Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

DART: Diversity-Enhanced Autonomy in Robot Teams

  • Conference paper
  • First Online:
Robotics Research

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 10))

  • 2937 Accesses

Abstract

This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. While current approaches to multi-robot coordination have been successful in structured, well understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. The reason for this is not due to limitations in robot hardware, which has advanced significantly in the past decade, but in how multi-robot problems are solved. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed to the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which relies on a diverse set of control policies that work together synergistically to make multi-robot systems more resilient in unstructured and uncertain environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agha-mohammadi, A., Ure, N.K., How, J.P., Vian, J.: Health aware stochastic planning for persistent package delivery missions using quadrotors. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3389–3396 (2014)

    Google Scholar 

  2. Lonsdorf, K.: Hungry? Call your neighborhood delivery robot. NPR Morning Edition. http://www.npr.org/sections/alltechconsidered/2017/03/23/520848983/hungry-call-your-neighborhood-delivery-robot (2017)

  3. Sung, C., Ayanian, N., Rus, D.: Improving the performance of multi-robot systems by task switching. In: IEEE International Conference on Robotics and Automation, pp. 2999–3006 (2013)

    Google Scholar 

  4. Glaser, A.: These surveillance robots will work together to chase down suspects. Recode. https://www.recode.net/2017/4/18/15264908/surveillance-robots-network-cornell-suspects (2017)

  5. Schurr, N., Marecki, J., Tambe, M., Scerri, P., Kasinadhuni, N., Lewis, J.: The future of disaster response: humans working with multiagent teams using DEFACTO. In: AAAI Spring Symposium AI Technologies for Homeland Security (2005)

    Google Scholar 

  6. Jennings, J.S., Whelan, G., Evans, W.F.: Cooperative search and rescue with a team of mobile robots. In: International Conference on Advanced Robotics, pp. 193–200 (1997)

    Google Scholar 

  7. Chung, T.H., Clement, M.R., Day, M.A., Jones, K.D., Davis, D., Jones, M.: Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs. In: International Conference on Robotics and Automation, pp. 1255–1262 (2016)

    Google Scholar 

  8. Glaser, A.: Intel invented a way for a single operator to fly hundreds of drones at once. Recode. https://www.recode.net/2016/11/4/13517550/intel-single-operator-fly-hundreds-drones-shooting-star (2016)

  9. Hauert, S., Leven, S., Varga, M., Ruini, F., Cangelosi, A., Zufferey, J., Floreano, D.: Reynolds flocking in reality with fixed-wing robots: communication range vs. maximum turning rate. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5015–5020. IEEE (2011)

    Google Scholar 

  10. Kushleyev, A., Mellinger, D., Powers, C., Kumar, V.: Towards a swarm of agile micro quadrotors. Auton. Robot. 35(4), 287–300 (2013)

    Article  Google Scholar 

  11. Preiss, J.A., Hoenig, W., Sukhatme, G.S., Ayanian, N.: Crazyswarm: a large nano-quadcopter swarm. In: IEEE International Conference on Robotics and Automation (2017)

    Google Scholar 

  12. Rubenstein, M., Ahler, C., Nagpal, R.: Kilobot: a low cost scalable robot system for collective behaviors. In: IEEE International Conference on Robotics and Automation, pp. 3293–3298 (2012)

    Google Scholar 

  13. D’Andrea, R., Wurman, P.: Future challenges of coordinating hundreds of autonomous vehicles in distribution facilities. In: IEEE International Conference on Technologies for Practical Robot Applications, pp. 80–83 (2008)

    Google Scholar 

  14. Hagerty J.R.: Meet the new generation of robots for manufacturing. Wall Street J. (2015). Last accessed 2 June 2015

    Google Scholar 

  15. Barret, B.: Disney’s latest attraction? 300 drones flying in formation. Wired. https://www.wired.com/2016/11/disneys-latest-attraction-300-drones-flying-formation/ (2016)

  16. Kenny, C.: Why Do People Die in Earthquakes? The Costs, Benefits and Institutions of Disaster Risk Reduction in Developing Countries. The World Bank (2009)

    Google Scholar 

  17. DJI: DJI documents faster search and rescue responses with drones. DJI Newsroom. http://www.dji.com/newsroom/news/dji-documents-faster-search-and-rescue-responses-with-drones (2016)

  18. Liu, Y., Nejat, G.: Robotic urban search and rescue: a survey from the control perspective. J. Intel. Robot. Syst. 72(2), 147–165 (2013)

    Article  Google Scholar 

  19. Hoffman, L.R.: The group problem-solving process. In: Berkowitz, L. (ed.) Group Processes, pp. 101–114. Academic Press, New York (1978)

    Google Scholar 

  20. Hoffman, L.R., Maier, N.R.F.: Quality and acceptance of problem solutions by members of homogeneous and heterogeneous groups. J. Abnorm. Soc. Psychol. 62, 401–407 (1961)

    Article  Google Scholar 

  21. Nemeth, C.: Differential contributions of majority and minority influence. Psychol. Rev. 93, 23–32 (1986)

    Article  Google Scholar 

  22. Jackson, S.: Team composition in organizations. In: Worchel, S., Wood, W., Simpson, J. (eds.) Group Process and Productivity. Sage, London (1992)

    Google Scholar 

  23. Parker, L.E.: Alliance: an architecture for fault tolerant multirobot cooperation. IEEE Trans. Robot. Autom. 14(2), 220–240 (1998)

    Article  MathSciNet  Google Scholar 

  24. Pimenta, L.C.A., Kumar, V., Mesquita, R.C., Pereira, G.A.S.: Sensing and coverage for a network of heterogeneous robots. In: IEEE Conference on Decision and Control, pp. 3947–3952 (2008)

    Google Scholar 

  25. Huang, J., Farritor, S.M., Qadi, A., Goddard, S.: Localization and follow-the-leader control of a heterogeneous group of mobile robots. IEEE/ASME Trans. Mechatronics 11(2), 205–215 (2006)

    Article  Google Scholar 

  26. Prorok, A., Hsieh, M.A., Kumar, V.: The impact of diversity on optimal control policies for heterogeneous robot swarms. IEEE Trans. Robot. 33(2), 346–358 (2017)

    Article  Google Scholar 

  27. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  28. Sugawara, K., Kazama, T., Watanabe, T.: Foraging behavior of interacting robots with virtual pheromone. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 3074–3079 (2004)

    Google Scholar 

  29. Berman, S., Halasz, A., Kumar, V., Pratt, S.: Bio-inspired group behaviors for the deployment of a swarm of robots to multiple destinations. In: IEEE International Conference on Robotics and Automation, pp. 2318–2323 (2007)

    Google Scholar 

  30. Kumar, G.P., Buffin, A., Pavlic, T.P., Pratt, S.C., Berman, S.M.: A stochastic hybrid system model of collective transport in the desert ant aphaenogaster cockerelli. In: International Conference on Hybrid Systems: Computation and Control, pp. 119–124, New York, NY, USA. ACM (2013)

    Google Scholar 

  31. Tang, F., Parker, L.E.: A complete methodology for generating multi-robot task solutions using asymtre-d and market-based task allocation. In: IEEE International Conference on Robotics and Automation, pp. 3351–3358 (2007)

    Google Scholar 

  32. Parker, L.E., Tang, F.: Building multirobot coalitions through automated task solution synthesis. Proc. IEEE 94(7), 1289–1305 (2006)

    Article  Google Scholar 

  33. Balch, T.: Learning roles: Behavioral diversity in robot teams. In: AAAI Workshop on Multiagent Learning (1997)

    Google Scholar 

  34. Balch, T.: Hierarchic social entropy: an information theoretic measure of robot group diversity. Auton. Robot. 8(3), 209–238 (2000)

    Article  Google Scholar 

  35. Goldberg, D., Matarić, M.J.: Interference as a tool for designing and evaluating multi-robot controllers. In: Proceedings AAAI, pp. 637–642, Providence, Rhode Island (1997)

    Google Scholar 

  36. Mouret, J.B., Doncieux, S.: Encouraging behavioral diversity in evolutionary robotics: an empirical study. Evol. Comput. 20(1), 91–133 (2011). Last accessed 03 Nov 2016

    Google Scholar 

  37. Pugh, J.K., Soros, L.B., Stanley, K.O.: Quality diversity: a new frontier for evolutionary computation. Front. Robot. AI 3, 40 (2016)

    Article  Google Scholar 

  38. Vassiliades, V., Christodoulou, C.: Behavioral plasticity through the modulation of switch neurons. Neural Netw. 74, 35–51 (2016)

    Article  Google Scholar 

  39. Umedachi, T., Ito, K., Ishiguro, A.: Soft-bodied amoeba-inspired robot that switches between qualitatively different behaviors with decentralized stiffness control. Adapt. Behav. 23(2), 97–108 (2015)

    Article  Google Scholar 

  40. Jandt, J.M., Bengston, S., Pinter-Wollman, N., Pruitt, J.N., Raine, N.E., Dornhaus, A., Sih, A.: Behavioural syndromes and social insects: personality at multiple levels. Biol. Rev. 89, 48–67 (2014)

    Article  Google Scholar 

  41. Chittka, L., Skorupski, P., Raine, N.E.: Speed-accuracy tradeoffs in animal decision making. Trends Ecol. Evol. 24, 400–407 (2009)

    Article  Google Scholar 

  42. Burns, J.G., Dyer, A.G.: Diversity of speed-accuracy strategies benefits social insects. Curr. Biol. 18, R953–R954 (2008)

    Article  Google Scholar 

  43. Crosland, M.W.J.: Variation in ant aggression and kin discrimination ability within and between colonies. J. Insect Behav. 3, 359–379 (1990)

    Article  Google Scholar 

  44. Tavakoli, A., Nalbandian, H., Ayanian, N.: Crowdsourced coordination through online games (Late Breaking Report). In: ACM/IEEE International Conference on Human-Robot Interaction, Christchurch, New Zealand (2016)

    Google Scholar 

  45. Matignon, L., Laurent, G.J., Le Fort-Piat, N.: Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems. Knowl. Eng. Rev. 27(1), 1–31 (2012)

    Article  Google Scholar 

  46. Foerster, J., Assael, Y.M., de Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 2137–2145 (2016)

    Google Scholar 

  47. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Ulrike, V.L., Isabelle, G., Samy B., Hanna W., and Rob F. (Eds.) Proceedings of the 31 st International Conference on Neural Information Processing System (NIPS 17), pp. 6382–6393, Curran Associates Inc., USA (2017)

    Google Scholar 

  48. Matarić, M.J.: Learning to behave socially. Int. Conf. Simul. Adapt. Behav. 617, 453–462 (1994)

    Google Scholar 

  49. Recchia, T., Chung, J., Pochiraju, K.: Improving learning in robot teams through personality assignment. Biol. Inspired Cogn. Arch. 3, 51–63 (2013)

    Google Scholar 

  50. Buffet, O., Dutech, A., Charpillet, F.: Shaping multi-agent systems with gradient reinforcement learning. Auton. Agents Multi-Agent Syst. 15(2), 197–220 (2007)

    Article  Google Scholar 

  51. Amato, C., Konidaris, G.D., Kaelbling, L.P.: Planning with macro-actions in decentralized POMDPs. In: International Conference on Autonomous Agents and and multi-agent systems, pp. 1273–1280 (2014)

    Google Scholar 

  52. Amato, C., Konidaris, G.D., Cruz, G., Maynor, C.A., How, J.P., Kaelbling, L.P.: Planning for decentralized control of multiple robots under uncertainty. In: IEEE International Conference on Robotics and Automation, pp. 1241–1248 (2015)

    Google Scholar 

Download references

Acknowledgements

This work was supported by NSF CAREER (IIS-1553726) and the Okawa Foundation Research Award. Special thanks to Gaurav Sukhatme, M. Ani Hsieh, and Fei Sha for conversations and guidance in formulating this line of research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nora Ayanian .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayanian, N. (2020). DART: Diversity-Enhanced Autonomy in Robot Teams. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_2

Download citation

Publish with us

Policies and ethics