1,934 research outputs found
Design, analysis and passive balance control of a 7-DOF biped robot
Biped robots have many advantages than traditional wheeled or tracked robots. They have better mobility in rough terrain and can travel on discontinuous path. The legs can also provide an active suspension that decouples the path of the trunk from the paths of the feet. Furthermore, the legs are able to step over considerably bigger obstacles compared to wheeled robots. However, it is difficult to maintain the balance of biped robots because they can easily tip over or slide down. To be able to walk stably, it is necessary for the robot to walk through a proper trajectory, which is the goal of this research. In this research, a complete 7-DOF biped walking trajectory is planned based on human walking trajectory by cubic Hermite interpolation method. The kinematics and dynamic model of the biped are derived by Denavit-Hartenberg (D-H) representation and Euler-Lagrange motion equations, respectively. The zero moment point of the robot is simulated to check the stability of the walking trajectory. The setpoint sampling method and sampling rate for trajectory tracking control are investigated by studying sinusoidal curve tracking on a single link robot arm. Two control
sampling time selection methods are introduced for digital controllers.
A 7-DOF biped is designed and built for experiments. Each joint has its own
independent microcontroller-based control system. PD controllers are used to control the
biped joints.
Simulations are performed for the walking trajectory and zero moment point.
Simulation results show that the walking trajectory is stable for the 7-DOF biped.
Experiment results indicate that the sampling time is proper and the PID controller works
well in both setpoint control and trajectory tracking. The experiment for the marching in
place shows the trajectory is stable and the biped can balance during the marching process
Natural ZMP trajectories for biped robot reference generation
The control of a biped humanoid is a challenging
task due to the hard-to-stabilize dynamics. Walking reference
trajectory generation is a key problem. Linear Inverted
Pendulum Model (LIPM) and Zero Moment Point (ZMP)
Criterion based approaches in stable walking reference
generation are reported. In these methods, generally, the ZMP
reference during a stepping motion is kept fixed in the middle of
the supporting foot sole. This kind of reference generation lacks
naturalness, in that, the ZMP in the human walk does not stay
fixed, but it moves forward under the supporting foot. This paper
proposes a reference generation algorithm based on the LIPM
and moving support foot ZMP references. The application of
Fourier series approximation simplifies the solution and it
generates a smooth ZMP reference. A simple inverse kinematics
based joint space controller is used for the tests of the developed
reference trajectory through full-dynamics 3D simulation. A 12
DOF biped robot model is used in the simulations. Simulation
studies suggest that the moving ZMP references are more energy
efficient than the ones with fixed ZMP under the supporting foot.
The results are promising for implementations
Adaptive, fast walking in a biped robot under neuronal control and learning
Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks
Asymptotically Stable Walking of a Five-Link Underactuated 3D Bipedal Robot
This paper presents three feedback controllers that achieve an asymptotically
stable, periodic, and fast walking gait for a 3D (spatial) bipedal robot
consisting of a torso, two legs, and passive (unactuated) point feet. The
contact between the robot and the walking surface is assumed to inhibit yaw
rotation. The studied robot has 8 DOF in the single support phase and 6
actuators. The interest of studying robots with point feet is that the robot's
natural dynamics must be explicitly taken into account to achieve balance while
walking. We use an extension of the method of virtual constraints and hybrid
zero dynamics, in order to simultaneously compute a periodic orbit and an
autonomous feedback controller that realizes the orbit. This method allows the
computations to be carried out on a 2-DOF subsystem of the 8-DOF robot model.
The stability of the walking gait under closed-loop control is evaluated with
the linearization of the restricted Poincar\'e map of the hybrid zero dynamics.
Three strategies are explored. The first strategy consists of imposing a
stability condition during the search of a periodic gait by optimization. The
second strategy uses an event-based controller. In the third approach, the
effect of output selection is discussed and a pertinent choice of outputs is
proposed, leading to stabilization without the use of a supplemental
event-based controller
Restricted Discrete Invariance and Self-Synchronization For Stable Walking of Bipedal Robots
Models of bipedal locomotion are hybrid, with a continuous component often
generated by a Lagrangian plus actuators, and a discrete component where leg
transfer takes place. The discrete component typically consists of a locally
embedded co-dimension one submanifold in the continuous state space of the
robot, called the switching surface, and a reset map that provides a new
initial condition when a solution of the continuous component intersects the
switching surface. The aim of this paper is to identify a low-dimensional
submanifold of the switching surface, which, when it can be rendered invariant
by the closed-loop dynamics, leads to asymptotically stable periodic gaits. The
paper begins this process by studying the well-known 3D Linear Inverted
Pendulum (LIP) model, where analytical results are much easier to obtain. A key
contribution here is the notion of \textit{self-synchronization}, which refers
to the periods of the pendular motions in the sagittal and frontal planes
tending to a common period. The notion of invariance resulting from the study
of the 3D LIP model is then extended to a 9-DOF 3D biped. A numerical study is
performed to illustrate that asymptotically stable walking may be obtained.Comment: Conferenc
Biped robot walking control on inclined planes with fuzzy parameter adaptation
The bipedal structure is suitable for a robot functioning in the human environment, and assuming assistive roles. However, the bipedal walk is a poses a difficult control problem. Walking on even floor is not satisfactory for the applicability of a humanoid robot. This paper presents a study on bipedal walk on inclined planes. A Zero Moment Point (ZMP) based reference generation technique is employed. The orientation of the upper body is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. This system uses a sampling time larger than the one of the joint space position controllers. A newly defined measure of the oscillatory behavior of the body pitch angle and the average value of the pelvis pitch angle are used as inputs to the fuzzy adaptation system. A 12-degrees-of-freedom (DOF) biped robot model is used in the full-dynamics 3-D simulations. Simulations are carried out on even floor and inclined planes with different slopes. The results indicate that the fuzzy adaptation algorithms presented are successful in enabling the robot to climb slopes of 5.6 degrees (10 percent)
A geometric approach to three-dimensional hipped bipedal robotic walking
This paper presents a control law that results in stable walking for a three-dimensional bipedal robot with a hip. To obtain this control law, we utilize techniques from geometric reduction, and specifically a variant of Routhian reduction termed functional Routhian reduction, to effectively decouple the dynamics of the three-dimensional biped into its sagittal and lateral components. Motivated by the decoupling afforded by functional Routhian reduction, the control law we present is obtained by combining three separate control laws: the first shapes the potential energy of the sagittal dynamics of the biped to obtain stable walking gaits when it is constrained to the sagittal plane, the second shapes the total energy of the walker so that functional Routhian reduction can be applied to decoupling the dynamics of the walker for certain initial conditions, and the third utilizes an output zeroing controller to stabilize to the surface defining these initial conditions. We numerically verify that this method results in stable walking, and we discuss certain attributes of this walking gait
Humanoid robot walking control on inclined planes
The humanoid bipedal structure is suitable for a assitive robot functioning in the human environment. However, the bipedal walk is a difficult control problem. Walking just on even floor is not satisfactory for the applicability of a humanoid robot. This paper presents a study on bipedal walk on inclined planes. A Zero Moment Point (ZMP) based reference generation technique is employed. The orientation of the feet is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. This system uses a sampling time larger than the one of the joint space position controllers. The average value of the body pitch angle is used as the inputs to the fuzzy logic system. A foot pitch orientation compensator implemented independently for the two feet complements the fuzyy controller. A 12-degrees-of-freedom (DOF) biped robot model is used in the full-dynamics 3-D simulations. Simulations are carried out on even floor and inclined planes with different slopes. The results indicate that the control method presented is successful in enabling the robot to climb slopes of 8.5 degrees (15 percent grade)
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