are checked against rich specifications given as formulas of temporal logics. The formal synthesis problem, in which the goal is to synthesize or control a finite system from a temporal logic specification, has recently received increased interest. In this article, we review some recent results on the connection between optimal control and formal synthesis. Specifically, we focus on the following problem: Given a cost and a correctness temporal logic specification for a dynamical system, generate an optimal control strategy that satisfies the specification. We first provide a short overview of automata-based methods, in which the dynamics of the system are mapped to a finite abstraction that is then controlled using an automaton corresponding to the specification. We then provide a detailed overview of a class of methods that rely on mapping the specification and the dynamics to constraints of an optimization problem. We discuss advantages and limitations of these two types of approaches and suggest directions for future research.

show abstract

Task planning approaches

Kala

2024

Autonomous Mobile Robots

View full text Add to dashboard Cite

Optimal Temporal Logic Control for Deterministic Transition Systems With Probabilistic Penalties

Cited by 6 publications

References 28 publications

Multi-robot mission planning using evolutionary computation with incremental task addition

Multi-robot mission planning using evolutionary computation with incremental task addition

Formal Methods for Control Synthesis: An Optimization Perspective

Task planning approaches

Contact Info

Product

Resources

About