135 research outputs found
Running synthesis and control for monopods and bipeds with articulated
Bibliography: p. 179-20
Design and control of SLIDER: an ultra-lightweight, knee-less, low-cost bipedal walking robot
Most state-of-the-art bipedal robots are designed to be highly anthropomorphic and therefore possess legs with knees. Whilst this facilitates more human-like locomotion, there are implementation issues that make walking with straight or near-straight legs difficult. Most bipedal robots have to move with a constant bend in the legs to avoid singularities at the knee joints, and to keep the centre of mass at a constant height for control purposes. Furthermore, having a knee on the leg increases the design complexity as well as the weight of the leg, hindering the robot’s performance in agile behaviours such as running and jumping. We present SLIDER, an ultra-lightweight, low-cost bipedal walking robot with a novel knee-less leg design. This nonanthropomorphic straight-legged design reduces the weight of the legs significantly whilst keeping the same functionality as anthropomorphic legs. Simulation results show that SLIDER’s low-inertia legs contribute to less vertical motion in the center of mass (CoM) than anthropomorphic robots during walking, indicating that SLIDER’s model is closer to the widely used Inverted Pendulum (IP) model. Finally, stable walking on flat terrain is demonstrated both in simulation and in the physical world, and feedback control is implemented to address challenges with the physical robot
A Modular Framework to Generate Robust Biped Locomotion: From Planning to Control
Biped robots are inherently unstable because of their complex kinematics as
well as dynamics. Despite the many research efforts in developing biped
locomotion, the performance of biped locomotion is still far from the
expectations. This paper proposes a model-based framework to generate stable
biped locomotion. The core of this framework is an abstract dynamics model
which is composed of three masses to consider the dynamics of stance leg, torso
and swing leg for minimizing the tracking problems. According to this dynamics
model, we propose a modular walking reference trajectories planner which takes
into account obstacles to plan all the references. Moreover, this dynamics
model is used to formulate the controller as a Model Predictive Control (MPC)
scheme which can consider some constraints in the states of the system, inputs,
outputs and also mixed input-output. The performance and the robustness of the
proposed framework are validated by performing several numerical simulations
using MATLAB. Moreover, the framework is deployed on a simulated
torque-controlled humanoid to verify its performance and robustness. The
simulation results show that the proposed framework is capable of generating
biped locomotion robustly
Explainable robotics applied to bipedal walking gait development
Explainability is becoming an important topic in artificial intelligence (AI). A well explainable system can increase the trust in the application of that system. The same holds for robotics where the walking gait controller can be some AI system. We will show that a simple and explainable controller that enables an energy efficient walking gait and can handle uneven terrains, can be developed by a well structured design method. The main part of the controller consist of three simple neural networks with 4, 6 and 8 neurons. So, although creating a stable and energy efficient walking gait is a complex problem, it can be generated without some deep neural network or some complex mathematical model
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
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