Abstract
Research into legged robotics is primarily motivated by the prospects of building machines that are able to navigate in challenging and complex environments that are predominantly non-flat. In this context, control of contact forces is fundamental to ensure stable contacts and equilibrium of the robot. In this paper we propose a planning/control framework for quasi-static walking of quadrupedal robots, implemented for a demanding application in which regulation of ground reaction forces is crucial. Experimental results demonstrate that our 75-kg quadruped robot is able to walk inside two high-slope (\(50^\circ \)) V-shaped walls; an achievement that to the authors’ best knowledge has never been presented before. The robot distributes its weight among the stance legs so as to optimize user-defined criteria. We compute joint torques that result in no foot slippage, fulfillment of the unilateral constraints of the contact forces and minimization of the actuators effort. The presented study is an experimental validation of the effectiveness and robustness of QP-based force distributions methods for quasi-static locomotion on challenging terrain.










Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
In the following we keep using \(x_{com}\) even if in the implementation we actually used \(x_{com-base}\).
References
Armstrong, B. (1989). On finding exciting trajectories for identification experiments involving systems with nonlinear dynamics. The International Journal of Robotics Research, 8(6), 28–48. doi:10.1177/027836498900800603.
Bloesch, M., Hutter, M., Hoepflinger, M., Leutenegger, S., Gehring, C., Remy, C. D., & Siegwart, R. (2012). State estimation for legged robots-consistent fusion of leg kinematics and IMU. Robotics: Science and Systems. http://roboticsproceedings.org/rss08/p03.pdf.
Boaventura, T., Semini, C., Buchli, J., Frigerio, M., Focchi, M., & Caldwell, D. G., (2012). Dynamic torque control of a hydraulic quadruped robot. In 2012 IEEE international conference on robotics and automation (pp. 1889–1894). doi:10.1109/ICRA.2012.6224628, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6224628.
Bretl, T., & Lall, S. (2008). Testing static equilibrium for legged robots. IEEE Transactions on Robotics, 24(4), 794–807. doi:10.1109/TRO.2008.2001360.
Buchli, J., Kalakrishnan, M., Pastor, P., & Schaal, S., (2009). Compliant quadruped locomotion over rough terrain. In IEEE/RSJ international conference on intelligent robots and systems 2009 IROS 2009. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5354681.
Cheng, G., Hyon, S. H., Ude, A., Morimoto, J., Hale, J. G., Hart, J., Nakanishi, J., Bentivegna, D., Hodgins, J., Atkeson, C., Mistry, M., Schaal. S., et al. (2008). CB: Exploring neuroscience with a humanoid research platform. In 2008 IEEE international conference on robotics and automation (pp. 1772–1773). doi:10.1109/ROBOT.2008.4543459, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4543459.
de Lasa, M., Mordatch, I., Hertzmann, A., et al. (2010). Feature-based locomotion controllers. ACM Transactions on Graphics, 29(4), 1. doi:10.1145/1833351.1781157, http://portal.acm.org/citation.cfm?doid=1833351.1781157.
Feng, S., Xinjilefu, X., Atkeson, C. G., & Kim, J. (2015). Optimization based controller design and implementation for the Atlas robot in the DARPA robotics challenge finals. In 2015 IEEE-RAS international conference on humanoid robots.
Ferreau, H. J., Kirches, C., & Potschka, A. (2013). qpOASES: A parametric active-set algorithm for quadratic programming. Mathematical Programming Computation. doi:10.1007/s12532-014-0071-1.
Frigerio, M., Buchli, J., Caldwell, D. G., et al. (2012). Code generation of algebraic quantities for robot controllers. 2012 IEEE/RSJ international conference on intelligent robots and systems (pp. 2346–2351). doi:10.1109/IROS.2012.6385694, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6385694.
Gautier, M. (1991). Numerical calculation of the base inertial parameters of robots. Journal of Robotic Systems, 8(4), 1020–1025, doi:10.1109/ROBOT.1990.126126, http://onlinelibrary.wiley.com/doi/10.1002/rob.4620080405/abstract.
Gehring, C., Coros, S., Hutter, M., Bloesch, M., Hoepflinger, M., & Siegwart, R. (2013). Control of dynamic gaits for a quadrupedal robot. In IEEE international conference on robotics and automation (ICRA). https://infoscience.epfl.ch/record/189756.
Gertz, E., & Wright, S. (2001). OOQP user guide. Mathematics and Computer Science Division Technical Memorandum. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.4765&rep=rep1&type=pdf.
Guennebaud, G., Furfaro, A., & Gaspero, L. D. (2011). eiquadprog.hh. http://www.diegm.uniud.it/digaspero/index.php/software.
Hutter, M., Hoepflinger, M., & Gehring, C. (2012). Hybrid operational space control for compliant legged systems. In Proceedings of robotics: Science and systems, 2012. http://roboticsproceedings.org/rss08/p17.pdf.
Hyon, S., Hale, J., Cheng, G., et al. (2007). Full-body compliant humanhumanoid interaction: Balancing in the presence of unknown external forces. IEEE transactions on robotics, 23(5), 884–898. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4339533.
Johnson, M., Shrewsbury, B., Bertrand, S., Wu, T., Duran, D., Floyd, M., et al. (2015). Team IHMC’s lessons learned from the DARPA robotics challenge trials. International Journal of Field Robotics, 32, 192–208.
Jovic, J., Philipp, F., Escande, A., Ayusawa, K., Yoshida, E., Kheddar, A., Venture, G., et al. (2015). Identification of dynamics of humanoids : Systematic exciting motion generation. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2173–2179). doi:10.1109/IROS.2015.7353668.
Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K., & Hirukawa, H. (2003). Resolved momentum control : humanoid motion planning based on the linear and angular momentum National Institute of Advanced Industrial Science and Technology (AIST). In 2003 IEEE/RSJ international conference on intelligent robots and systems (IROS), October (pp. 1644–1650).
Kuindersma, S., Deits, R., Andr, M. F., Dai, H., Permenter, F., Pat, K., & Russ, M. (2016). Optimization-based locomotion planning, estimation, and control design for Atlas. Autonomous Robots, 40(3), 429–455.
Lee, S., & Goswami, A. (2010). Ground reaction force control at each foot: A momentum-based humanoid balance controller for non-level and non-stationary ground. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010 (pp. 3157–3162). doi:10.1109/IROS.2010.5650416, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5650416, http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5650416.
Lee, S. H., & Goswami, A. (2012). A momentum-based balance controller for humanoid robots on non-level and non-stationary ground. Autonomous Robots, 33(4), 399–414. doi:10.1007/s10514-012-9294-z.
Lin, P. C., Komsuoglu, H., Koditschek, D., et al. (2005) A leg configuration measurement system for full-body pose estimates in a hexapod robot. IEEE Transactions on Robotics, 21(3), 411–422. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1435485.
Macchietto, A., & Shelton, C. R. (2009). Momentum control for balance. In ACM Transactions on graphics (TOG).
Mistry, M., Schaal, S., Yamane, K., et al. (2009). Inertial parameter estimation of floating base humanoid systems using partial force sensing. In IEEE international conference on humanoids 2009. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5379531.
Nishiwaki, K., Kagami, S., Kuniyoshi, Y., Inaba, M., & Inoue, H. (2002). Online generation of humanoid walking motion based on a fast generation method of motion pattern that follows desired ZMP. IEEE/RSJ international conference on intelligent robots and systems (Vol. 3, pp. 2684–2689). doi:10.1109/IRDS.2002.1041675.
Orin, D. E., Goswami, A., Lee, S. H., et al. (2013). Centroidal dynamics of a humanoid robot. Autonomous Robots, 35(2–3), 161–176. doi:10.1007/s10514-013-9341-4.
Ott, C., Roa, M. A., Hirzinger, G., et al. (2011). Posture and balance control for biped robots based on contact force optimization. In 2011 11th IEEE-RAS international conference on humanoid robots (pp. 26–33). doi:10.1109/Humanoids.2011.6100882, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6100882.
Pratt, J., Chew, C., Torres, A., Dilworth, P., Pratt, G., et al. (2001). Virtual model control: An intuitive approach for bipedal locomotion. The International Journal of Robotics Research, 20, 129–143. doi:10.1177/02783640122067309, http://ijr.sagepub.com/content/20/2/129.short.
Righetti, L., Buchli, J., Mistry, M., Schaal, S., et al. (2011). Inverse dynamics control of floating-base robots with external constraints: A unified view. In 2011 IEEE international conference on robotics and automation (pp. 1085–1090). doi:10.1109/ICRA.2011.5980156, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5980156.
Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., Schaal, S., et al. (2013). Optimal distribution of contact forces with inverse dynamics control. The International Journal of Robotics Research. doi:10.1177/0278364912469821.
Schaal, S., (2001). The S L Simulation and Real-Time Control Software Package.
Schumacher, P. M., Zanderigo, E., & Morari, M. (2009). Short Papers, 6(2), 256–264. doi:10.1109/TCBB.2006.4,0504378.
Semini, C., Tsagarakis, N. G., Guglielmino, E., Focchi, M., Cannella, F., Caldwell, D. G., et al. (2011). Design of HyQ—A hydraulically and electrically actuated quadruped robot. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225(6), 831–849. doi:10.1177/0959651811402275.
Stephens, B. J., & Atkeson, C. G., (2010). Dynamic balance force control for compliant humanoid robots. 2010 IEEE/RSJ international conference on intelligent robots and systems (pp. 1248–1255). doi:10.1109/IROS.2010.5648837, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5648837.
Acknowledgments
This research has been funded by the Fondazione Istituto Italiano di Tecnologia.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
1.1 Intuitive justification of foot placement
This section explains our choices regarding foot positioning for quadrupedal walking on v-shaped terrain. We show that, when the robot stands on three feet, having an acute support triangle is convenient for maintaining the robot in equilibrium. We know that the robot is in equilibrium when the net external force and moment (about any point) acting on it are zero. We define a reference frame \(O_1\) located at foot 1 (see Fig. 10), with the axis \(z_1\) aligned with gravity and the axis \(x_1\) pointing towards foot 2 (which we assume to be approximately aligned with foot 1). At the equilibrium, the net moment \(m\in \mathbb {R}^3\) about \(z_1\) has to be zero, that is:
where \(P_{xy}\in \mathbb {R}^{3\times 3}\) projects onto the \(x_1y_1\) plane, \(P_z\in \mathbb {R}^{3\times 3}\) projects onto the \(z_1\) axis, \(f_2 (f_3)\in \mathbb {R}^3\) is the GRF at the foot 2 (3), and \(p_{12}, p_{13} \in \mathbb {R}^3\) are the lever arms from foot 1 to foot 2 and 3, respectively. The first term of (17) always generates a positive moment about \(z_1\) because of the unilaterality constraints, i.e., \(f_{2y}>0\). To have equilibrium then we need \(f_3\) (i.e., the second term) to generate a negative moment about \(z_1\). In other words \((P_{xy}f_3)\) must lie in the right halfspace delimited by the line passing through feet 1 and 3. Similarly, computing the net moment about \(z_2\) (i.e., the z axis of the frame \(O_2\)), we can infer that to have equilibrium \((P_{xy}f_3)\) must lie in the left halfspace delimited by the line passing through feet 2 and 3. This implies that \((P_{xy}f_3)\) must lie—not only inside the friction cone, but also—inside the support cone, that is the cone originating in \(O_3\) and delimitated by two sides of the support triangle (green cone in Fig. 10). We can then state that having an acute support triangle leaves more freedom in the choice of \(f_3\) because it results in a bigger area of intersection between the friction cone and the support cone. If \(p_3\) gets too close to \(p_1\) or \(p_2\), a part of the friction cone of \(f_3\) stops intersecting the support cone, leaving less freedom for the choice of \(f_3\) (e.g. red support triangle in Fig. 10).
Taking advantage of these insights we planned contact configurations that generate acute support triangles. A gait sequence that satisfies this requirement is RH, RF, LH, LF, in which we set an initial offset positions for the feet along the x direction (see Fig.10 (bottom)).
Rights and permissions
About this article
Cite this article
Focchi, M., del Prete, A., Havoutis, I. et al. High-slope terrain locomotion for torque-controlled quadruped robots. Auton Robot 41, 259–272 (2017). https://doi.org/10.1007/s10514-016-9573-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-016-9573-1