Biped robots have gained much attention for decades. A variety of researches has been conducted to make them able to assist or even substitute for humans in performing special tasks. In addition, studying biped robots is important in order to understand the human locomotion and to develop and improve control strategies for prosthetic and orthotic limbs. Some challenges encountered in the design of biped robots are: (1) biped robots have unstable structures due to the passive joint located at the unilateral foot-ground contact. (2) They have different configuration when switching from walking phase to another. During the single support phase, the robot is under-actuated, while turning into an over-actuated system during the double-support phase. (3) Biped robots have many degrees of freedom, and (4) interact with different unknown environments. Therefore, this work is focused on offline computational optimal control strategies for zero-moment point-based biped robots. Computational optimal control has been performed to investigate the effects of some imposed constraints on biped locomotion, such as enforcing swing foot to move level to the ground, hip motion with constant height etc. finite difference approach has been used to transcribe infinite dimensional optimal control problem into finite dimensional suboptimal control problem. Then parameter optimization has been used to get suboptimal trajectory of the biped with the imposing different constraints. In general, any artificially imposed constraint to biped locomotion can lead to increase in value of input control torques. On the other hand, suboptimal trajectory of biped robot during complete gait cycle had been accomplished with different cases such that continuous dynamic response occurs. Enforcing the biped locomotion to move with linear transition of zero-moment point during the DSP can lead to more energy consumption.
Inhaltsverzeichnis (Table of Contents)
- 1 Computational Optimal Control: Theory and Simulation
- 1.1 Forward dynamics-based optimization
- 1.2 Inverse dynamics-based optimization
- 1.2.1 Discretization
- 1.2.2 Parameter optimization
- 1.2.3 Motivating example
- 1.3 The effect of miscellaneous imposed constraints on performance index of biped locomotion during the SSP
- 1.3.1 Dynamic modeling
- 1.3.2 Discretization and parameter optimization
- 1.4 Suboptimal trajectory planning of biped robot during complete gait cycle
- 1.4.1 Dynamic modeling
- 1.4.2 Discretization and parameter optimization
- 1.5 Simulation Results
- 1.5.1 SSP The effect of constraints on the performance index of biped locomotion during the
- 1.5.2 Suboptimal trajectory planning of biped locomotion during complete gait cycle
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This work investigates offline computational optimal control strategies for zero-moment point (ZMP)-based biped robots. The primary objective is to explore the impact of various constraints on biped locomotion using computational optimal control techniques. The study employs finite difference methods to transform the infinite-dimensional optimal control problem into a finite-dimensional suboptimal control problem, leveraging parameter optimization for trajectory generation.
- Impact of imposed constraints on biped locomotion
- Comparison of forward and inverse dynamics-based optimization methods
- Suboptimal trajectory planning for complete gait cycles
- Analysis of dynamic response and continuous actuating torques
- Energy consumption considerations in bipedal locomotion
Zusammenfassung der Kapitel (Chapter Summaries)
1 Computational Optimal Control: Theory and Simulation: This chapter provides a foundational overview of direct optimal control methods, focusing on the advantages of inverse dynamics-based optimization. It details the discretization process and parameter optimization techniques used to solve the optimal control problem. The chapter also introduces the concept of transforming an infinite-dimensional problem into a finite-dimensional one through discretization, highlighting the trade-off between computational ease and suboptimal solutions. Specific attention is given to the challenges posed by biped robot locomotion and the limitations of alternative approaches like dynamic programming and indirect methods based on Pontryagin's Maximum Principle. The chapter sets the stage for subsequent chapters by establishing the theoretical framework and computational methodology used throughout the study.
Schlüsselwörter (Keywords)
Biped robots, zero-moment point (ZMP), optimal control, inverse dynamics, parameter optimization, gait cycle, single support phase (SSP), double support phase (DSP), trajectory planning, constraints, energy consumption, computational methods, suboptimal control.
Frequently Asked Questions: Computational Optimal Control for Biped Robots
What is the main topic of this document?
This document provides a comprehensive overview of a study investigating offline computational optimal control strategies for zero-moment point (ZMP)-based biped robots. It focuses on the impact of various constraints on biped locomotion using computational optimal control techniques.
What are the key objectives of the research?
The primary objective is to explore the effects of different constraints on biped locomotion. The research also aims to compare forward and inverse dynamics-based optimization methods, plan suboptimal trajectories for complete gait cycles, analyze dynamic response and continuous actuating torques, and consider energy consumption in bipedal locomotion.
What methods are used in this research?
The research employs finite difference methods to transform the infinite-dimensional optimal control problem into a finite-dimensional suboptimal control problem. Parameter optimization is used for trajectory generation. The study contrasts forward and inverse dynamics-based optimization approaches.
What are the key themes explored in the study?
Key themes include the impact of imposed constraints on biped locomotion; a comparison of forward and inverse dynamics-based optimization; suboptimal trajectory planning for complete gait cycles; analysis of dynamic response and continuous actuating torques; and energy consumption considerations in bipedal locomotion.
What are the chapter summaries?
Chapter 1 ("Computational Optimal Control: Theory and Simulation") provides a foundational overview of direct optimal control methods, focusing on inverse dynamics-based optimization. It details discretization and parameter optimization techniques, highlighting the transformation of an infinite-dimensional problem into a finite-dimensional one and the challenges of biped robot locomotion. It establishes the theoretical framework and computational methodology for the entire study.
What are the key words associated with this research?
Key words include: Biped robots, zero-moment point (ZMP), optimal control, inverse dynamics, parameter optimization, gait cycle, single support phase (SSP), double support phase (DSP), trajectory planning, constraints, energy consumption, computational methods, suboptimal control.
What specific aspects of biped locomotion are analyzed?
The research analyzes the single support phase (SSP) and the double support phase (DSP) of biped locomotion, investigating how constraints affect the performance index during these phases. It also examines the suboptimal trajectory planning for a complete gait cycle, including the impact of constraints.
What are the advantages of using inverse dynamics-based optimization?
The document highlights the advantages of inverse dynamics-based optimization as a direct optimal control method, although it doesn't explicitly detail these advantages beyond mentioning it as a focus of the study.
What is the role of discretization in this research?
Discretization is a crucial step in the research, transforming the infinite-dimensional optimal control problem into a finite-dimensional, suboptimal control problem, making it computationally tractable.
What are the limitations of the approach used in this research?
The approach uses suboptimal solutions due to the discretization process. The document acknowledges the trade-off between computational ease and suboptimality, and implicitly mentions the limitations of alternative methods like dynamic programming and indirect methods based on Pontryagin's Maximum Principle, but doesn't explicitly detail the limitations of its chosen approach.
- Quote paper
- Dr. Hayder Al-Shuka (Author), 2018, Design of walking patterns for zero-momentum point (ZMP)-based biped robots. A computational optimal control approach, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/434367
