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Social Cues in the Autonomous Navigation of Indoor Mobile Robots

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Abstract

Robots are now operating in workspaces occupied by humans and the robots often need to avoid people while travelling to their desired goal. Humans follow some navigation conventions while walking, called as social cues, like avoiding an oncoming person from the left. It is important for the robots to display the same social cues to be socially accepted by the humans. Current works in social robot motion planning are limited to maintenance of a socially compliant distance. This paper exhibits a socialistic behavior of a robot avoiding an oncoming human by the preferred side. This paper extends the social force model to incorporate the social cues by adding new social forces. The paper also extends the geometric approach to incorporate the social cues by selecting the geometric gap as per the social preference. Finally, we propose a novel hybrid approach combining the social potential field and geometric method, wherein the preferred gap for navigation adds a new social force to the robot. The proposed approach maintains the proactive nature of the geometric approach as well as retains the reactive nature of the social force model. The hybrid method maintains a larger clearance and generates safer trajectories as compared to the baseline social potential field and follow the gap methods. The experiments are done with the Pioneer LX robot using vision cameras and a lidar for navigation operating at the Centre of Intelligent Robotics at IIIT Allahabad.

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References

  1. Breazeal C, Dautenhahn K, Kanda T (2016) Social robotics. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer handbooks. Springer, Cham

    Google Scholar 

  2. Alač M, Movellan J, Tanaka F (2011) When a robot is social: spatial arrangements and multimodal semiotic engagement in the practice of social robotics. Soc Stud Sci 41(6):893–926

    Article  Google Scholar 

  3. Weiss A, Bader M, Vincze M, Hasenhütl G, Moritsch S (2014) Designing a service robot for public space: an action and experiences-approach. In: Proceedings of the 2014 ACM/IEEE international conference on human–robot interaction, 2014

  4. Tsuda N, Harimoto S, Saitoh T, Konishi R (2009) Mobile robot with following and returning mode. In: The 18th IEEE international symposium on robot and human interactive communication, Toyama, pp 933–938

  5. Shiomi M, Kanda T, Glas DF, Satake S, Ishiguro H, Hagita N (2009) Field trial of networked social robots in a shopping mall. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, St. Louis, MO, pp 2846–2853

  6. Bera A, Randhavane T, Prinja R, Manocha D (2017) SocioSense: Robot navigation amongst pedestrians with social and psychological constraints. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), Vancouver, BC, pp 7018–7025

  7. Burgoon JK, Jones SB (1976) Toward a theory of personal space expectations and their violations. Hum Commun Res 2(2):131–146

    Article  Google Scholar 

  8. Pandey AK, Alami R (2010) A framework towards a socially aware mobile robot motion in human-centered dynamic environment. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, pp 5855–5860

  9. Kala R, Warwick K (2013) Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes. Eng Appl Artif Intell 26(5–6):1588–1601

    Article  Google Scholar 

  10. Gopinath D, Jain S, Argall BD (2017) Human-in-the-loop optimization of shared autonomy in assistive robotics. IEEE Robot Autom Lett 2(1):247–254

    Article  Google Scholar 

  11. Schirner G, Erdogmus D, Chowdhury K, Padir T (2013) The future of human-in-the-loop cyber-physical systems. Computer 46(1):36–45

    Article  Google Scholar 

  12. Ferrer G, Garrell A, Sanfeliu A (2013) Robot companion: a social force based approach with human awareness-navigation in crowded environments. In: IEEE/RSJ international conference on intelligent robots and systems, pp 1688–1694

  13. Sisbot EA, Marin-Urias LF, Alami R, Simeon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):874–883

    Article  Google Scholar 

  14. Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev E 51(5):4282

    Article  Google Scholar 

  15. Reif J, Wang H (1995) Social potential fields: a distributed behavioral control for autonomous robots. In: International workshop on algorithmic foundations of robotics, AK Peters, Wellesley, MA, USA, pp 431–459

  16. Sezer V, Gokasan M (2012) A novel obstacle avoidance algorithm: follow the gap method. Robot Auton Syst 60(9):1123–1134

    Article  Google Scholar 

  17. Kala R (2018) Routing-based navigation of dense mobile robots. Intell Serv Robot 11(1):25–39

    Article  Google Scholar 

  18. Murphy R (2000) The hybrid deliberative/reactive paradigm. In: Murphy R (ed) Introduction to AI robotics, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  19. Knepper RA, Rus D (2012) Pedestrian-inspired sampling-based multi-robot collision avoidance. In: IEEE international symposium on robot and human interactive communication, pp 94–100

  20. van den Berg J, Lin M, Manocha D (2008) Reciprocal velocity obstacles for real-time multi-agent navigation. In: Proceedings of the IEEE international conference on robotics and automation, pp 1928–1935

  21. van den Berg, J, Guy, SJ, Lin, M, Manocha, D (2011) Reciprocal n-body collision avoidance. In: Pradalier C, Siegwart R, Hirzinger G (eds) Robotics research. Springer tracts in advanced robotics, vol 70. Springer, Berlin. https://doi.org/10.1007/978-3-642-19457-3_1

    Chapter  Google Scholar 

  22. Zhang D, Xie Z, Li P, Yu J, Chen Z (2015) Real-time navigation in dynamic human environments using optimal reciprocal collision avoidance. In: International conference on mechatronics, pp 2232–2237

  23. Repiso E, Garrell A, Sanfeliu A (2019) Adaptive side-by-side social robot navigation to approach and interact with people. Int J Soc Robot. https://doi.org/10.1007/s12369-019-00559-2

    Article  Google Scholar 

  24. Demir M, Sezer V (2017) Improved follow the gap method for obstacle avoidance. In: IEEE international conference on advanced intelligent mechatronics, pp 1435–1440

  25. Ozdemir A, Sezer V (2018) Follow the gap with dynamic window approach. Int J Semant Comput 12(1):43–57

    Article  Google Scholar 

  26. Borenstein J, Koren Y (1991) The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans Robot Autom 7(3):278–288

    Article  Google Scholar 

  27. Ulrich I, Borenstein J (1998) VFH + : reliable obstacle avoidance for fast mobile robots. In: Proceedings of the 1998 IEEE international conference on robotics and automation, pp 1572–1577

  28. Ulrich I, Borenstein J (2000) VFH/sup*: local obstacle avoidance with look-ahead verification. In: Proceedings of the 2000 international conference on robotics and automation, pp 2505–2511

  29. Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of the IEEE international conference on robotics and automation, vol 2, pp 500–505

  30. Ferrer G, Garrell A, Sanfeliu A (2013) Social-aware robot navigation in urban environment. In: European conference on mobile robots, pp 331–336

  31. Zanlungo F, Ikeda T, Kanda T (2011) Social force model with explicit collision prediction. Europhys Lett 93:68005

    Article  Google Scholar 

  32. Shiomi M, Zanlungo F, Hayashi K, Kanda T (2014) Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model. Int J Soc Robot 6(3):443–455

    Article  Google Scholar 

  33. Takahashi M, Suzuki T, Cinquegrani F, Sorbello R, Pagello E (2009) A mobile robot for transport applications in hospital domain with safe human detection algorithm. In: 2009 IEEE international conference on robotics and biomimetics, pp 1543–1548

  34. Görür OC, Erkmen AM (2014) Elastic networks in reshaping human intentions by proactive social robot moves. In: The 23rd IEEE international symposium on robot and human interactive communication, pp 1012–1017

  35. Truong XT, Ngo TD (2017) Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion model. IEEE Trans Autom Sci Eng 14(4):1743–1760

    Article  Google Scholar 

  36. Ratsamee P, Mae Y, Ohara K, Takubo T, Arai T (2013) Human–robot collision avoidance using a modified social force model with body pose and face orientation. Int J Humanoid Robot 10(01):1350008

    Article  Google Scholar 

  37. Luber M, Spinello L, Silva J, Arras KO (2012) Socially-aware robot navigation: a learning approach. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, pp 902–907

  38. Long P, Liu W, Pan J (2017) Deep-learned collision avoidance policy for distributed multiagent navigation. IEEE Robot Autom Lett 2(2):656–663

    Article  Google Scholar 

  39. Khare A, Motwani R, Akash S, Patil J, Kala R (2018) Learning the goal seeking behaviour for mobile robots. In: 2018 3rd Asia-Pacific conference on intelligent robot systems, pp 56–60

  40. Chen YF, Liu M, Everett M, How JP (2017) Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. In: 2017 IEEE international conference on robotics and automation, pp 285–292

  41. Everett M, Chen YF, How JP (2018) Motion planning among dynamic, decision-making agents with deep reinforcement learning. In: 2018 IEEE/RSJ international conference on intelligent robots and systems, pp 3052–3059

  42. Chen YF, Everett M, Liu M, How JP (2017) Socially aware motion planning with deep reinforcement learning. In: 2017 IEEE/RSJ international conference on intelligent robots and systems, pp 1343–1350

  43. Abbeel P, Ng AY (2004) Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the twenty-first international conference on machine learning. ACM

  44. Ng AY, Russell SJ (2000) Algorithms for inverse reinforcement learning. In: Proceedings of the international conference on machine learning, p 2

  45. Kim B, Pineau J (2015) Socially adaptive path planning in human environments using inverse reinforcement learning. Int J Soc Robot 8(1):51–66

    Article  Google Scholar 

  46. Kretzschmar H, Spies M, Sprunk C, Burgard W (2016) Socially compliant mobile robot navigation via inverse reinforcement learning. Int J Robot Res 35(11):1289–1307

    Article  Google Scholar 

  47. Truong XT, Ngo TD (2016) Dynamic social zone based mobile robot navigation for human comfortable safety in social environments. Int J Soc Robot 8(5):663–684

    Article  Google Scholar 

  48. Paliwal SS, Kala R (2018) Maximum clearance rapid motion planning algorithm. Robotica 36(6):882–903

    Article  Google Scholar 

  49. Kruse T, Pandey AK, Alami R, Kirsch A (2013) Human-aware robot navigation: a survey. Robot Auton Syst 61(12):1726–1743

    Article  Google Scholar 

  50. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  51. Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503

    Article  Google Scholar 

  52. Quigley M, Gerkey B, Conley K, Faust J, Foote F, Leibs J, Berger E, Wheeler R, Ng A (2009) ROS: an open-source robot operating system. In: ICRA workshop on open source software, vol 3, no 3.2, p 5

  53. Reddy AK, Malviya V, Kala R. Video results. https://www.youtube.com/watch?v=JZt-798I7cw&list=PLWKZs3S5723nUEENFte7pvHRO49YIm8Tj

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Acknowledgements

This work is supported by the Science and Engineering Research Board, Department of Science and Technology, Government of India vide research grant ECR/2015/000406 and the Indian Institute of Information Technology, Allahabad, India. The authors wish to thank Abhinav Malviya for his help in recording and analysis of the socialistic behaviors used in this study.

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  1. Centre of Intelligent Robotics, Indian Institute of Information Technology, Allahabad, Prayagraj, India

    Arun Kumar Reddy, Vaibhav Malviya & Rahul Kala

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  1. Arun Kumar Reddy
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  2. Vaibhav Malviya
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  3. Rahul Kala
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Correspondence to Vaibhav Malviya.

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Reddy, A.K., Malviya, V. & Kala, R. Social Cues in the Autonomous Navigation of Indoor Mobile Robots. Int J of Soc Robotics 13, 1335–1358 (2021). https://doi.org/10.1007/s12369-020-00721-1

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