Avoiding Obstacles during Push Recovery Using Real-Time Vision Feedback

This paper introduces an obstacle-avoiding algorithm for bipedal robots, especially in push recovery situations. Typically, There are many algorithms that plan footstep to avoid obstacles based on vision recognition data. However, if the robot is pushed, the planned footprint will change, and thus, there is no guarantee that it will avoid obstacles. Although modified stepping positions can be limited, the robot’s stability is not assured. Our proposed algorithm focuses on avoiding obstacles through vision recognition in push recovery situations and generating compensation actions for instability by restricting modified footsteps. We fuse vision feedback with our previous push recovery algorithm, which optimizes the ankle, hip, and stepping strategies. We build simple grid data using vision recognition and apply it to the inequality constraint of the stepping position. We validate the effectiveness of our algorithm using the bipedal platform GAZELLE with the Kinect V2 RGBD sensor.

[1]  L. Nashner,et al.  The organization of human postural movements: A formal basis and experimental synthesis , 1985, Behavioral and Brain Sciences.

[2]  Pierre-Brice Wieber,et al.  Ankle, hip and stepping strategies for humanoid balance recovery with a single Model Predictive Control scheme , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[3]  Aghil Yousefi-Koma,et al.  A Reactive and Efficient Walking Pattern Generator for Robust Bipedal Locomotion , 2017, 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM).

[4]  Robert Wittmann,et al.  Fast object approximation for real-time 3D obstacle avoidance with biped robots , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[5]  Jun-Ho Oh,et al.  A robust walking controller optimizing step position and step time that exploit advantages of footed robot , 2019, Robotics Auton. Syst..

[6]  B. E. Maki,et al.  The role of limb movements in maintaining upright stance: the "change-in-support" strategy. , 1997, Physical therapy.

[7]  Jun-Ho Oh,et al.  Biped walking stabilization based on foot placement control using capture point feedback , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Sergey V. Drakunov,et al.  Capture Point: A Step toward Humanoid Push Recovery , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[9]  A. Hof.  The 'extrapolated center of mass' concept suggests a simple control of balance in walking. , 2008, Human movement science.

[10]  Alin Albu-Schäffer,et al.  Bipedal walking control based on Capture Point dynamics , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Daniel Maier,et al.  Integrated perception, mapping, and footstep planning for humanoid navigation among 3D obstacles , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Alin Albu-Schäffer,et al.  Three-Dimensional Bipedal Walking Control Based on Divergent Component of Motion , 2015, IEEE Transactions on Robotics.

[13]  KangKyu Lee,et al.  Design and control of the rapid legged platform GAZELLE , 2020 .

[14]  Jun-Ho Oh,et al.  A Robust Walking Controller Based on Online Optimization of Ankle, Hip, and Stepping Strategies , 2019, IEEE Transactions on Robotics.

[15]  F. Horak,et al.  Central programming of postural movements: adaptation to altered support-surface configurations. , 1986, Journal of neurophysiology.

[16]  Nikolaos G. Tsagarakis,et al.  Online regeneration of bipedal walking gait pattern optimizing footstep placement and timing , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[17]  Aghil Yousefi-Koma,et al.  Push recovery of a humanoid robot based on model predictive control and capture point , 2016, 2016 4th International Conference on Robotics and Mechatronics (ICROM).

[18]  Masayuki Inaba,et al.  Online decision of foot placement using singular LQ preview regulation , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[19]  Aghil Yousefi-Koma,et al.  Robust bipedal locomotion control based on model predictive control and divergent component of motion , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Kazuhito Yokoi,et al.  Biped walking pattern generation by using preview control of zero-moment point , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[21]  Olivier Stasse,et al.  Fast Humanoid Robot Collision-Free Footstep Planning Using Swept Volume Approximations , 2012, IEEE Transactions on Robotics.

[22]  R. Siegwart,et al.  ROBOT-CENTRIC ELEVATION MAPPING WITH UNCERTAINTY ESTIMATES , 2014 .

[23]  Olivier Stasse,et al.  Real-time footstep planning for humanoid robots among 3D obstacles using a hybrid bounding box , 2012, 2012 IEEE International Conference on Robotics and Automation.