Temporal logic motion planning in unknown environments

Ayala, Angel; Andersson, Sean B.; Belta, Călin

doi:10.1109/iros.2013.6697120

Cited by 31 publications

(26 citation statements)

References 14 publications

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“…To this end, we embedded runtime monitors into a layered reactive control architecture together with other simple and scalable components to achieve a navigation solution that does not require strong assumptions. Our approach amounts to a more active use of runtime monitors beyond checking assumptions of an offline planner at runtime [14,2,8]. We believe the simplicity and breadth of runtime monitors would make them ideal to cover many use cases and increase the level of assurance in robotic applications.…”

Section: Discussionmentioning

confidence: 99%

Reactive Control Meets Runtime Verification: A Case Study of Navigation

Ulus

Belta

2019

Runtime Verification

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This paper presents an application of specification based runtime verification techniques to control mobile robots in a reactive manner. In our case study, we develop a layered control architecture where runtime monitors constructed from formal specifications are embedded into the navigation stack. We use temporal logic and regular expressions to describe safety requirements and mission specifications, respectively. An immediate benefit of our approach is that it leverages simple requirements and objectives of traditional control applications to more complex specifications in a non-intrusive and compositional way. Finally, we demonstrate a simulation of robots controlled by the proposed architecture and we discuss further extensions of our approach.

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Section: Discussionmentioning

confidence: 99%

Reactive Control Meets Runtime Verification: A Case Study of Navigation

Ulus

Belta

2019

Runtime Verification

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“…Each node ν of the PRM is used to create a FIRM node as follows. First the system model (1) and observation model (2) are linearised with respect to a node ν resulting in the linear models:…”

Section: Feedback-based Information Roadmapmentioning

confidence: 99%

“…To achieve this, it is necessary to verify which transitions of the FIRM do not violate the LTL specification. A similar problem has been solved in the literature for deterministic systems with perfect state information [2,19] using a monitor [4] which identifies if a specification has been satisfied or falsified as early as possible. In this work, since the state of the system is unknown, we use the Rabin automaton instead.…”

Section: Local Targetsmentioning

confidence: 99%

Sampling-Based Reactive Motion Planning with Temporal Logic Constraints and Imperfect State Information

Montana

Dodd

2017

Lecture Notes in Computer Science

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract. This paper presents a method that allows mobile systems with uncertainty in motion and sensing to react to unknown environments while high-level specifications are satisfied. Although previous works have addressed the problem of synthesising controllers under uncertainty constraints and temporal logic specifications, reaction to dynamic environments has not been considered under this scenario. The method uses feedback-based information roadmaps (FIRMs) to break the curse of history associated with partially observable systems. A transition system is incrementally constructed based on the idea of FIRMs by adding nodes on the belief space. Then, a policy is found in the product Markov decision process created between the transition system and a Rabin automaton representing a linear temporal logic formula. The proposed solution allows the system to react to previously unknown elements in the environment. To achieve fast reaction time, a FIRM considering the probability of violating the specification in each transition is used to drive the system towards local targets or to avoid obstacles. The method is demonstrated with an illustrative example.

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“…This type of approach is good for an environment where the robot operates in partially known workspaces. Another approach for motion planning in unknown environment was proposed by Ayala et al [7]. The authors solved the LTL specification for an unknown environment wherein an accurate assumption of the sensors and actuators data was made, and the environment was partitioned into finite square cells.…”

Section: Related Workmentioning

confidence: 99%

Linear Temporal Logic-based Mission Planning

Kumar¹,

Kala²

2016

IJIMAI

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-In this paper, we describe the Linear Temporal Logic-based reactive motion planning. We address the problem of motion planning for mobile robots, wherein the goal specification of planning is given in complex environments. The desired task specification may consist of complex behaviors of the robot, including specifications for environment constraints, need of task optimality, obstacle avoidance, rescue specifications, surveillance specifications, safety specifications, etc. We use Linear Temporal Logic to give a representation for such complex task specification and constraints. The specifications are used by a verification engine to judge the feasibility and suitability of plans. The planner gives a motion strategy as output. Finally a controller is used to generate the desired trajectory to achieve such a goal. The approach is tested using simulations on the LTLMoP mission planning tool, operating over the Robot Operating System. Simulation results generated using high level planners and low level controllers work simultaneously for mission planning and controlling the physical behavior of the robot.

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Temporal logic motion planning in unknown environments

Cited by 31 publications

References 14 publications

Reactive Control Meets Runtime Verification: A Case Study of Navigation

Reactive Control Meets Runtime Verification: A Case Study of Navigation

Sampling-Based Reactive Motion Planning with Temporal Logic Constraints and Imperfect State Information

Linear Temporal Logic-based Mission Planning

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