Open Access Open Access  Restricted Access Subscription Access

AI based Biped Robot

S. M. Jadhav, Dhondge Kaustubh V, Kadekar Gausmohammad I, Surve Ashlesha D, Patil Rupesh R.

Abstract


The primary focus in this paper is on bipedal robot development and controlling it for the different surface conditions by using an AI based approach. Pressure sensor, servomotor and software-driven microprocessors are the core components of the system. The two-path communication among robot and ground by methods for pressure detecting information for different ground surface conditions is fundamental. In our machine, we're imposing the robot which may be managed by means of AI. The movement of two-legged gadget is called as taking walks. Strolling can be statically or dynamically solid. Going for walks is usually dynamically solid. Airborne time for ASIMO is 0.08 sec. Keep foot flat at the floor (fully actuated). Estimate hazard of foot roll by means of measuring ground reaction forces. Cautiously design desired trajectories thru optimization. Keep knees bent (avoid singularity). The adaptive trajectory monitoring manages (high remarks profits). The maximum critical undertaking inside the development of biped robot is robotic mechanical structure. Stiffness and compliance consist with biped determine with flexibility of shape. The primary goal is to design a biped shape that may effortlessly control and capable of dealing with entire situation like humans. A normal man can weigh up to ten kg of load without difficulty and panting with accuracy. Even as designing we ought to remember such a lot of parameters inside the proposed machine, we're interfacing the flexi force sensor with Arduino mega. And measuring the foot stress of robotic after that send that fee to raspberry pi primarily based on ai robot will controlled mechanically.

Full Text:

PDF

References


Bhattacharya, S., Maiti, T. K., Dutta, S., Luo, A., Miura-Mattausch, M., &Mattausch, H. J. System Simulation for Robot Control Based on AI Approach. In 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), 1-4. IEEE.

Sinnet, R. W., Powell, M. J., Shah, R. P., & Ames, A. D. (2011). A human-inspired hybrid control approach to bipedal robotic walking. IFAC Proceedings Volumes, 44(1), 6904-6911.

Lee, H. W., Yang, J. L., Zhang, S. Q., & Chen, Q. (2018, July). Research on the Stability of Biped Robot Walking on Different Road Surfaces. In 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII) 54-57. IEEE.

Wilamowski, B. M., & Yu, H. (2010). Improved computation for Levenberg–Marquardt training. IEEE transactions on neural networks, 21(6), 930-937.

Allgeuer, P., & Behnke, S. (2018, November). Bipedal walking with corrective actions in the tilt phase space. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) . 1-9. IEEE.

http://ijsrst.com/archive.php?v=9&i=48&pyear=2020

https://dataaspirant.com/2017/01/13/support-vector-machine-algorithm/

https://www.tekscan.com/products-solutions/embedded-force-sensors

https://www.researchgate.net/profile/HJ_Mattausch

https://en.wikipedia.org/wiki/Humanoid_robot


Refbacks

  • There are currently no refbacks.