Abstract
Active balancing in autonomous humanoid robots is a challenging task due to the complexity of combining a walking gait with dynamic balancing, vision and high-level behaviors. Humans not only walk successfully over even and uneven terrain, but can recover from the interaction of external forces such as impacts with obstacles and active pushes. While push recovery has been demonstrated successfully in, expensive robots, it is more challenging with robots that are inexpensive, with limited power in actuators and less accurate sensing. This work describes a closed-loop feedback control method that uses an accelerometer and gyroscope to allow an inexpensive humanoid robot to actively balance while walking and recover from pushes. Three common balancing strategies: center of pressure, centroidal moment pivot, and step-out, for biped robots are studied. An experiment is performed to test three hand-tuned closed-loop feedback control configurations; using only the gyroscope, only the accelerometer, and a combination of both sensors to recover from pushes. Each of the sensors is discretized into four discrete domains in order to categorize pushes with different strengths. Experimental results show that the combination of gyroscope and accelerometer outperforms the other methods with 100% recovery from a light push and 70% recovery from a strong push. The proposed closed-loop feedback control is examined in both simulation and real-world.
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References
Huang Q, Yokoi K, Kajita S, Kaneko K, Aral H, Koyachi N, Tanie K (2001) Planning walking patterns for a biped robot. IEEE Trans Robot Autom 17(3):280–289
Shojaeipour S, Parhizkar B, Hosseinmemar A, Shojaeipour A, Esfandiari H, Mobasheri E, Gebril B, Mohana Z (2010) Laser-pointer rangefinder between mobile robot and obstacles via webcam based. In: Key Engineering Materials. Trans Tech Publ, Vol 447, pp 609–613
Hosseinmemar A, Baltes J, Anderson J, Lau MC, Lun CF, Wang Z (2018) Closed-loop push recovery for an inexpensive humanoid robot. In: Mouhoub M, Sadaoui S, Ait Mohamed O, Ali M (eds) Recent Trends and Future Technology in Applied Intelligence. Springer International Publishing, Cham, pp 233–244
Ishiguro H, Ono T, Imai M, Maeda T, Kanda T, Nakatsu R (2001) Robovie: an interactive humanoid robot. Ind Robot: Int J 28(6):498–504
Cousins S (2010) ROS on the PR2. IEEE Robot Autom Mag 17(3):23–25
Stephens B (2007) Humanoid push recovery. In: Proceedings of Humanoids-2007. IEEE, Pittsburgh, pp 589–595
Stephens BJ, Atkeson CG (2010) Push recovery by stepping for humanoid robots with force controlled joints. In: Proceedings of Humanoids-2010, Nashville, pp 52–59
Kim JY, Park IW, Oh JH (2007) Walking control algorithm of biped humanoid robot on uneven and inclined floor. J Intell Robot Syst 48:457–484
Kuindersma S, Deits R, Fallon M, Valenzuela A, Dai H, Permenter F, Koolen T, Marion P, Tedrake R (2016) Optimization-based locomotion planning, estimation, and control design for Atlas. Auton Robot 40(3):429–455
Baltes J, Tu KY, Sadeghnejad S, Anderson J (2017) Hurocup: competition for multi-event humanoid robot athletes. Knowl Eng Rev 32:1–14
Pratt J, Carff J, Drakunov S, Goswami A (2006) Capture point: A step toward humanoid push recovery. In: 2006 6th IEEE-RAS International Conference on Humanoid Robots. IEEE, pp 200–207
Lee SH, Goswami A (2007) Reaction mass pendulum (RMP): An explicit model for centroidal angular momentum of humanoid robots. In: Proceedings of ICRA-2007, Rome, pp 4667–4672
Kajita S, Morisawa M, Miura K, Nakaoka S, Harada K, Kaneko K, Kanehiro F, Yokoi K (2010) Biped walking stabilization based on linear inverted pendulum tracking. In: Proceedings of IROS 2010. IEEE, Taipei, pp 4489–4496
Vukobratović M (1973) How to control artificial anthropomorphic systems. IEEE Trans Syst Man Cybern 3 (5):497–507
Yi SJ, Zhang BT, Hong D, Lee DD (2011) Online learning of a full body push recovery controller for omnidirectional walking. In: IEEE-RAS International conference on humanoid robots
Hirai K, Hirose M, Haikawa Y, Takenaka T (1998) The development of Honda humanoid robot. In: Proceedings of ICRA-98, pp 1321–1326
Morisawa M, Kanehiro F, Kaneko K, Mansard N, Sola J, Yoshida E, Yokoi K, Laumond JP (2010) Combining suppression of the disturbance and reactive stepping for recovering balance. In: Proceedings of IROS 2010, Taipei, pp 3150–3156
Cho BK, Park SS, Oh JH (2010) Stabilization of a hopping humanoid robot for a push. In: Proceedings of IROS 2010, Taipei, pp 60–65
Missura M, Behnke S (2013) Omnidirectional capture steps for bipedal walking. In: Proceedings of Humanoids-2013, Atlanta, pp 401–408
Collins JJ, De Luca CJ (1993) Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories. Exper Brain Res 95(2):308–318
Schmitz A, Missura M, Behnke S (2011) Learning footstep prediction from motion capture. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6556 LNAI, pp 97–108
Iverach-Brereton C (2015) Rocking the Bongo Board: Humanoid Robotic Balancing on Dynamic Terrain. Master’s thesis, University of Manitoba, Winnipeg, Manitoba
Iverach-Brereton C, Postnikoff B, Baltes J, Hosseinmemar A (2017) Active balancing and turning for alpine skiing robots. Knowl Eng Rev 32:e6, 1–10
Tajima R, Honda D, Suga K (2009) Fast running experiments involving a humanoid robot. In: Proceedings of ICRA-2009, pp 1571–1576
Sreenath K, Park HW, Poulakakis I, Grizzle JW (2011) A compliant hybrid zero dynamics controller for stable, efficient and fast bipedal walking on MABEL. Int J Robot Res 30(9):1170–1193
Yun SK, Goswami A (2011) Momentum-based reactive stepping controller on level and non-level ground for humanoid robot push recovery. In: Proceedings of IROS-2011, San Francisco
Lee SH, Goswami A (2010) Ground reaction force control at each foot: A momentum-based humanoid balance controller for non-level and non-stationary ground. In: Proceedings of IROS-2010, Taipei, pp 3157–3162
Hofmann A (2006) Robust execution of bipedal walking tasks from biomechanical principles. PhD thesis, Massachusetts Institute of Technology
Robotis: Dynamixel (2018) http://www.robotis.us/dynamixel/
Robotis: Usb2dynamixel (2017) http://support.robotis.com/en/product/auxdevice/interface/usb2dxl_manual.htm/
Trossenrobotics: Robotis usb2dynamixel adapter (2018) https://www.trossenrobotics.com/robotis-bioloid-usb2dynamixel.aspx
Pratt J, Carff J, Drakunov S, Goswami A (2006) Capture point: a step toward humanoid push recovery. In: Proceedings of Humanoids-2006, Genoa
System RO (2016) Kinetic Kame robot operating system http://www.ros.org/
Gazebo: Why gazebo? (1999) http://www.quanmax.com/site/product/qutepc-3000-series/
System RO (2017) Rviz. http://wiki.ros.org/rviz
RoboCup: Humanoid league technical challenge. http://www.robocuphumanoid.org/wp-content/uploads/HumanoidLeagueRules2015-06-29.pdf
Ramezani S, Setaieshi A, Pourmohammadi N, Yarahmadi P, Arvand A, Fallah F, Hosseinmemar A, Santos J, Morris K, Lau MC et al (2017) Autman humanoid teen size team description paper robocup 2017 humanoid robot league. Teen-size Humanoid League, Team Description Paper, Nagoya
Anderson J, Baltes J, Tu KY (2009) Improving robotics competitions for real-world evaluation of ai. In: Proceedings of the AAAI Spring Symposium on Experimental Design for Real-World Systems, Stanford
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Hosseinmemar, A., Baltes, J., Anderson, J. et al. Closed-loop push recovery for inexpensive humanoid robots. Appl Intell 49, 3801–3814 (2019). https://doi.org/10.1007/s10489-019-01446-z
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DOI: https://doi.org/10.1007/s10489-019-01446-z