Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences

Lasota PA, Shah JA (2015) Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration. Hum Factors 57(1):21–33 Paulin R, Fraichard T, Reignier P (2019) Using human attention to address human–robot motion. IEEE Robot Autom Lett 4(2):2038–2045 Chandan G, Jain A, Jain H, Mohana (2018) Real time object detection and tracking using deep learning and opencv. In: 2018 International Conference on inventive research in computing applications (ICIRCA), pp 1305–1308 . https://doi.org/10.1109/ICIRCA.2018.8597266 Brousseau B, Rose J (2012) An energy-efficient, fast fpga hardware architecture for opencv-compatible object detection. In: 2012 International conference on field-programmable technology. IEEE, pp 166–173 Ulrich I, Borenstein J (2000) Vfh/sup */: local obstacle avoidance with look-ahead verification. In: Proceedings 2000 ICRA. Millennium conference. IEEE international conference on robotics and automation. Symposia proceedings (Cat. No.00CH37065), vol 3. pp 2505–2511, https://doi.org/10.1109/ROBOT.2000.846405 Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107. https://doi.org/10.1109/TSSC.1968.300136 Pacchierotti E, Christensen HI, Jensfelt P (2005) Human–robot embodied interaction in hallway settings: a pilot user study. In: ROMAN 2005. IEEE international workshop on robot and human interactive communication, 2005. IEEE, pp 164–171 Minguez J, Montano L (2004) Nearness diagram (nd) navigation: collision avoidance in troublesome scenarios. IEEE Trans Robot Autom 20(1):45–59 Ogren P, Leonard NE (2005) A convergent dynamic window approach to obstacle avoidance. IEEE Trans Robot 21(2):188–195. https://doi.org/10.1109/TRO.2004.838008 Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23–33. https://doi.org/10.1109/100.580977 Warren, CW (1989) Global path planning using artificial potential fields. In: 1989 IEEE international conference on robotics and automation. IEEE Computer Society, pp 316–317 Warren, CW (1990) Multiple robot path coordination using artificial potential fields. In: Proceedings, IEEE international conference on robotics and automation, vol 1. pp 500–505. https://doi.org/10.1109/ROBOT.1990.126028 Vadakkepat P, Tan KC, Wang M-L (2000) Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No.00TH8512), vol 1. pp 256–263. https://doi.org/10.1109/CEC.2000.870304 Sisbot EA, Clodic A, Fontmarty M, Brethes L, Alami, R et al (2006) Implementing a human-aware robot system. In: ROMAN 2006—The 15th IEEE international symposium on robot and human interactive communication. IEEE, pp 727–732 Chung SY, Huang HP (2011) Predictive navigation by understanding human motion patterns. Int J Adv Robot Syst 8(1):3 Ferrer G, Sanfeliu A (2014) Proactive kinodynamic planning using the extended social force model and human motion prediction in urban environments. In: 2014 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 1730–1735 Chung SY, Huang HP (2010) A mobile robot that understands pedestrian spatial behaviors. In: 2010 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 5861–5866 Bai H, Cai S, Ye N, Hsu D, Lee WS (2015) Intention-aware online pomdp planning for autonomous driving in a crowd. In: 2015 IEEE international conference on robotics and automation (icra). IEEE, pp 454–460 Foka A, Trahanias P (2010) Probabilistic autonomous robot navigation in dynamic environments with human motion prediction. Int J Soc Robot 2:79–94. https://doi.org/10.1007/s12369-009-0037-z Madani, O, Hanks, S, Condon, A (1999) On the undecidability of probabilistic planning and infinite-horizon partially observable markov decision problems. In: AAAI/IAAI. pp 541–548 Tsitskilis J, Papadimitriou C (1987) The complexity of Markov decision processes. Math Oper Res 12(3):441–450 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 (IROS). IEEE, pp 1343–1350 Mnih V, Kavukcuoglu K, Silver, D, Graves, A, Antonoglou, I, Wierstra, D, Riedmiller, M (2013) Playing atari with deep reinforcement learning. arXiv:1312.5602 Hessel, M, Modayil, J, Van Hasselt, H, Schaul, T, Ostrovski, G, Dabney, W, Horgan, D, Piot, B, Azar, M, Silver, D (2017) Rainbow: combining improvements in deep reinforcement learning. arXiv:1710.02298 Karkus, P, Hsu, D, Lee, WS (2017) Qmdp-net: Deep learning for planning under partial observability. In: Advances in neural information processing systems. pp 4694–4704 Cai, P, Luo, Y, Saxena, A, Hsu, D, Lee, WS (2019) Lets-drive: Driving in a crowd by learning from tree search. arXiv:1905.12197 Chandra S, Bharti AK (2013) Speed distribution curves for pedestrians during walking and crossing. Procedia Soc Behav Sci 104:660–667 Taylor MJD, Dabnichki P, Strike S (2005) A three-dimensional biomechanical comparison between turning strategies during the stance phase of walking. Hum Mov Sci 24(4):558–573 Gupta, A, Johnson, J, Fei-Fei, L, Savarese, S, Alahi, A (2018) Social gan: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2255–2264 Schulz AT, Stiefelhagen R (2015) A controlled interactive multiple model filter for combined pedestrian intention recognition and path prediction. In: 2015 IEEE 18th international conference on intelligent transportation systems. IEEE, pp 173–178 Rabinowitz, N, Perbet, F, Song, F, Zhang, C, Eslami, SMA, Botvinick, M (2018) Machine theory of mind. In: Dy J, Krause A (eds) Proceedings of the 35th international conference on machine learning, proceedings of machine learning research, vol 80. PMLR, pp 4218–4227 Pynadath, DV, Marsella, SC (2005) Psychsim: Modeling theory of mind with decision-theoretic agents. In: Proceedings of the 19th international joint conference on artificial intelligence, IJCAI’05. Morgan Kaufmann Publishers Inc., San Francisco, pp 1181–1186 Petković T, Marković I, Petrović I (2017) Human intention recognition in flexible robotized warehouses based on markov decision processes. In: Iberian robotics conference. Springer, pp 629–640 Petković T, Puljiz D, Marković I, Hein B (2019) Human intention estimation based on hidden markov model motion validation for safe flexible robotized warehouses. Robot Comput Integr Manuf 57:182–196 Baker CL, Tenenbaum JB (2014) Modeling human plan recognition using bayesian theory of mind. In: Plan, activity, and intent recognition: theory and practice. pp 177–204 Sisbot EA, Marin-Urias LF, Alami R, Simeon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):874–883 Rios-Martinez J, Renzaglia A, Spalanzani A, Martinelli A, Laugier C (2012) Navigating between people: a stochastic optimization approach. In: 2012 IEEE international conference on robotics and automation. IEEE, pp 2880–2885 Scandolo L, Fraichard T (2011) An anthropomorphic navigation scheme for dynamic scenarios. In: 2011 IEEE international conference on robotics and automation. IEEE, pp 809–814 Hall ET (1966) The hidden dimension, vol 609. Doubleday, Garden City Kirby R, Simmons R, Forlizzi J (2009) Companion: a constraint-optimizing method for person-acceptable navigation. In: RO-MAN 2009-the 18th IEEE international symposium on robot and human interactive communication. IEEE, pp 607–612 Boutilier C (1999) Sequential optimality and coordination in multiagent systems. IJCAI 99:478–485 Proper S, Tadepalli P (2009) Solving multiagent assignment markov decision processes. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems, vol 1. pp 681–688 Lauer M, Riedmiller M (2000) An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In: Proceedings of the seventeenth international conference on machine learning. Morgan Kaufmann, pp 535–542 Becker R, Zilberstein S, Lesser V, Goldman CV (2004) Solving transition independent decentralized Markov decision processes. J Artif Intell Res 22:423–455 Sapio A, Bhattacharyya SS, Wolf M (2018) Efficient solving of Markov decision processes on gpus using parallelized sparse matrices. In: 2018 conference on design and architectures for signal and image processing (DASIP). IEEE, pp 13–18 Qu G, Li N (2019) Exploiting fast decaying and locality in multi-agent mdp with tree dependence structure. In: 2019 IEEE 58th conference on decision and control (CDC). pp 6479–6486. https://doi.org/10.1109/CDC40024.2019.9029635 Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural Comput 11(1):229–242. https://doi.org/10.1162/089976699300016890 Gérin-Lajoie M, Richards CL, McFadyen BJ (2005) The negotiation of stationary and moving obstructions during walking: anticipatory locomotor adaptations and preservation of personal space. Mot Control 9(3):242–269 Kruse T, Pandey AK, Alami R, Kirsch A (2013) Human-aware robot navigation: a survey. Robot Auton Syst 61(12):1726–1743 Pinto J, Fern A (2017) Learning partial policies to speedup mdp tree search via reduction to iid learning. J Mach Learn Res 18(65):1–35 Tutsoy O, Barkana DE (2021) Model free adaptive control of the under-actuated robot manipulator with the chaotic dynamics. ISA transactions Tutsoy O, Brown M (2016) An analysis of value function learning with piecewise linear control. J Exp Theor Artif Intell 28(3):529–545 Jain S, Argall B (2019) Probabilistic human intent recognition for shared autonomy in assistive robotics. ACM Trans Hum Rob Interaction THRI 9(1):1–23 Fiore SM, Wiltshire TJ, Lobato EJ, Jentsch FG, Huang WH, Axelrod B (2013) Toward understanding social cues and signals in human-robot interaction: effects of robot gaze and proxemic behavior. Front Psychol 4:859 Wiltshire TJ, Warta SF, Barber D, Fiore SM (2017) Enabling robotic social intelligence by engineering human social-cognitive mechanisms. Cogn Syst Res 43:190–207