Temporal logic motion planning for dynamic robots

Fainekos, Georgios; Girard, Antoine; Kress-Gazit, Hadas; Pappas, George J.

doi:10.1016/j.automatica.2008.08.008

Cited by 384 publications

(318 citation statements)

References 22 publications

(48 reference statements)

Supporting

Mentioning

313

Contrasting

Unclassified

Order By: Relevance

“…This issue of navigation functions is mitigated in [9] by using sequential composition [10] of smooth extensions of local feedback rules. Approximation simulation [11] is another smooth extension method that aims to keep the spatial distance between the low-order and the high-order models bounded, but is too restrictive and computationally costly. Also, backstepping is applied to extend only stability properties of kinematic unicycles to dynamic unicycles [12].…”

Section: A Motivation and Prior Literaturementioning

confidence: 99%

Smooth extensions of feedback motion planners via reference governors

Arslan

Koditschek

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In robotics, it is often practically and theoretically convenient to design motion planners for approximate loworder (e.g., position-or velocity-controlled) robot models first, and then adapt such reference planners to more accurate high-order (e.g., force/torque-controlled) robot models. In this paper, we introduce a novel provably correct approach to extend the applicability of low-order feedback motion planners to high-order robot models, while retaining stability and collision avoidance properties, as well as enforcing additional constraints that are specific to the high-order models. Our smooth extension framework leverages the idea of reference governors to separate the issues of stability and constraint satisfaction, affording a bidirectionally coupled robot-governor system where the robot ensures stability with respect to the governor and the governor enforces state (e.g., collision avoidance) and control (e.g., actuator limits) constraints. We demonstrate example applications of our framework for augmenting path planners and vector field planners to the second-order robot dynamics.Keywords smooth extensions; command governors; reference governors; collision avoidance;robot dynamics;stability;collision avoidance;constraint satisfaction;feedback motion planners;path planners;reference governors;robot models;robot-governor system;robotics;second-order robot dynamics;smooth extensions;stability;vector field planners;Asymptotic stability;Collision avoidance;Computational modeling;Dynamics;Navigation;Robots;Stability analysis Disciplines Computer Engineering | Controls and Control Theory | Electrical and Computer Engineering | Engineering | RoboticsThis conference paper is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/790 Smooth Extensions of Feedback Motion Planners via Reference GovernorsOmur Arslan and Daniel E. KoditschekAbstract-In robotics, it is often practically and theoretically convenient to design motion planners for approximate loworder (e.g., position-or velocity-controlled) robot models first, and then adapt such reference planners to more accurate highorder (e.g., force/torque-controlled) robot models. In this paper, we introduce a novel provably correct approach to extend the applicability of low-order feedback motion planners to high-order robot models, while retaining stability and collision avoidance properties, as well as enforcing additional constraints that are specific to the high-order models. Our smooth extension framework leverages the idea of reference governors to separate the issues of stability and constraint satisfaction, affording a bidirectionally coupled robot-governor system where the robot ensures stability with respect to the governor and the governor enforces state (e.g., collision avoidance) and control (e.g., actuator limits) constraints. We demonstrate example applications of our framework for augmenting path planners and vector field planners to the second-order robot dynamics.

show abstract

Section: A Motivation and Prior Literaturementioning

confidence: 99%

Smooth extensions of feedback motion planners via reference governors

Arslan

Koditschek

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…To deal with LTL constraints, a hierarchical approach for motion planning for point-mass linear robot models with temporal goals has been proposed recently in [7]. The focus of the approach is on the construction of provably correct motion plans.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, such a general version of the motion planning problem has not been examined before in the literature. We propose a geometry-based, multi-layered synergistic approach that trades some of the completeness guarantees provided by [7] to efficiently solve the problem. The idea of trading strong completeness guarantees for efficiency and scalability has also been used successfully for solving challenging instances of traditional motion planning problems using sampling-based algorithms [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…As an example, the temporal goal "Eventually visit region A followed by a visit to region B or region C", can be easily expressed using LTL. An approach for motion planning with deterministic µ-calculus specifications has been proposed recently in [10].To deal with LTL constraints, a hierarchical approach for motion planning for point-mass linear robot models with temporal goals has been proposed recently in [7]. The focus of the approach is on the construction of provably correct motion plans.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Sampling-based motion planning with temporal goals

Bhatia

Kavraki

Vardi

2010

2010 IEEE International Conference on Robotics and Automation

208

206

View full text Add to dashboard Cite

Abstract-This paper presents a geometry-based, multilayered synergistic approach to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described over subsets of the workspace (called propositions) using temporal logic. A multi-layered synergistic framework has been proposed recently for solving planning problems involving significant discrete structure. In this framework, a high-level planner uses a discrete abstraction of the system and the exploration information to suggest feasible high-level plans. A low-level sampling-based planner uses the physical model of the system, and the suggested high-level plans, to explore the state-space for feasible solutions. In this paper, we advocate the use of geometry within the above framework to solve motion planning problems involving temporal goals. We present a technique to construct the discrete abstraction using the geometry of the obstacles and the propositions defined over the workspace. Furthermore, we show through experiments that the use of geometry results in significant computational speedups compared to previous work. Traces corresponding to trajectories of the system are defined employing the sampling interval used by the low-level algorithm. The applicability of the approach is shown for second-order nonlinear robot models in challenging workspace environments with obstacles, and for a variety of temporal logic specifications. I. INTRODUCTIONTraditional motion planning algorithms have considered the problem of constructing a motion plan for a given robot model, such that the plan takes the robot from a set of initial states to a set of goal states while avoiding obstacles in the workspace [1], [2]. A class of planning approaches have been proposed recently that use a richer framework to specify complex temporal goals like coverage, ordering of events, etc [3]-[8] using formalisms like Linear Temporal Logic (LTL) (cf. [9]). As an example, the temporal goal "Eventually visit region A followed by a visit to region B or region C", can be easily expressed using LTL. An approach for motion planning with deterministic µ-calculus specifications has been proposed recently in [10].To deal with LTL constraints, a hierarchical approach for motion planning for point-mass linear robot models with temporal goals has been proposed recently in [7]. The focus of the approach is on the construction of provably correct motion plans. Currently, the approach can handle secondorder linear robot models and more work needs to be done to extend the approach for nonlinear robot models. An approach combing synthesis technique with receding horizon control has also been proposed recently for linear robot models [8].The focus of this paper is motion planning problems involving, nonlinear robot models with finite geometry, com-

show abstract

“…In [20], temporal logic constraints are used for optimal path planning of a robot for surveillance. In [21], the temporal logic motion planning problem for mobile robots that are modeled by continuous-time second order linear dynamics is investigated. However, the aforementioned temporal logic based approaches only applied to robots with either no or linear closed-loop dynamics.…”

Section: Introductionmentioning

confidence: 99%

A framework for multi-robot motion planning from temporal logic specifications

Koo

Quottrup

et al. 2012

Sci. China Inf. Sci.

View full text Add to dashboard Cite

We propose a framework for the coordination of a network of robots with respect to formal requirement specifications expressed in temporal logics. A regular tessellation is used to partition the space of interest into a union of disjoint regular and equal cells with finite facets, and each cell can only be occupied by a robot or an obstacle. Each robot is assumed to be equipped with a finite collection of continuous-time nonlinear closed-loop dynamics to be operated in. The robot is then modeled as a hybrid automaton for capturing the finitely many modes of operation for either staying within the current cell or reaching an adjacent cell through the corresponding facet. By taking the motion capabilities into account, a bisimilar discrete abstraction of the hybrid automaton can be constructed. Having the two systems bisimilar, all properties that are expressible in temporal logics such as Linear-time Temporal Logic, Computation Tree Logic, and µ-calculus can be preserved. Motion planning can then be performed at a discrete level by considering the parallel composition of discrete abstractions of the robots with a requirement specification given in a suitable temporal logic. The bisimilarity ensures that the discrete planning solutions are executable by the robots. For demonstration purpose, a finite automaton is used as the abstraction and the requirement specification is expressed in Computation Tree Logic. The model checker Cadence SMV is used to generate coordinated verified motion planning solutions. Two autonomous aerial robots are used to demonstrate how the proposed framework may be applied to solve coordinated motion planning problems.

show abstract

Temporal logic motion planning for dynamic robots

Cited by 384 publications

References 22 publications

Smooth extensions of feedback motion planners via reference governors

Smooth extensions of feedback motion planners via reference governors

Sampling-based motion planning with temporal goals

A framework for multi-robot motion planning from temporal logic specifications

Contact Info

Product

Resources

About