Efficient Walking Gait Generation via Principal Component Representation of Optimal Trajectories: Application to a Planar Biped Robot With Elastic Joints

Abstract

Recently, the method of choice to exploit robot dynamics for efficient walking is numerical optimization (NO). The main drawback in NO is the computational complexity, which strongly affects the time demand of the solution. Several strategies can be used to make the optimization more treatable and to efficiently describe the solution set. In this work, we present an algorithm to encode effective walking references, generated off-line via numerical optimization, extracting a limited number of principal components and using them as a basis of optimal motions. By combining these components, a good approximation of the optimal gaits can be generated at run time. The advantages of the presented approach are discussed, and an extensive experimental validation is carried out on a planar legged robot with elastic joints. The biped thus controlled is able to start and stop walking on a treadmill, and to control its speed dynamically as the treadmill speed changes.

An Overview on Principles for Energy Efficient Robot Locomotion

“…A possible solution to address this problem can be to determine the robot trajectories based on whole-body dynamics using a trajectory optimization approach targeting minimum CoT. Gasparri et al ( 2018 ) formulated an optimal control problem in a way that robot dynamic parameters such as joint impedance may also be optimized, in addition to typical state and control variables. Once the locomotion constraints defining periodic change of contact phases (single and double supports) are set, in addition to the robot dynamics and conventional constraints e.g., joint limits, the optimal control problem is solved using a direct method.…”

Enhancing Robot-Environment Physical Interaction via Optimal Impedance Profiles

“…Particular effort will be devoted to improvement of the optimization routine in terms of computation time, to enable on-line implementation. The use of Machine Learning techniques is currently under evaluation to infer impedance and trajectory profiles for an unknown case, leveraging on a set of previously solved examples [32], [33].…”

Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

“…Numerical optimization methods have been used to adjust optimal gaits at runtime for a robot walking on a treadmill with a speed change [21]. As in most works, in this work, we neglected the friction between the robot's feet and floor.…”