Abstract
In the human-robot integration environment, efficient and safe navigation is of great significance for mobile service robots. At present, human-robot integration environment is highly uncertain and dynamic, which brings new challenges to motion planning. In order to solve this problem, this paper proposes a dynamic obstacle avoidance strategy based on imitation learning in a Generative Adversarial Network (GAN) framework. When the robot detects a pedestrian around it, it generates an active obstacle avoidance point that maintains an appropriate distance from the pedestrian according to the pedestrian pose and the global path planned by the A* algorithm as a sub-goal to guide the robot for motion planning. In the experiment, the performance of the algorithm is evaluated by the number of entering the pedestrian person space, the time cost and the trajectory length. Compared with the Dynamic Window Approach (DWA) and Proactive Social Motion Model (PSMM) algorithms, the experimental results show that our proposed algorithm has better performance than the other two algorithms in the human-robot integration environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: 1985 IEEE International Conference on Robotics and Automation, pp. 500–505 (1985)
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)
LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Tech. Rep (TR98-11), Computer Science Dept, Iowa State University (1998)
Liu, X., Li, X., Su, H., Zhao, Y., Ge, S.S.: The opening workspace control strategy of a novel manipulator-driven emission source microscopy system. ISA Transactions (2022)
Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. 4(1), 23–33 (1997)
Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation, pp. 1928–1935 (2008)
Cheng, H., Zhu, Q., Liu, Z., Xu, T., Lin, L.: Decentralized navigation of multiple agents based on ORCA and model predictive control. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3446–3451 (2017)
Hall, E.T.: Proxemics. Curr. Anthropol. 9(2–3), 83–108 (1968)
Pacchierotti, E., Christensen, H.I., Jensfelt, P.: Human-robot embodied interaction in hallway settings: a pilot user study. In: 2005 IEEE International Workshop on Robot and Human Interactive Communication, pp. 164–171 (2005)
Pacchierotti, E., Christensen, H.I., Jensfelt, P.: Evaluation of passing distance for social robots. In: 2006 IEEE International Symposium on Robot and Human Interactive Communication, pp. 315–320 (2006)
Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S.: Effects of robot motion on human-robot collaboration. In: 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 51–58 (2015)
Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261–2680 (2009)
Antonini, G., Bierlaire, M., Weber, M.: Discrete choice models of pedestrian walking behavior. Transport. Res. Part B: Methodol. 40(8), 667–687 (2006)
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)
Ellis, D., Sommerlade, E., Reid, I.: Modelling pedestrian trajectory patterns with gaussian processes. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1229–1234 (2009)
Truong, X.-T., Ngo, T.D.: Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion model. IEEE Trans. Autom. Sci. Eng. 14(4), 1743–1760 (2017)
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social lstm: Human trajectory prediction in crowded spaces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961–971 (2016)
Nishimura, M., Yonetani, R.: L2B: Learning to balance the safety-efficiency trade-off in interactive crowd-aware robot navigation. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11004–11010 (2020)
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social gan: Socially acceptable trajectories with generative adversarial networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2255–2264 (2018)
Zhang, T., Wang, J., Meng, M.Q.-H.: Generative adversarial network based heuristics for sampling-based path planning. IEEE/CAA J. Automatica Sinica. 9(1), 64–74 (2021)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)
Acknowledgements
This work was made possible through funding from the National Key R &D Program of China grant #2020YFB1313601, National Science Foundation of China grant #61903267 and China Postdoctoral Science Foundation grant #2020M681691 awarded to Wenzheng Chi.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kong, Y., Wang, Y., Hong, Y., Ye, R., Chi, W., Sun, L. (2022). A Generative Adversarial Network Based Motion Planning Framework for Mobile Robots in Dynamic Human-Robot Integration Environments. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13817. Springer, Cham. https://doi.org/10.1007/978-3-031-24667-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-24667-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24666-1
Online ISBN: 978-3-031-24667-8
eBook Packages: Computer ScienceComputer Science (R0)