Title: Inverse dynamics learned gait planner for a two-legged robot moving on uneven terrains using neural networks
Authors: Pandu Ranga Vundavilli, Dilip Kumar Pratihar
Addresses: Soft Computing Laboratory, Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur-721 302, India. ' Soft Computing Laboratory, Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur-721 302, India
Abstract: This paper proposes a method for design of inverse dynamics learned, neural network-based gait planner for a two-legged robot negotiating uneven terrains. The lower limbs| gaits are generated utilising inverse kinematics, and those of the trunk and swing foot are derived using a neural network aiming to maximise the dynamic balance margin. A genetic algorithm is used to provide training off-line to the gait planner. Its performance has been tested through computer simulations on different terrains, namely staircase, sloping surface and ditch. Simulation results show that the developed planner has successfully generated appropriate gaits to negotiate the uneven terrains.
Keywords: two-legged robots; uneven terrains; inverse dynamics; genetic algorithms; neural networks; gait learning; gait planning; robot planning; walking robots; legged robots; inverse kinematics; robot kinematics; robot simulation.
International Journal of Advanced Intelligence Paradigms, 2008 Vol.1 No.1, pp.80 - 109
Published online: 17 Oct 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article