A Model-driven Approach for the Formal Analysis of Human-Robot Interaction Scenarios

Robots are currently mostly found in industrial settings. In the future, a wider range of environments will benefit from their inclusion. This calls for the development of tools that allow professionals to set up dependable robotic applications in which people productively interact with robots aware of their needs. Given the co-existence of humans and robots, the precise analysis—e.g., through formal verification techniques—of properties related to aspects such as human needs and physiology is of paramount importance. In this paper, we present a formally-based, model-driven approach to design and verify scenarios involving human-robot interactions. Some of the features of our approach are tailored to the healthcare domain, from which our case studies are derived. In our approach, the designer specifies the main parameters of the mission to generate the model of the application, which includes mobile robots, the humans to be served, including some of their physiological features, and the decision-maker that orchestrates the execution. All components are modeled through hybrid automata to capture variables with complex dynamics. The model is verified through Statistical Model Checking (SMC), using the Uppaal tool, to determine the probability of success of the mission. The results are examined by the developer, who iteratively refines the design until the probability of success is satisfactory.

[1]  Gul A. Agha,et al.  A Survey of Statistical Model Checking , 2018, ACM Trans. Model. Comput. Simul..

[2]  Kim G. Larsen,et al.  Uppaal SMC tutorial , 2015, International Journal on Software Tools for Technology Transfer.

[3]  Philip R. O. Payne,et al.  A Review of Clinical Workflow Studies and Methods , 2019, Health Informatics.

[4]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[5]  Dino Mandrioli,et al.  Formal model of human erroneous behavior for safety analysis in collaborative robotics , 2019, Robotics and Computer-Integrated Manufacturing.

[6]  Michael Luck,et al.  Quantitative Analysis of Multiagent Systems Through Statistical Model Checking , 2015, EMAS@AAMAS.

[7]  Oliver Kroemer,et al.  Interaction primitives for human-robot cooperation tasks , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Abdulmotaleb El-Saddik,et al.  Development of a fatigue-tracking system for monitoring human body movement , 2014, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[9]  Wisama Khalil,et al.  Suboptimal Trajectory Generation for Industrial Robots using Trapezoidal Velocity Profiles , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Thomas A. Henzinger,et al.  The Algorithmic Analysis of Hybrid Systems , 1995, Theor. Comput. Sci..

[11]  Cristina Seceleanu,et al.  Statistical Model Checking of Complex Robotic Systems , 2019, SPIN.

[12]  Prashant Krishnamurthy,et al.  Modeling of indoor positioning systems based on location fingerprinting , 2004, IEEE INFOCOM 2004.

[13]  Rosemarie E. Yagoda,et al.  How to work and play with robots: An approach to modeling human-robot interaction , 2012, Comput. Hum. Behav..

[14]  Stefanos Nikolaidis,et al.  Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[15]  Gurvinder S. Virk,et al.  ISO 13482 - The new safety standard for personal care robots , 2014, ISR 2014.

[16]  Rajeev Alur,et al.  A Theory of Timed Automata , 1994, Theor. Comput. Sci..

[17]  Séverin Lemaignan,et al.  Artificial cognition for social human-robot interaction: An implementation , 2017, Artif. Intell..

[18]  Mark J Hadley,et al.  A deterministic model of the free will phenomenon , 2018 .

[19]  Anthony G. Pipe,et al.  Model-Based Testing, Using Belief-Desire-Intentions Agents, of Control Code for Robots in Collaborative Human-Robot Interactions , 2016, ArXiv.

[20]  Clare Dixon,et al.  Formal Verification of an Autonomous Personal Robotic Assistant , 2014, AAAI Spring Symposia.

[21]  Dino Mandrioli,et al.  Safety Assessment of Collaborative Robotics Through Automated Formal Verification , 2020, IEEE Transactions on Robotics.

[22]  Mohamad Y. Jaber,et al.  Incorporating human fatigue and recovery into the learning–forgetting process , 2013 .

[23]  Wang Yi,et al.  Uppaal in a nutshell , 1997, International Journal on Software Tools for Technology Transfer.