An Energy Efficient Dynamic Gait for a Nao Robot (original) (raw)
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An energy efficient gait for a Nao robot
Benelux Artificial Intelligence Conference (BNAIC), 2013
The gait of humans is often assumed to be the most energy efficient way of walking. Srinivasan and Ruina [18] confirm this hypothesis using a simple model in which the human is a point mass with straight legs that can change in length during a step. Their results show that the inverted pendulum walk is the most energy efficient gait. The question is whether this result also holds for humanoid robots. This paper investigate what is the most energy efficient gait for a humanoid robot such as the Nao, and what the corresponding control policy is that needs to be implemented. To answer these questions, first, the model of Srinivasan and Ruina is adapted for humanoid robots, and is used to study the energy consumption of different gaits. The model assumes a gait with dynamic stability and assumes that the torque on the knee joint provides the main contribution to the energy consumption of a gait. The former assumption implies that no energy is needed to remain stable. The latter assumption is confirmed by an experiment with a humanoid robot, namely Nao. Based on experiments with this idealize model, a gait that minimizes the energy consumption is identified. A controller for the new gait is implemented and is evaluated on a Nao robot. In the future, this controller will be the basis of an intelligent controller that can adapt to varying circumstances.
A Low Power Walk for the NAO Robot
Generally online walk pattern generators for humanoids are simplified, and don't produce ideal gaits. Allowing the robot to 'settle' into a more natural gait through the modification of the low-level positional controller would provide significant benefits. In this paper we attempt to achieve this, by limiting the power available to each motor in a humanoid, hence restricting how rigidly the joint can follow the generated walk pattern. This approach was evaluated by implementing the control modification on a humanoid robotics platform. The results show a significant improvement in walk speed, efficiency, and robustness. Moreover, the approach used here could be easily applied to any walk pattern generator, as the modification is in the low-level positional control.
Biomimetic Energy-Based Humanoid Gait Design
Journal of Intelligent & Robotic Systems, 2020
One of the challenges facing humanoid robots is the design of a more human-like gait. In this paper, we propose a new paradigm for gait design for humanoids that is founded in the field of Kinesiology and is based on energy-exchange between potential and kinetic energies. Additionally, we propose an energy-based controller, which not only maintains the desired gait but is also more efficient than current controllers in terms of energy expenditure and joint motor torque exertion. Experiments were performed in simulation on Webots and on an actual humanoid platform, the Nao. Results indicate an improvement in mechanical energy consumption by 10% in simulations, and 1.8% on the Nao. Qualitatively, the proposed gait yielded motions that are more human-like.
Learning to Walk Fast: Optimized Hip Height Movement for Simulated and Real Humanoid Robots
Journal of Intelligent and Robotic Systems, 2015
The linear inverted pendulum model has been used predominantly to generate balanced humanoid walking. This model assumes that the hip height is fixed during the walk. In this paper, generating a fast walk is studied with the main focus on the effect of hip height movement. Our approach is based on modeling the hip height movement and learning its parameters in order to generate a fast walk. The hip height trajectory is generated using Fourier basis functions. The generated trajectory is the input to programmable Central Pattern Generators (CPGs) in order to modulate generated trajectories smoothly. The inverted pendulum model is utilized to model a balanced walking. A numerical approach is presented to control inverted pendulum dynamics.
Dynamic Lateral Stability for an Energy Efficient Gait
Benelux Artificial Intelligence Conference (BNAIC), 2014
This paper presents an energy efficient dynamically stable gait for a Nao humanoid robot. In previous work we identified a dynamically stable and energy efficient gait in the sagittal or walking direction of a Nao robot. This gait proved to be more energy efficient than the standard gait, provided by the manufacturer. Dynamic stability in the lateral direction was not addressed. Lateral stability was handled by full stiffness of the joint in lateral direction. In this paper we report on adding dynamic lateral stability. We do not yet incorporate feedback of sensors. This implies that the gait is only suited for flat horizontal surfaces that some lateral joint stiffness is needed in the implementation on the Nao.
Archive of Mechanical Engineering, 2016
This paper proposes an analysis of the effect of vertical position of the pivot point of the inverted pendulum during humanoid walking. We introduce a new feature of the inverted pendulum by taking a pivot point under the ground level allowing a natural trajectory for the center of pressure (CoP), like in human walking. The influence of the vertical position of the pivot point on energy consumption is analyzed here. The evaluation of a 3D Walking gait is based on the energy consumption. A sthenic criterion is used to depict this evaluation. A consequent reduction of joint torques is shown with a pivot point under the ground.
Generalized Learning to Create an Energy Efficient ZMP-Based Walking
Lecture Notes in Computer Science, 2015
In biped locomotion, the energy minimization problem is a challenging topic. This problem cannot be solved analytically since modeling the whole robot dynamics is intractable. Using the inverted pendulum model, researchers have defined the Zero Moment Point (ZMP) target trajectory and derived the corresponding Center of Mass (CoM) motion trajectory, which enables a robot to walk stably. A changing vertical CoM position has proved to be crucial factor in reducing mechanical energy costs and generating an energy efficient walk [1]. The use of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) on a Fourier basis representation, which models the vertical CoM trajectory, is investigated in this paper to achieve energy efficient walk with specific step length and period. The results show that different step lengths and step periods lead to different learned energy efficient vertical CoM trajectories. For the first time, a generalization approach is used to generalize the learned results, by using a programmable Central Pattern Generator (CPG) on the learned results. Online modulation of the trajectory is performed while the robot changes its walking speed using the CPG dynamics. This approach is implemented and evaluated on the simulated and real NAO robot.
Bipedal walking energy minimization by reinforcement learning with evolving policy parameterization
2011
Abstract We present a learning-based approach for minimizing the electric energy consumption during walking of a passively-compliant bipedal robot. The energy consumption is reduced by learning a varying-height center-of-mass trajectory which uses efficiently the robot's passive compliance. To do this, we propose a reinforcement learning method which evolves the policy parameterization dynamically during the learning process and thus manages to find better policies faster than by using fixed parameterization.
Robust biped locomotion using deep reinforcement learning on top of an analytical control approach
Robotics and Autonomous Systems, 2021
This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. This framework is composed of six main modules which are hierarchically connected to reduce the overall complexity and increase its flexibility. The core of this framework is a specific dynamics model which abstracts a humanoid's dynamics model into two masses for modeling upper and lower body. This dynamics model is used to design an adaptive reference trajectories planner and an optimal controller which are fully parametric. Furthermore, a learning framework is developed based on Genetic Algorithm (GA) and Proximal Policy Optimization (PPO) to find the optimum parameters and to learn how to improve the stability of the robot by moving the arms and changing its center of mass (COM) height. A set of simulations are performed to validate the performance of the framework using the official RoboCup 3D League simulation environment. The results validate the performance of the framework, not only in creating a fast and stable gait but also in learning to improve the upper body efficiency.