Abstract
Autonomous robots that execute plans in real-world environments may easily get failed due to unexpected environment dynamics. For robust plan execution, a great number of plan execution monitoring works has been proposed to estimate plan execution effects and make recovery plans for any detected plan failures. However, most works have assumed the full observability of environment states and accessibility of complete information about environment dynamics, which shows limitations when executing plans under environment dynamics and partial observability. To efficiently and effectively monitor robot plans in dynamic and partially observable environments, this paper first proposes an accompanying action policy that specifies an interaction pattern between information-gathering plan and the task plan to estimate and monitor task plan execution. To provide a concrete implementation of the above pattern, we first identify the task achievement and execution monitoring as two separated goals in a robot task, and then propose a hybrid planning approach that integrates two planners to solve the goals. For a task-achievement goal, we utilize a classical planning framework ROSPlan to efficiently compute the global task plan. While executing each action of the plan, we cast the execution monitoring problem as a Partially Observable Markov Decision Process (POMDP), which plans the information-gathering plans for monitors the action execution effects and recover failures when necessary. We further implement a typical robotic service task to verify the efficiency and effectiveness of our approach. By comparing with both the baseline plan-invariant execution monitoring approach and a full POMDP planning approach, the hybrid planning approach is efficient in task planning and execution monitoring and effectively recovering plan failures in unknown environments.
Similar content being viewed by others
References
Salehie, M., Tahvildari L.: Self-adaptive software: Landscape and research challenges[J]. ACM Trans. Auton. Adapt. Syst. (TAAS) 4(2), 14 (2009)
Pettersson, O.: Execution monitoring in robotics: A survey[J]. Robot. Auton. Syst. 53(2), 73–88 (2005)
Feng, D., Germain, C.: Fault monitoring with sequential matrix factorization[J]. ACM Trans. Auton. Adapt. Syst. (TAAS) 10(3), 20 (2015)
Angelopoulos, K., Papadopoulos, A.V., Souza, V.E.S., et al.: Engineering self-adaptive software systems: From requirements to model predictive control[J]. ACM Trans. Auton. Adapt. Syst. (TAAS) 13(1), 1 (2018)
Shani, G., Pineau, J., Kaplow, R.: A survey of point-based POMDP solvers[J]. Auton. Agent. Multi-Agent Syst. 27(1), 1–51 (2013)
Ingrand, F., Ghallab, M.: Deliberation for autonomous robots: A survey[J]. Artif. Intell. 247, 10–44 (2017)
Pettersson, O., Karlsson, L., Saffiotti, A.: Model-free execution monitoring in behavior-based robotics[J]. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 37(4), 890–901 (2007)
Kuestenmacher, A., Akhtar, N., Plöger, P.G., et al.: Towards robust task execution for domestic service robots[J]. J. Intell. Robot. Syst. 76(1), 5–33 (2014)
Konecný, S, Stock, S., Pecora, F., et al.: Planning domain+ execution semantics: a way towards robust execution[C]. In: Qualitative Representations for Robots AAAI Spring Symposium (2014)
Konecný, S, Pecora, F., Saffiotti, A.: Execution knowledge for execution monitoring: what, why, where and what for[C]. In: IEEE/RSJ international conference on intelligent robots and systems (IROS)-Workshop on AI Robotics, Chicago, Illinois, pp. 14–18 (2014)
Mendoza, J.P., Veloso, M., Simmons, R.: Plan execution monitoring through detection of unmet expectations about action outcomes[C]. In: IEEE International Conference on Robotics and Automation, IEEE, pp. 3247–3252 (2015)
Wilson, M.A., McMahon, J., Aha, D.W.: Bounded expectations for discrepancy detection in goal-driven autonomy[C]. In: AI and Robotics: Papers from the AAAI Workshop (2014)
Havur, G., Haspalamutgil, K., Palaz, C., et al.: A case study on the Tower of Hanoi challenge: Representation, reasoning and execution[C]. In: IEEE International Conference on Robotics and Automation, IEEE, pp. 4552–4559 (2013)
Iocchi, L., Jeanpierre, L., Lazaro, M.T., et al.: A practical framework for robust decision-theoretic planning and execution for service robots[C] ICAPS. 486–494 (2016)
Hofmann, A.G., Williams, B.C.: Temporally and spatially flexible plan execution for dynamic hybrid systems[J]. Artif. Intell. 247, 266–294 (2017)
Soeanu, A., Debbabi, M., Allouche, M.: Hierarchy aware distributed plan execution monitoring[J]. Expert Syst. Appl. Int. J. 43(C), 66–81 (2016)
Gateau, T., Lesire, C., Barbier, M.: Hidden: Cooperative plan execution and repair for heterogeneous robots in dynamic environments. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4790–4795). IEEE. (2013)
Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains[J]. Artif. Intell. 101(1-2), 99–134 (1998)
Yang, S., Mao, X., Liu, Z., et al.: The accompanying behavior model and implementation architecture of autonomous robot software[C]. In: 2017 24th Asia-Pacific Software Engineering Conference (APSEC), IEEE, pp. 209–218 (2017)
Liu, Z., Mao, X., Yang, S.: A dual-loop control model and software framework for autonomous robot software[C]. In: Asia-Pacific Software Engineering Conference (APSEC), 2017 24th, IEEE, pp. 229–238 (2017)
Cashmore, M., Fox, M., Long, D., et al.: ROSPlan: Planning in the robot operating system[C]. In: Twenty-Fifth International Conference on Automated Planning and Scheduling, pp. 333–341 (2015)
Somani, A., Ye, N., Hsu, D., et al.: DESPOT: Online POMDP planning with regularization[C]. In: Advances in neural information processing systems, pp. 1772–1780 (2013)
Xiao, Y., Katt, S., ten Pas, A., et al.: Online planning for target object search in clutter under partial observability[C]. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp. 8241–8247 (2019)
Wandzel, A., Oh, Y., Fishman, M., Kumar, N., Wong, L.L.S., Tellex, S.: Multi-object search using object-oriented POMDPs[C]. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, p. 7194–7200 (2019)
Veiga, T.S., Miraldo, P., Ventura, R., et al.: Efficient object search for mobile robots in dynamic environments: Semantic map as an input for the decision maker[C]. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 2745–2750 (2016)
Li, J.K., Hsu, D., Lee, W.S.: Act to see and see to act: POMDP planning for objects search in clutter[C]. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 5701–5707 (2016)
Funding
This work is supported by the Key Laboratory of Software Engineering for Complex Systems. The work is also supported by the National Natural Science Foundation of China under Grant No.62172426.
Author information
Authors and Affiliations
Contributions
Dr. Shuo Yang made primary contributions to the conception or design of the work. Professor Xinjun Mao made optimization of the software architecture and concept reconsideration.
Corresponding authors
Ethics declarations
Ethics approval
Approval was obtained from the ethics committee of the National University of Defense Technology.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Consent for Publication
The participant has consented to the submission of the research manuscript to the journal.
Competing interests
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Availability of data and materials
All data generated or analyzed during this study are included in this published article. The source codes used during the research are available from the corresponding author on reasonable request.
Rights and permissions
About this article
Cite this article
Yang, S., Mao, X., Wang, Q. et al. A Hybrid Planning Approach for Accompanying Information-gathering in Plan Execution Monitoring. J Intell Robot Syst 103, 19 (2021). https://doi.org/10.1007/s10846-021-01480-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-021-01480-5