ABSTRACT
We propose an approach to synthesize control protocols for autonomous systems that account for uncertainties and imperfections in interactions with human operators. As an illustrative example, we consider a scenario involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a human operator but also has a certain degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge about the uncertainties and imperfections in the operator-autonomy interactions, we use abstractions based on Markov decision processes and augment these models to stochastic two-player games. Our approach enables the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the effects of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance; it can also provide informative feedback (e.g., Pareto curves showing the trade-offs between multiple mission objectives), potentially assisting the operator in decision-making.
- K. R. Boff and J. E. Lincoln. Engineering Data Compendium: Human Perception and Performance. AAMRL, Wright-Patterson AFB, OH, 1988.Google Scholar
- T. Chen, V. Forejt, M. Kwiatkowska, D. Parker, and A. Simaitis. PRISM-games: A model checker for stochastic multi-player games. In TACAS, pages 185--191. Springer, 2013. Google ScholarDigital Library
- T. Chen, V. Forejt, M. Kwiatkowska, A. Simaitis, and C. Wiltsche. On stochastic games with multiple objectives. In MFCS, pages 266--277. Springer, 2013.Google ScholarCross Ref
- T. Chen, M. Kwiatkowska, A. Simaitis, and C. Wiltsche. Synthesis for multi-objective stochastic games: An application to autonomous urban driving. In QEST, pages 322--337. Springer, 2013. Google ScholarDigital Library
- N. J. Cooke and H. K. Pedersen. Unmanned aerial vehicles. In Handbook of Aviation Human Factors. CRC Press, 2009.Google ScholarCross Ref
- B. Crandall, G. Klein, and R. R. Homan. Working Minds: A Practitioner's Guide to Cognitive Task Analysis. MIT Press, 2006.Google ScholarCross Ref
- J. A. DeJoode, N. J. Cooke, S. M. Shope, and H. K. Pedersen. Guilding the design of a deployable uav operations cell. In Human Factors of Remotely Operated Vehicles, volume 7, pages 311--327, 2006.Google ScholarCross Ref
- D. Donath, A. Rauschert, and A. Schulte. Cognitive assistant system concept for multi-UAV guidance using human operator behaviour models. In HUMOUS, 2010.Google Scholar
- Federal Aviation Administration. Integration of Civil Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Roadmap, 2013.Google Scholar
- L. Humphrey, E. Wolff, and U. Topcu. Formal specification and synthesis of mission plans for unmanned aerial vehicles. In Proc. of the AAAI Spring Symposium, 2014.Google Scholar
- H. S. Koelega. Extraversion and vigilance performance: 30 years of inconsistencies. Psychological Bulletin, 112(2): 239--258, 1992.Google ScholarCross Ref
- M. Kwiatkowska, G. Norman, and D. Parker. PRISM 4.0: Verification of probabilistic real-time systems. In CAV, pages 585--591. Springer, 2011. Google ScholarDigital Library
- M. Kwiatkowska and D. Parker. Automated verification and strategy synthesis for probabilistic systems. In ATVA, pages 5--22. Springer, 2013.Google ScholarCross Ref
- S. Makeig, F. S. Elliott, M. Inlow, and D. A. Kobus. Predicting lapses in vigilance using brain evoked responses to irrelevant auditory probes. Technical Report TR 90-39, Naval Health Research Center, 1990.Google Scholar
- M. L. Cummings. Operator interaction with centralized versus decentralized UAV architectures. In Handbook of Unmanned Aerial Vehicles. Springer, 2015. in press.Google ScholarCross Ref
- National Highway Traffic Safety Administration. Preliminary Statement of Policy Concerning Automated Vehicles, 2013.Google Scholar
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley and Sons, 1994. Google ScholarCross Ref
- K. Savla and E. Frazzoli. A dynamical queue approach to intelligent task management for human operators. Proc. of the IEEE, 100(3): 672--686, 2012.Google ScholarCross Ref
- L. S. Shapley. Stochastic games. Proc. Natl. Acad. Sci. USA, 39(10): 1095, 1953.Google ScholarCross Ref
- A. Stewart, M. Cao, A. Nedic, D. Tomlin, and N. E. Leonard. Towards human--robot teams: Model-based analysis of human decision making in two-alternative choice tasks with social feedback. Proc. of the IEEE, 100(3): 751--775, 2012.Google ScholarCross Ref
- G. Trafton, L. Hiatt, A. Harrison, F. Tamborello, S. Khemlani, and A. Schultz. ACT-R/E: An embodied cognitive architecture for human-robot interaction. Journal of Human-Robot Interaction, 2(1): 30--55, 2012.Google ScholarDigital Library
- K. W. Williams. Human factors implications of unmanned aircraft accidents: Flight-control problems. In Human Factors of Remotely Operated Vehicles, volume 7, pages 105--116, 2006.Google ScholarCross Ref
- T. Wongpiromsarn, U. Topcu, and R. M. Murray. Synthesis of control protocols for autonomous systems. Unmanned Systems, 1(1): 21--39, Jul 2013.Google ScholarCross Ref
Index Terms
- Controller synthesis for autonomous systems interacting with human operators
Recommendations
Collaborative models for autonomous systems controller synthesis
AbstractWe show how detailed simulation models and abstract Markov models can be developed collaboratively to generate and implement effective controllers for autonomous agent search and retrieve missions. We introduce a concrete simulation model of an ...
Flight validation of a feedforward gust-attenuation controller for an autonomous helicopter
This paper presents a practical scheme to control heave motion for hover and automatic landing of a Rotary-wing Unmanned Aerial Vehicle (RUAV) in the presence of strong horizontal gusts. A heave motion model is constructed for the purpose of capturing ...
Comments