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

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 letter, we present an algorithm to encode effective walking references, generated offline 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 change

Implementation and Integration of Fuzzy Algorithms for Descending Stair of KMEI Humanoid Robot

Locomotion of humanoid robot depends on the mechanical characteristic of the robot. Walking on descending stairs with integrated control systems for the humanoid robot is proposed. The analysis of trajectory for descending stairs is calculated by the constrains of step length stair using fuzzy algorithm. The established humanoid robot on dynamically balance on this matter of zero moment point has been pretended to be consisting of single support phase and double support phase. Walking transition from single support phase to double support phase is needed for a smooth transition cycle. To accomplish the problem, integrated motion and controller are divided into two conditions: motion working on offline planning and controller working online walking gait generation. To solve the defect during locomotion of the humanoid robot, it is directly controlled by the fuzzy logic controller. This paper verified the simulation and the experiment for descending stair of KMEI humanoid robot.&nbsp

Fast walking with rhythmic sway of torso in a 2D passive ankle walker

There is a category of biped robots that are equipped with passive or un-actuated ankles, which we call Passive-Ankle Walkers (PAWs). Lack of actuation at ankles is a disadvantage in the fast walking of PAWs. We started this study with an intuitive hypothesis that rhythmic sway of torso may enable faster walking in PAWs. To test this hypothesis, firstly, we optimized the rhythmic sway of torso of a simulated PAW model for fast walking speed, and analyzed the robustness of the optimal trajectories. Then we implemented the optimal trajectories on a real robot. Both the simulation analysis and the experimental results indicated that optimized torso-swaying can greatly increase the walking speed by 40%. By analyzing the walking patterns of the simulated model and the real robot, we identified the reason for the faster walking with swaying-torso: The rhythmic sway of torso enables the robot to walk with a relatively large step-length while still keeninu a hizh sten-frenuencv

A contact-implicit direct trajectory optimization scheme for the study of legged maneuverability

For legged robots to move safely in unpredictable environments, they need to be manoeuvrable, but transient motions such as acceleration, deceleration and turning have been the subject of little research compared to constant-speed gait. They are difficult to study for two reasons: firstly, the way they are executed is highly sensitive to factors such as morphology and traction, and secondly, they can potentially be dangerous, especially when executed rapidly, or from high speeds. These challenges make it an ideal topic for study by simulation, as this allows all variables to be precisely controlled, and puts no human, animal or robotic subjects at risk. Trajectory optimization is a promising method for simulating these manoeuvres, because it allows complete motion trajectories to be generated when neither the input actuation nor the output motion is known. Furthermore, it produces solutions that optimize a given objective, such as minimizing the distance required to stop, or the effort exerted by the actuators throughout the motion. It has consequently become a popular technique for high-level motion planning in robotics, and for studying locomotion in biomechanics. In this dissertation, we present a novel approach to studying motion with trajectory optimization, by viewing it more as “trajectory generation” – a means of generating large quantities of synthetic data that can illuminate the differences between successful and unsuccessful motion strategies when studied in aggregate. One distinctive feature of this approach is the focus on whole-body models, which capture the specific morphology of the subject, rather than the highly-simplified “template” models that are typically used. Another is the use of “contact-implicit” methods, which allow an appropriate footfall sequence to be discovered, rather than requiring that it be defined upfront. Although contact-implicit methods are not novel, they are not widely-used, as they are computationally demanding, and unnecessary when studying comparatively-predictable constant speed locomotion. The second section of this dissertation describes innovations in the formulation of these trajectory optimization problems as nonlinear programming problems (NLPs). This “direct” approach allows these problems to be solved by general-purpose, open-source algorithms, making it accessible to scientists without the specialized applied mathematics knowledge required to solve NLPs. The design of the NLP has a significant impact on the accuracy of the result, the quality of the solution (with respect to the final value of the objective function), and the time required to solve the proble

Push recovery with stepping strategy based on time-projection control

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed of three pendulums to simulate swing and torso dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use a particular time-projection method to adjust the next footstep location on-line during the motion continuously. This adjustment, which is found based on both pelvis and swing foot tracking errors, naturally takes the swing dynamics into account. Suggested adjustments are added to the Cartesian 3LP gaits and converted to joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies also ensure enough ground clearance in perturbed walking conditions. The proposed structure is robust, yet uses very simple state estimation and basic position tracking. We rely on the physical series elastic actuators to absorb impacts while introducing simple laws to compensate their tracking bias. Extensive experiments demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness due to relatively soft springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our proposed architecture.Comment: 20 pages journal pape