Robotics and Integrated Formal Methods: Necessity meets Opportunity

Robotic systems are multi-dimensional entities, combining both hardware and software, that are heavily dependent on, and influenced by, interactions with the real world. They can be variously categorised as embedded, cyberphysical, real-time, hybrid, adaptive and even autonomous systems, with a typical robotic system being likely to contain all of these aspects. The techniques for developing and verifying each of these system varieties are often quite distinct. This, together with the sheer complexity of robotic systems, leads us to argue that diverse formal techniques must be integrated in order to develop, verify, and provide certification evidence for, robotic systems. Furthermore, we propose the fast evolving field of robotics as an ideal catalyst for the advancement of integrated formal methods research, helping to drive the field in new and exciting directions and shedding light on the development of large-scale, dynamic, complex systems.

[1]  Clare Dixon,et al.  On Formal Specification of Emergent Behaviours in Swarm Robotic Systems , 2005 .

[2]  Nadeem Akhtar,et al.  Contribution to the Formal Specification and Verification of a Multi-Agent Robotic System , 2015, ArXiv.

[3]  Z. M. Bi,et al.  Development of reconfigurable machines , 2008 .

[4]  Fanny Dufossé,et al.  Specifying Safety Monitors for Autonomous Systems Using Model-Checking , 2014, SAFECOMP.

[5]  Rafael H. Bordini,et al.  Model checking agent programming languages , 2012, Automated Software Engineering.

[6]  André Platzer,et al.  On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles , 2013, Robotics: Science and Systems.

[7]  Sarfraz Khurshid,et al.  Verification of Multi-agent Negotiations Using the Alloy Analyzer , 2007, IFM.

[8]  Michael Fisher,et al.  Generating Certification Evidence for Autonomous Unmanned Aircraft Using Model Checking and Simulation , 2014, J. Aerosp. Inf. Syst..

[9]  Andreas Rausch,et al.  Towards the Verification of Safety-critical Autonomous Systems in Dynamic Environments , 2016, V2CPS@IFM.

[10]  Clare Dixon,et al.  A corroborative approach to verification and validation of human–robot teams , 2016, Int. J. Robotics Res..

[11]  Jesper Andersson,et al.  FORMS: a formal reference model for self-adaptation , 2010, ICAC '10.

[12]  Atif Mashkoor,et al.  How to Select the Suitable Formal Method for an Industrial Application: A Survey , 2016, ABZ.

[13]  Martin Gogolla,et al.  Using Models at Runtime to Address Assurance for Self-Adaptive Systems , 2015, Models@run.time@Dagstuhl.

[14]  Sandor M. Veres,et al.  A stochastically verifiable autonomous control architecture with reasoning , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[15]  Matthias Althoff,et al.  Formalising and Monitoring Traffic Rules for Autonomous Vehicles in Isabelle/HOL , 2017, IFM.

[16]  Graeme Smith,et al.  MAZE: An Extension of Object-Z for Multi-Agent Systems , 2014, ABZ.

[17]  Michael Fisher,et al.  Formal verification of autonomous vehicle platooning , 2016, Sci. Comput. Program..

[18]  Arnaud Lanoix,et al.  Using CSP||B Components: Application to a Platoon of Vehicles , 2009, FMICS.

[19]  Elena Troubitsyna,et al.  Formal Development and Assessment of a Reconfigurable On-board Satellite System , 2012, SAFECOMP.

[20]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[21]  Jonathan Lawry,et al.  Formal Specification and Analysis of Autonomous Systems under Partial Compliance , 2016 .

[22]  Wei Li,et al.  Modelling and Verification of Timed Robotic Controllers , 2017, IFM.

[23]  J. L. Rash,et al.  Requirements of an integrated formal method for intelligent swarms , 2005, FMICS '05.

[24]  Richard M. Murray,et al.  Safety verification of a fault tolerant reconfigurable autonomous goal-based robotic control system , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Sanjit A. Seshia,et al.  Combining Model Checking and Runtime Verification for Safe Robotics , 2017, RV.

[26]  Clare Dixon,et al.  Toward Reliable Autonomous Robotic Assistants Through Formal Verification: A Case Study , 2016, IEEE Transactions on Human-Machine Systems.

[27]  Michael Fisher,et al.  Modular Verification of Vehicle Platooning with Respect to Decisions, Space and Time , 2018, FTSCS.

[28]  Kerstin Eder,et al.  Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms , 2015, TAROS.

[29]  Radu Grosu,et al.  Collision Avoidance for Mobile Robots with Limited Sensing and Limited Information About the Environment , 2015, RV.

[30]  Koen V. Hindriks,et al.  Toward a programming theory for rational agents , 2009, Autonomous Agents and Multi-Agent Systems.

[31]  Reid G. Simmons,et al.  Towards automatic verification of autonomous systems , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[32]  Antonios Tsourdos,et al.  Verification of heterogeneous multi-agent system using MCMAS , 2015, Int. J. Syst. Sci..

[33]  Ewen Denney,et al.  Automating the Assembly of Aviation Safety Cases , 2014, IEEE Transactions on Reliability.

[34]  Clare Dixon,et al.  Analysing robot swarm behaviour via probabilistic model checking , 2012, Robotics Auton. Syst..

[35]  Michael Wooldridge,et al.  The dMARS Architecture: A Specification of the Distributed Multi-Agent Reasoning System , 2004, Autonomous Agents and Multi-Agent Systems.

[36]  Michael Fisher,et al.  Verifying autonomous systems , 2013, CACM.

[37]  Danny Weyns,et al.  A survey of formal methods in self-adaptive systems , 2012, C3S2E '12.

[38]  Hadas Kress-Gazit,et al.  Decentralized control of robotic swarms from high-level temporal logic specifications , 2017, 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS).