Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 152 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 26 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 101 tok/s Pro
Kimi K2 199 tok/s Pro
GPT OSS 120B 430 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Human-Centered Autonomy for UAS Target Search (2309.06395v3)

Published 12 Sep 2023 in cs.RO and cs.HC

Abstract: Current methods of deploying robots that operate in dynamic, uncertain environments, such as Uncrewed Aerial Systems in search & rescue missions, require nearly continuous human supervision for vehicle guidance and operation. These methods do not consider high-level mission context resulting in cumbersome manual operation or inefficient exhaustive search patterns. We present a human-centered autonomous framework that infers geospatial mission context through dynamic feature sets, which then guides a probabilistic target search planner. Operators provide a set of diverse inputs, including priority definition, spatial semantic information about ad-hoc geographical areas, and reference waypoints, which are probabilistically fused with geographical database information and condensed into a geospatial distribution representing an operator's preferences over an area. An online, POMDP-based planner, optimized for target searching, is augmented with this reward map to generate an operator-constrained policy. Our results, simulated based on input from five professional rescuers, display effective task mental model alignment, 18\% more victim finds, and 15 times more efficient guidance plans then current operational methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. Hunter Ray, Ryan Singer and Nisar Ahmed “A Review of the Operational Use of UAS in Public Safety Emergency Incidents” In Proc. of the International Conference on Unmanned Aircraft Systems (ICUAS) Dubrovnik, Croatia: IEEE, 2022, pp. 922–931
  2. William B. Rouse and Nancy M. Morris “On looking into the black box: Prospects and limits in the search for mental models” In Psychological Bulletin 100, 1986, pp. 349–363
  3. Aaquib Tabrez, Matthew B. Luebbers and Bradley Hayes “A Survey of Mental Modeling Techniques in Human–Robot Teaming” In Current Robotics Reports 1, 2020, pp. 259–267
  4. “Toward Genuine Robot Teammates: Improving Human-Robot Team Performance Using Robot Shared Mental Models” In Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems Auckland, New Zealand: IFAAMAS, 2020, pp. 429–437
  5. Matthias Scheutz, Scott A. DeLoach and Julie A. Adams “A Framework for Developing and Using Shared Mental Models in Human-Agent Teams” In Journal of Cognitive Engineering and Decision Making 11.3, 2017, pp. 203–224
  6. Stefano V. Albrecht and Peter Stone “Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems” In Artificial Intelligence 258, 2018, pp. 66–95
  7. Stewart Jamieson, Jonathan P. How and Yogesh Girdhar “Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments” In 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 1806–1812
  8. Christopher A. Miller “Delegation and intent expression for human-automation interaction” In Proc. of the International Conference on Human-Computer Interaction in Aerospace, HCI-Aero ’14 New York, NY: Association for Computing Machinery, 2014
  9. “Multi-modal active perception for information gathering in science missions” In Autonomous Robots 43, 2019, pp. 1827–1853
  10. “A survey of inverse reinforcement learning: Challenges, methods and progress” In Artificial Intelligence 297, 2021, pp. 103500
  11. “Inverse Reward Design” In Advances in Neural Information Processing Systems 30 Curran Associates, Inc., 2017
  12. “The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications” In Proc. of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023
  13. Derek Kingston, Steven Rasmussen and Laura Humphrey “Automated UAV tasks for search and surveillance” In 2016 IEEE Conference on Control Applications (CCA), 2016, pp. 1–8 IEEE
  14. CB Thomas “US coast guard addendum to the US NSS to the IAMSAR” In United States Coast Guard, Tech. Rep. COMDTINST MI6130 2, 2013
  15. Frédéric Bourgault, Tomonari Furukawa and Hugh F Durrant-Whyte “Optimal search for a lost target in a bayesian world” In Field and Service Robotics: Recent Advances in Reserch and Applications Springer, 2006, pp. 209–222
  16. Mykel J Kochenderfer, Tim A Wheeler and Kyle H Wray “Algorithms for decision making” MIT press, 2022
  17. “Planning search and rescue missions for UAV teams” In Proceedings of the Twenty-second European Conference on Artificial Intelligence, 2016, pp. 1777–1778
  18. “Multi-resolution POMDP planning for multi-object search in 3D” In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 2022–2029 IEEE
  19. “Multi-object search using object-oriented POMDPs” In 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 7194–7200 IEEE
  20. “Online planning for target object search in clutter under partial observability” In 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 8241–8247 IEEE
  21. Nisar R. Ahmed, Eric M. Sample and Mark Campbell “Bayesian Multicategorical Soft Data Fusion for Human–Robot Collaboration” In IEEE Transactions on Robotics 29, 2013, pp. 189–206
  22. “HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing” In Press In IEEE Transactions on Robotics, 2022
  23. “Structured synthesis and compression of semantic human sensor models for Bayesian estimation” In 2016 American Control Conference (ACC) Boston, MA: IEEE, 2016, pp. 5479–5485
  24. Charles Luke Burks “Active Collaborative Planning and Sensing in Human-Robot Teams”, 2020
  25. Christopher M. Bishop “Pattern Recognition and Machine Learning” Cambridge U.K.: Springer, 2006
  26. “Human-Centered Autonomy for UAS Target Search”, 2023 arXiv:2309.06395 [cs.RO]
  27. “Monte-Carlo planning in large POMDPs” In Advances in neural information processing systems 23, 2010
  28. “Cumulated gain-based evaluation of IR techniques” In ACM Transactions on Information Systems 20, 2002, pp. 422–446
  29. “Fully bayesian learning and spatial reasoning with flexible human sensor networks” In Proc. of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems - ICCPS ’15 Seattle, Washington: ACM Press, 2015, pp. 80–89
  30. “Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits” In 2023 IEEE International Conference on Robotics and Automation, 2023
Citations (2)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 1 like.

Upgrade to Pro to view all of the tweets about this paper: