Multi-Robot Systems: Modeling, Specification, and Model Checking

Mohammed, Ammar; Furbach, Ulrich; Stolzenburg, Frieder

doi:10.5772/7349

Cited by 19 publications

(7 citation statements)

References 38 publications

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“…Model-based test generation usefully complements this technique by systematically directing tests according to the requirements of the SUT. This requires the development of a formal model of the system, which additionally enables exhaustive verification through formal methods, as explored by previous authors for HRI [3,6,14,19,20,25].…”

Section: Discussionmentioning

confidence: 99%

“…We require a verification methodology that is sufficiently realistic (models the system with sufficient detail) while thoroughly exploring the range of possible outcomes, without exceeding resource constraints. s. Prior work [3,6,14,19,20,25] has explored the use of formal methods to verify HRI. Formal methods can achieve full coverage of a highly abstracted model of the interactions, but are limited in the level of detail that can practically be modelled.…”

Section: Introductionmentioning

confidence: 99%

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Coverage-Driven Verification - An approach to verify code for robots that directly interact with humans

Araiza-Illan,

Western,

Pipe

et al. 2015

Preprint

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Collaborative robots could transform several industries, such as manufacturing and healthcare, but they present a significant challenge to verification. The complex nature of their working environment necessitates testing in realistic detail under a broad range of circumstances. We propose the use of Coverage-Driven Verification (CDV) to meet this challenge. By automating the simulation-based testing process as far as possible, CDV provides an efficient route to coverage closure. We discuss the need, practical considerations, and potential benefits of transferring this approach from microelectronic design verification to the field of humanrobot interaction. We demonstrate the validity and feasibility of the proposed approach by constructing a custom CDV testbench and applying it to the verification of an object handover task.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coverage-Driven Verification - An approach to verify code for robots that directly interact with humans

Araiza-Illan,

Western,

Pipe

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…4 Formal verification has been used before for robotic systems. For example, Mohammed et al [13] used hybrid automata and hybrid statecharts for formal modeling and verification of multirobot systems, Cowley and Taylor [14] used dependent-type theory and linear logic for the formal verification of assembly robots, and Kouskoulas et al [15] formally verified control algorithms for surgical robots. However, very little work has been conducted to formally verify the safety and trustworthiness of robotic home companions.…”

Section: B Related Workmentioning

confidence: 99%

Toward Reliable Autonomous Robotic Assistants Through Formal Verification: A Case Study

Webster

Dixon

Fisher

et al. 2016

IEEE Trans. Human-Mach. Syst.

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It is essential for robots working in close proximity to people to be both safe and trustworthy. We present a case study on formal verification for a high-level planner/scheduler for the Care-O-bot, an autonomous personal robotic assistant. We describe how a model of the Care-O-bot and its environment was developed using Brahms, a multiagent workflow language. Formal verification was then carried out by automatically translating this model to the input language of an existing model checker. Four sample properties based on system requirements were verified. We then refined the environment model three times to increase its accuracy and the persuasiveness of the formal verification results. The first refinement uses a user activity log based on real-life experiments, but is deterministic. The second refinement uses the activities from the user activity log nondeterministically. The third refinement uses "conjoined activities" based on an observation that many user activities can overlap. The four samples properties were verified for each refinement of the environment model. Finally, we discuss the approach of environment model refinement with respect to this case study.

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“…Their behavior consists of discrete state transitions plus continuous evolution [8]. Hybrid automata have been successfully applied especially to technical and embedded systems, e. g. for describing multi-robot behavior [2,18,20]. However, a feasible procedure for learning hybrid automata does not seem to be available.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks and Continuous Time

Stolzenburg,

Ruh

2016

Preprint

Self Cite

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The fields of neural computation and artificial neural networks have developed much in the last decades. Most of the works in these fields focus on implementing and/or learning discrete functions or behavior. However, technical, physical, and also cognitive processes evolve continuously in time. This cannot be described directly with standard architectures of artificial neural networks such as multi-layer feed-forward perceptrons. Therefore, in this paper, we will argue that neural networks modeling continuous time are needed explicitly for this purpose, because with them the synthesis and analysis of continuous and possibly periodic processes in time are possible (e. g. for robot behavior) besides computing discrete classification functions (e. g. for logical reasoning). We will relate possible neural network architectures with (hybrid) automata models that allow to express continuous processes.

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Multi-Robot Systems: Modeling, Specification, and Model Checking

Cited by 19 publications

References 38 publications

Coverage-Driven Verification - An approach to verify code for robots that directly interact with humans

Coverage-Driven Verification - An approach to verify code for robots that directly interact with humans

Toward Reliable Autonomous Robotic Assistants Through Formal Verification: A Case Study

Neural Networks and Continuous Time

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