Low Impact Force and Energy Consumption Motion Planning for Hexapod Robot with Passive Compliant Ankles
- 53 Downloads
Motion planning plays an important role in the performance optimization of legged robots. This paper presents a method to minimize the impact force and energy consumption effectively by providing an integrated strategy of motion planning subject to velocity and acceleration constraints. The parameters defined for the motion planning are computed to generate the foot trajectory. A foot–terrain interaction model and an energy-consumption model are formulated to evaluate the contact force and power consumption for statically stable gaits. The proposed method has been implemented on a hexapod robot. The acceleration of foot landing is reduced, and constant velocity control of the trunk body with passive compliant ankles is achieved for reducing the impact force and energy consumption. Extensive experiments have been carried out, and the experimental results have demonstrated the effectiveness of the proposed method in comparison with a conventional method.
KeywordsMotion planning Impact force Energy consumption Hexapod Passive compliant ankles
Unable to display preview. Download preview PDF.
This study was supported in part by the National Natural Science Foundation of China (Grant No. 51575120/61370033), National Basic Research Program of China (Grant No. 2013CB035502), Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51521003), Foundation of Chinese State Key Laboratory of Robotics and Systems (Grant No. SKLRS201501B, SKLRS20164B), Harbin Talent Program for Distinguished Young Scholars (No. 2014RFYXJ001), and the “111 Project” (Grant No. B07018).
- 2.Nagatani, K., Noyori, T., Yoshida, K.: Development of multi-D.O.F. tracked vehicle to traverse weak slope and climb up rough slope. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, pp. 2849–2854 (2013)Google Scholar
- 3.Wooden, D., Malchano, M., Blankespoor, K., Howardy, A., Rizzi, A.A., Raibert, M.: Autonomous navigation for BigDog. In: Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, pp. 4736–4741 (2010)Google Scholar
- 8.Irawan, A., Nonami H Ohroku, K., Akutsu, Y., Imamura, S.: Adaptive impedance control with compliant body balance for hydraulically driven hexapod robot. J. Syst. Des. Dyn. 5(5), 893–908 (2011)Google Scholar
- 9.Raibert, M., Blankespoor, K., Nelson, G., Playter, R.: BigDog, the rough-terrain quadruped robot. In: Proceedings of 17th World Congress on the International Federation of Automatic Control, Seoul. 10822–10825 (2008)Google Scholar
- 11.Townsend, J., Biesiadecki, J., Collins, C.: ATHLETE mobility performance with active terrain compliance. In: Proceedings of the IEEE Aerospace Conference, Big Sky, pp. 1–7 (2010)Google Scholar
- 12.Chavez-Clemente, D.: Gait optimization for multi-legged walking robots, with application to a lunar hexapod, pp. 1–150. Department of Aeronautics and Astronautics, Stanford University (2011)Google Scholar
- 15.Manjanna, S., Dudek, G.: Autonomous gait selection for energy efficient walking. In: 2015 IEEE International Conference on Robotics and Automation, pp. 5155–5162 (2015)Google Scholar
- 19.Kottege, N., Parkinson, C.: Energetics-informed hexapod gait transitions across terrains. In: 2015 IEEE International Conference on Robotics and Automation, pp. 5140–5147 (2015)Google Scholar
- 23.Ruina, A., Bertram, J.E., Srinivasan, M.: A collisional model of the energetic cost of support work qualitatively explains leg sequencing in walking and galloping pseudo-elastic leg behavior in running and the walk-to-run transition. J. Theor. Biol. 237(2), 170–192 (2005)MathSciNetCrossRefGoogle Scholar
- 29.Dupont, P.E.: Friction modeling in dynamic robot simulation. In: Proceedings. 1990 IEEE International Conference on Robotics and Automation, pp. 1370–1376 (1990)Google Scholar
- 30.Gogoussis, A., Donath, M.: Coulomb friction joint and drive effects in robot mechanisms. In: Proceedings. 1987 IEEE International Conference on Robotics and Automation, vol. 4, pp. 828–836 (1987)Google Scholar