Skip to main content
Springer Nature Link
Log in

Measuring the Effectiveness of Readability for Mobile Robot Locomotion

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

The inclusion of social and human-like behaviors is recently emphasized within mobile robot locomotion. These behaviors are required to support the seamless integration of mobile robots into environments which they share with humans. This work demonstrates a distinct benefit of these behaviors, which goes beyond positive apperception. It is shown that human-like robot locomotion reduces the planning effort for all agents within an environment. This effect is revealed in an experiment that compares human locomotion during avoidance of an oncoming human or wheeled robot. In order to evaluate recorded data, a framework for the analysis of human trajectories is proposed. Confidence intervals based on a spline regression model are used to account for variance in the data. This qualitative method is complemented by a comparative analysis, that quantifies differences and analogies within the data. Thus, the framework allows for a statistically feasible qualitative and quantitative analysis of trajectories. Results show, that extra planning effort for the avoidance is prevented by readable human-like robot locomotion. The study indicates that locomotion planning requires less effort from subjects if the mutual trajectory prediction is facilitated by robots that externalize intentions and comply with human-like behaviors.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Albrecht S, Basili P, Glasauer S, Leibold (Sobotka) M, Ulbrich M (2012) Modeling and analysis of human navigation with crossing interferer using inverse optimal control. In: International conference on mathematical modelling, pp 158–163

  2. Allgöwer G, Badgwell T, Qin J, Rawlings J, Wright S (1999) Nonlinear predictive control and moving horizon estimationan introductory overview. Advances in contro. Springer, London

    Google Scholar 

  3. Althoff D, Kuffner J, Wollherr D, Buss M (2012) Safety assessment of robot trajectories for navigation in uncertain and dynamic environments. Auton Robots 32(3):285–302

  4. Arechavaleta G, Laumond J, Hicheur H, Berthoz A (2006) The nonholonomic nature of human locomotion: a modeling study. In: International conference on biomedical robotics and biomechatronics, pp 158–163

  5. Arechavaleta G, Laumond JP, Hicheur H, Berthoz A (2008) An optimality principle governing human walking. Trans Robot 24(1):5–14

    Article  Google Scholar 

  6. Basili P, Huber M, Kourakos O, Lorenz T, Brandt T, Hirche S, Glasauer S (2012) Inferring the goal of an approaching agent: a human-robot study. In: International Workshop on Robots and Human Interactive Communications, pp 527–532

  7. Basili P, Sağlam M, Kruse T, Huber M, Kirsch A, Glasauer S (2013) Strategies of locomotor collision avoidance. Gait Posture 37(3):385–390

    Article  Google Scholar 

  8. van Basten B, Jansen S, Karamouzas I (2009) Exploiting motion capture to enhance avoidance behaviour in games. Motion Games 5884:29–40

    Article  Google Scholar 

  9. Bitgood S, Dukes S (2006) Not another step! economy of movement and pedestrian choice point behavior in shopping malls. Environ Behav 38(3):394–405

    Article  Google Scholar 

  10. Breazeal C, Kidd C, Thomaz A, Hoffman G, Berlin M (2005) Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. In: International Conference on Intelligent Robots and Systems, pp 708–713

  11. Buchin K, Buchin M, Van Kreveld M, Luo J (2011) Finding long and similar parts of trajectories. Comput Geom 44(9):465–476

    Article  MathSciNet  MATH  Google Scholar 

  12. Buchin K, Buchin M, Van Kreveld M, Löffler M, Silveira RI, Wenk C, Wiratma L (2013) Median trajectories. Algorithmica 66(3):595–614

    Article  MathSciNet  MATH  Google Scholar 

  13. Buss M, et al (2011) Towards proactive human-robot interaction in human environments. In: International Conference on Cognitive Infocommunications, pp 1–6

  14. Buss M, et al (2015) Iuro—Soziale Mensch-Roboter-Interaktion in den Straßen von München. at – Automatisierungstechnik

  15. Caraian S, Kirchner N, Colborne-Veel P (2015) Moderating a robot’s ability to influence people through its level of sociocontextual interactivity. In: International Conference on Human-Robot Interaction, pp 149–156

  16. Carton D, Turnwald A, Wollherr D, Buss M (2012) Proactively approaching pedestrians with an autonomous mobile robot in urban environments. In: International symposium on experimental robotics, Springer, pp 199–214

  17. Carton D, Turnwald A, Olszowy W, Wollherr D, Buss M (2014) Using penalized spline regression to calculate mean trajectories including confidence intervals of human motion data. In: Workshop on advanced robotics and its social impacts, pp 76–81

  18. Cassisi C, Montalto P, Pulvirenti A (2012) Similarity measures and dimensionality reduction techniques for time series data mining. In: Advances in Data Mining, Knowledge Discovery and Applications, INTECH, chap 3

  19. Cohen J (1988) Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates, Hillsdale, NJ

    MATH  Google Scholar 

  20. Cox D, Hinkley D (1974) Theoretical statistics. Chapman & Hal, Boca Raton

    Book  MATH  Google Scholar 

  21. Csibra G, Gergely G (2007) Obsessed with goals: functions and mechanisims of teleological interpretation of action in humans. Acta Psychol 124(1):60–78

    Article  Google Scholar 

  22. Dragan A, Srinivasa S (2013) Generating legible motion. In: Robotics: Science and Systems

  23. Dragan A, Srinivasa S (2014) Familiarization to robot motion. In: International conference on human-robot interaction, pp 366–373

  24. Dragan A, Bauman S, Forlizzi J, Srinivasa S (2015) Effects of robot motion on human-robot collaboration. In: International conference on human-robot interaction, pp 51–58

  25. Duffy BR (2003) Anthropomorphism and the social robot. Robot Auton Syst 42(3):177–190

    Article  MATH  Google Scholar 

  26. Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, boca raton

    Book  MATH  Google Scholar 

  27. Fahrmeir L, Kneib T, Lang S (2009) Regression. Springer, Berlin

    Book  MATH  Google Scholar 

  28. Fajen B, Warren W (2003) Behavioral dynamics of steering, obstacle avoidance, and route selection. Exp Psychol 29(2):343

    Google Scholar 

  29. Fink P, Foo P, Warren W (2007) Obstacle avoidance during walking in real and virtual environments. Trans Appl Percept 4(1):2

    Article  Google Scholar 

  30. Frith U, Frith C (2010) The social brain: allowing humans to boldly go where no other species has been. Philos Trans R Soc Lond B 365(1537):165–176

    Article  Google Scholar 

  31. Goffman E (1971) Relations in public: microstudies of the public order. Harper and Row, New York

    Google Scholar 

  32. Gudmundsson J, van Kreveld M, Speckmann B (2007) Efficient detection of patterns in 2d trajectories of moving points. Geoinformatica 11(2):195–215

    Article  Google Scholar 

  33. Hall ET (1966) The hidden dimension: man’s use of space in public and private. The Bodley Head Ltd, London

    Google Scholar 

  34. Hartnett J, Bailey K, Hartley C (1974) Body height, position, and sex as determinants of personal space. J Psychol 87:129–136

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Hicheur H, Pham Q, Arechavaleta G, Laumond J, Berthoz A (2007) The formation of trajectories during goal-oriented locomotion in humans. i. a. stereotyped behaviour. Eur J Neurosci 26(8):2376–2390

    Article  Google Scholar 

  37. Houska B, Ferreau HJ, Diehl M (2011) Acado toolkit—an open-source framework for automatic control and dynamic optimization. Optim Control Appl Methods 32(3):298–312

  38. Huber M, Su YH, Krüger M, Faschian K, Glasauer S, Hermsdörfer J (2014) Adjustments of speed and path when avoiding collisions with another pedestrian. PLoS One 9(2): e89589. doi:10.1371/journal.pone.0089589

  39. Karamouzas I, Overmars MH (2010) Simulating human collision avoidance using a velocity-based approach. VRIPHYS 10:125–134

    Google Scholar 

  40. Kato Y, Kanda T, Ishiguro H (2015) May i help you? Design of human-like polite approaching behavior. In: International conference on human-robot interaction, pp 35–42

  41. Keogh E, Pazzani M (2000) Scaling up dynamic time warping for datamining applications. In: International conference on knowledge discovery and data mining, pp 285–289

  42. 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 

  43. Kirby R, Simmons R, Forlizzi J (2009) Companion: A constraint-optimizing method for person-acceptable navigation. In: International symposium on robot and human interactive communication, pp 607–612

  44. Kirchner N, Alempijevic A (2012) A robot centric perspective on the HRI paradigm. Human-Robot Interact 1(2):135–157

    Google Scholar 

  45. van Kreveld M, Luo J (2007) The definition and computation of trajectory and subtrajectory similarity. In: International symposium on advances in geographic information systems, p 44

  46. Kruse T, Basili P, Glasauer S, Kirsch A (2012) Legible robot navigation in the proximity of moving humans. In: International workshop on advanced robotics and its social impacts, pp 83–88

  47. Kruse T, Kirsch A, Khambhaita H, Alami R (2014) Evaluating directional cost models in navigation. In: International conference on human-robot interaction, pp 350–357

  48. Kuderer M, Kretzschmar H, Sprunk C, Burgard W (2012) Feature-based prediction of trajectories for socially compliant navigation. In: Robotics: science and systems, pp 193 – 200

  49. Li Z, Deng J, Lu R, Xu Y, Bai J, Su CY (2015) Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach. TransSyst Man Cybernet 99:1

  50. Li Z, Xiao H, Yang C, Zhao Y (2015) Model predictive control of nonholonomic chained systems using general projection neural networks optimization. Trans Syst Man Cybernet Syst 45(10):1313–1321

    Article  Google Scholar 

  51. Lichtenthäler C, Kirsch A (2013) Towards legible robot navigation—how to increase the intend expressiveness of robot navigation behavior. ICSR

  52. Lichtenthäler C, Lorenz T, Kirsch A (2012) Influence of legibility on perceived safety in a virtual human-robot path crossing task. In: International symposium on robot and human interactive communication, pp 676–681

  53. McNeill Alexander R (2002) Energetics and optimization of human walking and running: the 2000 raymond pearl memorial lecture. Hum Biol 14(5):641–648

    Article  Google Scholar 

  54. Mombaur K, Truong A, Laumond JP (2009) From human to humanoid locomotion—an inverse optimal control approach. Autonom Robot 28(3):369–383

    Article  Google Scholar 

  55. Nass C, Moon Y (2000) Machines and mindlessness: social responses to computers. Int J Soc Issues 56(1):81–103

    Article  Google Scholar 

  56. Nilsson NJ (1984) Shakey the robot. SRI International Technical Note 325

  57. Olivier AH, Kulpa R, Pettré J, Crétual A (2009) Motion in games, lecture notes in computer science. In: Egges A, Geraerts R, Overmars M (eds) A velocity-curvature space approach for walking motions analysis, vol 5884. Springer, Berlin, pp 104–115

    Google Scholar 

  58. Olivier AH, Marin A, Crétual A, Pettré J (2012) Minimal predicted distance: a common metric for collision avoidance during pairwise interactions between walkers. Gait Posture 36(3):399–404

    Article  Google Scholar 

  59. Olivier AH, Marin A, Crétual A, Berthoz A, Pettré J (2013) Collision avoidance between two walkers: role-dependent strategies. Gait Posture 38(4):751–756

    Article  Google Scholar 

  60. Pacchierotti E, Christensen H, Jensfelt P (2006) Evaluation of passing distance for social robots. In: International symposim on robot and human interactive communication (ROMAN), pp 315–320

  61. Papadopoulos AV, Bascetta L, Ferretti G (2014) A comparative evaluation of human motion planning policies. In: IFAC world congress

  62. Paris S, Pettr J, Donikian S (2007) Pedestrian reactive navigation for crowd simulation: a predictive approach. Comput Graph Forum 26(3):665–674

    Article  Google Scholar 

  63. Pellegrini S, Ess A, Schindler K, Van Gool L (2009) You’ll never walk alone: modeling social behavior for multi-target tracking. In: International conference on computer vision, pp 261–268

  64. Pettré J, Ondrej J, Olivier AH, Crtual, Donikian S (2009) Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Eurographics symposium on computer animation

  65. Prassler E, Scholz J, Fiorini P (1999) Navigating a robotic wheelchair in a railway station during rush hour. Int J Robot Res 18(7):711–727

    Article  Google Scholar 

  66. Ratanamahatana CA, Lin J, Gunopulos D, Keogh E, Vlachos M, Das G (2010) Mining time series data. Springer, New York

    Google Scholar 

  67. Reeves B, Nass C (1996) The media equation: how people treat computers, television, and new media like real people and places. cambridge University Press, Cambridge

    Google Scholar 

  68. Reynolds C (1999) Steering behaviors for autonomous characters. In: Game developers conference, pp 763–782

  69. Rios-Martinez J, Spalanzani A, Laugier C (2014) From proxemics theory to socially-aware navigation: a survey. Int J Soc Robot 7(2):137–153

    Article  Google Scholar 

  70. 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 

  71. Sobel R, Lillith N (1975) Determinant of nonstationary personal space invasion. J Psychol 97:39–45

    Google Scholar 

  72. Sparrow W, Newell K (1998) Metabolic energy expenditure and the regulation of movement economy. Psychon Bull Rev 5(2):173–196

    Article  Google Scholar 

  73. Su J, Kurtek S, Klassen E, Srivastava A (2014) Statistical analysis of trajectories on riemannian manifolds: bird migration, hurricane tracking and video surveillance. Ann Appl Stat 8(1):530–552

    Article  MathSciNet  MATH  Google Scholar 

  74. Takayama L, Dooley D, Ju W (2011) Expressing thought: improving robot readability with animation principles. In: International conference on human-robot interaction, pp 69–76

  75. Turnwald A, Olszowy W, Wollherr D, Buss M (2014) Interactive navigation of humans from a game theoretic perspective. In: International conference on intelligent robots and systems, pp 703–708

  76. Turnwald A, Althoff D, Wollherr D, Buss M (2016) Understanding human avoidance behavior: interaction-aware decision making based on game theory. Int J Soc Robot 8(2):331–351

    Article  Google Scholar 

  77. Van Den Berg J, Guy S, Lin M, Manocha D (2011) Reciprocal n-body collision avoidance. Robot Res 70:3–19

  78. Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: International conference on data engineering, pp 673–684

  79. Wilkie D, Van Den Berg J, Manocha D (2009) Generalized velocity obstacles. In: International conference on intelligent robots and systems, pp 5573–5578

  80. Wolff M (1973) Notes on the behaviour of pedestrians. In: Silverman D (ed) People in places: the sociology of the familiar. Praeger, New York, pp 35–48

    Google Scholar 

  81. Wolfinger N (1995) Passing moments: some social dynamics of pedestrian interaction. J Contemp Ethnogr 24(3):323–340

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported within an ERC Advanced Grant, SHRINE (http://www.shrine-project.eu), Agreement No. 267877. The authors gratefully thank all participants for their valuable time.

Author information

Authors and Affiliations

  1. Chair of Automatic Control Engineering, Technical University of Munich, 80333, Munich, Germany

    Daniel Carton & Dirk Wollherr

  2. Department of Statistics, Ludwig-Maximilians-Universität München, 80539, Munich, Germany

    Wiktor Olszowy

Authors
  1. Daniel Carton
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wiktor Olszowy
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Dirk Wollherr
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Daniel Carton.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carton, D., Olszowy, W. & Wollherr, D. Measuring the Effectiveness of Readability for Mobile Robot Locomotion. Int J of Soc Robotics 8, 721–741 (2016). https://doi.org/10.1007/s12369-016-0358-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-016-0358-7

Keywords