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Trajectory generation and step planning of a 12 DoF biped robot on uneven surface

Published online by Cambridge University Press:  26 February 2018

Gaurav Gupta  and
Ashish Dutta
Gaurav Gupta*
Affiliation:
Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India. E-mail: adutta@iitk.ac.in
Ashish Dutta
Affiliation:
Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India. E-mail: adutta@iitk.ac.in
*
*Corresponding author. E-mail: ggupta9777@gmail.com
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Summary

One of the primary goals of biped locomotion is to generate and execute joint trajectories on a corresponding step plan that takes the robot from a start point to a goal while avoiding obstacles and consuming as little energy as possible. Past researchers have studied trajectory generation and step planning independently, mainly because optimal generation of robot gait using dynamic formulation cannot be done in real time. Also, most step-planning studies are for flat terrain guided by search heuristics. In the proposed method, a framework for generating trajectories as well as an overall step plan for navigation of a 12 degrees of freedom biped on an uneven terrain with obstacles is presented. In order to accomplish this, a dynamic model of the robot is developed and a trajectory generation program is integrated with it using gait variables. The variables are determined using a genetic algorithm based optimization program with the objective of minimizing energy consumption subject to balance and kinematic constraints of the biped. A database of these variables for various terrain angles and walking motions is used to train two neural networks, one for real-time trajectory generation and another for energy estimation. To develop a global navigation strategy, a weighted A* search is used to generate the footstep plan with energy considerations in sight. The efficacy of the approach is exhibited through simulation-based results on a variety of terrains.

Keywords

Biped robotGenetic algorithmNeural networkTrajectory generationStep planning
Type
Articles
Information
Robotica , Volume 36 , Issue 7 , July 2018 , pp. 945 - 970
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Vukobratović, M. and Borovac, B., “Zero-moment point – Thirty five years of its life,” Int. J. Humanoid Robot. 1 (01), 157–173 (2004).Google Scholar
2. Sardain, P. and Bessonnet, G., “Forces acting on a biped robot. Center of pressure-zero moment point,” IEEE Trans. Syst. Man Cybernet. Part A: Syst. Hum. 34 (5), 630–637 (2004).Google Scholar
3. Huang, Q., Yokoi, K., Kajita, S., Kaneko, K., Arai, H., Koyachi, N. and Tanie, K., “Planning walking patterns for a biped robot,” IEEE Trans. Robot. Autom. 17 (3), 280–289, (2001).Google Scholar
4. Kajita, S., Kanehiro, F., Kaneko, K., Yokoi, K. and Hirukawa, H., “The 3D Linear Inverted Pendulum Mode: A Simple Modeling for A Biped Walking Pattern Generation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1 (2001) pp. 239–246.Google Scholar
5. Park, I.-W., Kim, J.-Y., Lee, J. and Oh, J.-H., “Online Free Walking Trajectory Generation for Biped Humanoid Robot KHR-3 (HUBO),” Proceedings of the IEEE International Conference on Robotics and Automation ICRA2006 (2006) pp. 1231–1236.Google Scholar
6. Vermeulen, J., Verrelst, B., Vanderborght, B., Lefeber, D. and Guillaume, P., “Trajectory planning for the walking biped Lucy,” Int. J. Robot. Res. 25 (9), 867–887 (2006).Google Scholar
7. Gao, W., Jia, Z. and Fu, C., “Increase the feasible step region of biped robots through active vertical flexion and extension motions,” Robotica 35 (7), 1541–1561 (2017).Google Scholar
8. Takanishi, A., Takeya, T., Karaki, H. and Kato, I., “A Control Method for Dynamic Biped Walking Under Unknown External Force,” Proceedings of the IEEE International Workshop on Intelligent Robots and Systems IROS'90, Towards a New Frontier of Applications (1990) pp. 795–801.Google Scholar
9. Vundavilli, P. R. and Pratihar, D. K., “Soft computing-based gait planners for a dynamically balanced biped robot negotiating sloping surfaces,” Appl. Soft Comput. 9 (1), 191–208 (2009).Google Scholar
10. Manchester, I. R., Mettin, U., Iida, F. and Tedrake, R., “Stable dynamic walking over uneven terrain,” Int. J. Robot. Res. 30 (3), 265–279 (2011).Google Scholar
11. Bi, S., Zhuang, Z.-J., Xia, T., Mo, H.-X., Min, H.-Q. and Luo, R.-H., “Multi-objective Optimization for a Humanoid Robot Walking on Slopes,” Proceedings of the International Conference on Machine Learning and Cybernetics ICMLC2011, vol. 3 (2011) pp. 1261–1267.Google Scholar
12. Huang, W., Chew, C.-M., Zheng, Y. and Hong, G.-S., “Pattern Generation for Bipedal Walking on Slopes and Stairs,” Proceedings of the 8th IEEE-RAS International Conference on Humanoid Robots Humanoids2008 (2008) pp. 205–210.Google Scholar
13. Zheng, Y., Lin, M. C., Manocha, D., Adiwahono, A. H. and Chew, C.-M., “A Walking Pattern Generator for Biped Robots on Uneven Terrains,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems IROS2010 (2010) pp. 4483–4488.Google Scholar
14. Arakawa, T. and Fukuda, T., “Natural Motion Generation of Biped Locomotion Robot Using Hierarchical Trajectory Generation Method Consisting of GA, EP Layers,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1 (1997) pp. 211–216.Google Scholar
15. Sarkar, A. and Dutta, A., “8-DoF biped robot with compliant-links,” Robot. Autonom. Syst. 63, 57–67 (2015).Google Scholar
16. Lim, I.-s., Kwon, O. and Park, J. H., “Gait optimization of biped robots based on human motion analysis,” Robot. Autonom. Syst. 62 (2), 229–240 (2014).Google Scholar
17. Cardenas-Maciel, S. L., Castillo, O. and Aguilar, L. T., “Generation of walking periodic motions for a biped robot via genetic algorithms,” Appl. Soft Comput. 11 (8), 5306–5314 (2011).CrossRefGoogle Scholar
18. Gong, D., Yan, J. and Zuo, G., “A review of gait optimization based on evolutionary computation,” Appl. Computat. Intell. Soft Comput., vol. 2010 (2010) p. 12.Google Scholar
19. Capi, G., Nasu, Y., Barolli, L., Yamano, M., Mitobe, K. and Takeda, K., “A Neural Network Implementation of Biped Robot Optimal Gait During Walking Generated by Genetic Algorithm,” Proceedings of the Mediterranean Conference on Control and Automation, Dubrovnik, Croatia (2001).Google Scholar
20. Nagasue, J., Konishi, Y., Araki, N., Sato, T. and Ishigaki, H., “Slope-walking of a Biped Robot with k Nearest Neighbor Method,” Proceedings of the 4th International Conference on Innovative Computing, Information and Control ICICIC2009 (2009) pp. 173–176.Google Scholar
21. Ferreira, J. P., Crisostomo, M. M. and Coimbra, A. P., “SVR versus neural-fuzzy network controllers for the sagittal balance of a biped robot,” IEEE Trans. Neural Netw. 20 (12), 1885–1897 (2009).CrossRefGoogle ScholarPubMed
22. Vundavilli, P. R. and Pratihar, D. K., “Dynamically balanced optimal gaits of a ditch-crossing biped robot,” Robot. Autonom. Syst. 58 (4), 349–361 (2010).Google Scholar
23. Liu, Z., Wang, L., Zhang, Y. and Chen, C. P., “A SVM controller for the stable walking of biped robots based on small sample sizes,” Appl. Soft Comput. 38, 738–753 (2016).Google Scholar
24. Chestnutt, J., Kuffner, J., Nishiwaki, K. and Kagami, S., “Planning Biped Navigation Strategies in Complex Environments,” Proceedings of the IEEE International Conference on Humanoid Robots, Munich, Germany (2003).Google Scholar
25. Chestnutt, J. and Kuffner, J. J., “A Tiered Planning Strategy for Biped Navigation,” Proceedings of the 4th IEEE/RAS International Conference on Humanoid Robots, vol. 1 (2004) pp. 422–436.Google Scholar
26. Huang, W., Kim, J. and Atkeson, C. G., “Energy-based Optimal Step Planning for Humanoids,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA2013 (2013) pp. 3124–3129.Google Scholar
27. Hornung, A., Maier, D. and Bennewitz, M., “Search-based Footstep Planning,” Proceedings of the ICRA Workshop on Progress and Open Problems in Motion Planning and Navigation for Humanoids (Karlsruhe, Germany, 2013).Google Scholar
28. Li, T.-Y., Chen, P.-F. and Huang, P.-Z., “Motion Planning for Humanoid Walking in a Layered Environment,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA2003, vol. 3 (2003) pp. 3421–3427.Google Scholar
29. Cupec, R., Aleksi, I. and Schmidt, G., “Step sequence planning for a biped robot by means of a cylindrical shape model and a high-resolution 2.5 D map,” Robot. Autonom. Syst. 59 (2), 84–100 (2011).Google Scholar
30. Gutmann, J.-S., Fukuchi, M. and Fujita, M., “Real-time Path Planning for Humanoid Robot Navigation,” Proceedings of the International Joint Conference on Artificial Intelligence, vol. 19 (Lawrence Erlbaum Associates Ltd., 2005) p. 1232.Google Scholar
31. Candido, S., Kim, Y.-T. and Hutchinson, S., “An Improved Hierarchical Motion Planner for Humanoid Robots,” Proceedings of the 8th IEEE-RAS International Conference on Humanoid Robots Humanoids2008 (2008) pp. 654–661.Google Scholar
32. Prasanth, H. and Sudheer, A., “A hybrid approach to motion planning for stepping over and obstacle avoidance in humanoids,” Proceedings of the CAD/CAM, Robotics and Factories of the Future (Springer, 2016) pp. 651–664.Google Scholar
33. Ryu, S.-H., Kang, Y., Kim, S.-J., Lee, K., You, B.-J. and Doh, N. L., “Humanoid path planning from hri perspective: A scalable approach via waypoints with a time index,” IEEE Trans. Cybernet. 43 (1), 217–229 (2013).Google Scholar
34. Perrin, N., Stasse, O., Baudouin, L., Lamiraux, F. and Yoshida, E., “Fast humanoid robot collision-free footstep planning using swept volume approximations,” IEEE Trans. Cybernet. 28 (2), 427–439 (2012).Google Scholar
35. Craig, J. J., Introduction to Robotics: Mechanics and Control, vol. 3 (Pearson/Prentice Hall, Upper Saddle River, 2005).Google Scholar
36. Fu, K. S., Gonzalez, R. and Lee, C. G., Robotics: Control, Sensing, Vision, and Intelligence (Tata McGraw-Hill Education, Singapore, 1988).Google Scholar
37. Dasgupta, A. and Nakamura, Y., “Making Feasible Walking Motion of Humanoid Robots from Human Motion Capture Data,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2 (1999) pp. 1044–1049.Google Scholar
38. Dai, H., Valenzuela, A. and Tedrake, R., “Whole-body Motion Planning with Centroidal Dynamics and Full Kinematics,” Proceedings of the 14th IEEE-RAS International Conference on Humanoid Robots Humanoids2014 (2014) pp. 295–302.Google Scholar
39. Silva, F. M. and Machado, J. T., “Energy Analysis During Biped Walking,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1 (1999) pp. 59–64.Google Scholar
40. Koch, K. H., Mombaur, K. and Soueres, P., “Optimization-based walking generation for humanoid robot,” IFAC Proc. Vol. 45 (22), 498–504 (2012).Google Scholar
41. Srinivasan, M. and Ruina, A., “Computer optimization of a minimal biped model discovers walking and running,” Nature 439 (7072), 72–75 (2006).Google Scholar
42. Chevallereau, C. and Aoustin, Y., “Optimal reference trajectories for walking and running of a biped robot,” Robotica 19 (5), 557–569 (2001).Google Scholar
43. Capi, G., Nasu, Y., Barolli, L., Mitobe, K. and Takeda, K., “Application of genetic algorithms for biped robot gait synthesis optimization during walking and going up-stairs,” Adv. Robot. 15 (6), 675–694 (2001).Google Scholar
44. Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybernet. 4 (2), 100–107 (1968).Google Scholar
45. Pohl, I., “The Avoidance of (Relative) Catastrophe, Heuristic Competence, Genuine Dynamic Weighting and Computational Issues in Heuristic Problem Solving,” Proceedings of the 3rd International Joint Conference on Artificial intelligence (Morgan Kaufmann Publishers Inc., 1973) pp. 12–17.Google Scholar