Abstract
The computational complexity of humanoid robot balance control is reduced through the application of simplified kinematics and dynamics models. However, these simplifications lead to the introduction of errors that add to other inherent electro-mechanic inaccuracies and affect the robotic system. Linear control systems deal with these inaccuracies if they operate around a specific working point but are less precise if they do not. This work presents a model improvement based on the Linear Inverted Pendulum Model (LIPM) to be applied in a non-linear control system. The aim is to minimize the control error and reduce robot oscillations for multiple working points. The new model, named the Dynamic LIPM (DLIPM), is used to plan the robot behavior with respect to changes in the balance status denoted by the zero moment point (ZMP). Thanks to the use of information from force–torque sensors, an experimental procedure has been applied to characterize the inaccuracies and introduce them into the new model. The experiments consist of balance perturbations similar to those of push-recovery trials, in which step-shaped ZMP variations are produced. The results show that the responses of the robot with respect to balance perturbations are more precise and the mechanical oscillations are reduced without comprising robot dynamics.
Highlights
IntroductionThe most versatile but complex machines are humanoid robots. Their complex mechanical structure, high number of degrees of freedom (DOFs), and control requirements result in the need for simplifications that enable the deployment of multiple tasks
In robotics, the most versatile but complex machines are humanoid robots
Prior to walking pattern generation, robot joint constraints, dynamic parameters, and joint torques [5] have to be observed in real time so as not to overload the system and make the walking task viable
Summary
The most versatile but complex machines are humanoid robots. Their complex mechanical structure, high number of degrees of freedom (DOFs), and control requirements result in the need for simplifications that enable the deployment of multiple tasks. Human-like or humanoid robots are designed to work in scenarios in the same way that humans do, but at present they have very serious limitations when performing certain tasks. In this regard, for manufacturing plants in which heavy parts must be processed, disaster scenarios, service applications, etc. The humanoid robot is usually represented by means of simplified models that enable an easy way of designing controllers. These models represent the kinematics and dynamics of the robotic system in action.
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