Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Lyons, Damian M.; Arkin, Ronald C.; Jiang, Shengyuan; Harrington, Dagan; Tang, Fan; Tang, Peng

doi:10.1109/ictai.2015.22

Cited by 9 publications

(14 citation statements)

References 17 publications

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“…Although [1] discusses more complicated performance guarantees, we will typically restrict our attention to the guarantee that a mission will achieve some criterion on environment variables (usually a spatial accuracy for a waypoint goal and/or a temporal requirement for achieving the mission) with probability greater than a threshold before a time-limit. We demonstrated that this approach is fast and accurate when validated against physical executions (most recently [28]).…”

Section: Verification In Pars (Vipars)mentioning

confidence: 97%

Performance verification for robot missions in uncertain environments

Lyons

Arkin

Jiang

et al. 2017

Robotics and Autonomous Systems

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h i g h l i g h t s• This paper addresses the challenges involved in building a software tool for automatically verifying the behavior of multi-robot waypoint missions using formal methods.• Missions can include uncertainly located obstacles and uncertain environment geometry as well as uncertainty in robot motion.• We leverage a unique approach, VIPARS, to verifying performance guarantees for autonomous behavior-based robot software based on a combination of static analysis and Bayesian networks.• Two approaches to modeling probabilistic localization for verification are presented: a high-level approach and an approach that allows run-time localization code to be embedded within verification.• Verification and experimental validation results are presented for several autonomous robot missions, demonstrating the accuracy of verification and the mission-specific benefit of localization. a b s t r a c tEstablishing a-priori mission performance guarantees is crucial if autonomous robots are to be used with confidence in missions where failure could incur high costs in life and property damage. Automatic mission software verification, in addition to simulation and experimental benchmarking, is a key component of the solution for establishing performance guarantees. This component requires automatically verifying that the software constructed by the mission designer when executed in a partially known environment will adhere to the performance guarantee. In prior work we developed VIPARS, a unique approach to verifying performance guarantees for autonomous behavior-based robot software based on a combination of static analysis and Bayesian networks. While that approach produced fast and accurate verification of single robot missions with robot motion uncertainty, it did not address multiple-robot missions or any form of uncertainty related to environment geometry. This paper addresses the challenges involved in building a software tool for verifying the behavior of a multi-robot waypoint mission that includes uncertainly located obstacles and uncertain environment geometry as well as uncertainty in robot motion. An approach is presented to the problem of a-priori specification of uncertain environments for robot program verification. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach and an approach that allows run-time localization code to be embedded in verification. Verification and experimental validation results are presented for several autonomous robot missions, demonstrating the accuracy of verification and the mission-specific benefit of localization

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Section: Verification In Pars (Vipars)mentioning

confidence: 97%

Performance verification for robot missions in uncertain environments

Lyons

Arkin

Jiang

et al. 2017

Robotics and Autonomous Systems

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“…As previously mentioned in §5.2, the graphical robot mission design tool MissionLab contains a process algebra, PARS. The work in [121] describes a robot's behaviour using FSA and its environment using a Gaussian model, but MissionLab automatically translates these into PARS for checking the robot's behaviour within the given environment.…”

Section: Process Algebrasmentioning

confidence: 99%

Formal Specification and Verification of Autonomous Robotic Systems

et al. 2019

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Autonomous robotic systems are complex, hybrid, and often safety-critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics has received some attention in the literature, but no resource provides a current overview. This paper systematically surveys the state-of-the-art in formal specification and verification for autonomous robotics. Specially, it identifies and categorises the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics. Introduction, Methodology and Related WorkAn autonomous system is an artificially intelligent entity that makes decisions in response to input, independent of human interaction. Robotic systems are physical entities that interact with the physical world. Thus, we consider an autonomous robotic system as a machine that uses Artificial Intelligence (AI), has a physical presence in and interacts with the real world. They are complex, inherently hybrid, systems, combining both hardware and software; they often require close safety, legal, and ethical consideration. Autonomous robotics are increasingly being used in commonplace-scenarios, such as driverless cars [68], pilotless aircraft [176], and domestic assistants [174,60].While for many engineered systems, testing, either through real deployment or via simulation, is deemed sufficient; the unique challenges of autonomous robotics, their dependence on sophisticated software control and decision-making, and their increasing deployment in safety-critical scenarios, require a stronger form of verification. This leads us towards using formal methods, which are mathematically-based techniques for the specification and verification of software systems, to ensure the correctness of, and provide sufficient evidence for the certification of, robotic systems.We contribute an overview and analysis of the state-of-the-art in formal specification and verification of autonomous robotics. §1.1 outlines the scope, research questions and search criteria for our survey. §1.2 describes related work concerning formal methods for robotics and differentiates them from our work. We recognise the important role that middleware architectures and, non-and semi-formal techniques have in the development of reliable robotics and we briefly summarise some of these techniques in §2. The specification and verification challenges raised by autonomous robotic systems are discussed next: §3 describes the challenges of their context (the external challenges) and §4 describes the challenges of their organisation (the internal challenges). §5 discusses the formalisms used in the literature for specification and verification of autonomous robotics. §6 characterises the approaches to formal specification and verification of autonomous robotics found in the li...

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“…to Human in the Loop Robot Missions * system in providing performance guarantees for missions involving heterogeneous human-robot systems (Figure 3.1), we developed a simple biohazard search mission where the humanrobot team is tasked to search and locate a biohazard within a given environment. This mission with air and ground robots introduces beneficial heterogeneity into the WMD (e.g., biohazard) search missions we have addressed in our prior work [4]. Quadrotors have excellent speed and mobility, making ideal platforms for searching.…”

Section: Formal Performance Guarantees For An Approachmentioning

confidence: 99%

“…A brief introduction to our approach to formal verification is presented first to set the context for our proposed human operator model. In prior work [4] [2] [12], Lyons et al designed a framework for verifying the performance of autonomous behavior-based robot missions in uncertain environments. MissionLab [11] mission software is autotranslated [12] to a process-algebra notation PARS (Process Algebra for Robot Schemas) for analysis.…”

Section: Formal Verification Of Human In the Loopmentioning

confidence: 99%

“…While research in automatic verification of robot software has typically followed the methods used in the general purpose software verification field [1], we have developed an novel approach to efficiently establish probabilistic performance guarantees for behavior-behavior based robot software operating in uncertain environments [2]. A software module based on the approach, VIPARS, has been used to establish probabilistic performance guarantees for multiple-robot missions [3], for missions with probabilistic obstacle information [4] as well as missions using probabilistic localization software [5]. We have also studied the usability of our system [6].…”

Section: Introductionmentioning

confidence: 99%

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Formal performance guarantees for an approach to human in the loop robot missions

Lyons

Arkin

Jiang

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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A key challenge in the automatic verification of robot mission software, especially critical mission software, is to be able to effectively model the performance of a human operator and factor that into the formal performance guarantees for the mission. We present a novel approach to modelling the skill level of the operator and integrating it into automatic verification using a linear Gaussians model parameterized by experimental calibration. Our approach allows us to model different skill levels directly in terms of the behavior of the lumped, robot plus operator, system. Using MissionLab and VIPARS (a behavior-based robot mission verification module), we present a comparison of our predicted performance guarantees for two missions in which a teleoperated quadrotor identifies a target for an autonomous ground robot to intercept: one mission in which the operator flies the quadrotor by line of sight to locate the target and one where the operator flies the quadrotor using its video feed. We demonstrate the effectiveness of our approach by comparing predicated performance to experimentally measured performance.

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Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Cited by 9 publications

References 17 publications

Performance verification for robot missions in uncertain environments

Performance verification for robot missions in uncertain environments

Formal Specification and Verification of Autonomous Robotic Systems

Formal performance guarantees for an approach to human in the loop robot missions

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