1,109 research outputs found
A Massively-Parallel 3D Simulator for Soft and Hybrid Robots
Simulation is an important step in robotics for creating control policies and
testing various physical parameters. Soft robotics is a field that presents
unique physical challenges for simulating its subjects due to the nonlinearity
of deformable material components along with other innovative, and often
complex, physical properties. Because of the computational cost of simulating
soft and heterogeneous objects with traditional techniques, rigid robotics
simulators are not well suited to simulating soft robots. Thus, many engineers
must build their own one-off simulators tailored to their system, or use
existing simulators with reduced performance. In order to facilitate the
development of this exciting technology, this work presents an
interactive-speed, accurate, and versatile simulator for a variety of types of
soft robots. Cronos, our open-source 3D simulation engine, parallelizes a
mass-spring model for ultra-fast performance on both deformable and rigid
objects. Our approach is applicable to a wide array of nonlinear material
configurations, including high deformability, volumetric actuation, or
heterogenous stiffness. This versatility provides the ability to mix materials
and geometric components freely within a single robot simulation. By exploiting
the flexibility and scalability of nonlinear Hookean mass-spring systems, this
framework simulates soft and rigid objects via a highly parallel model for near
real-time speed. We describe an efficient GPU CUDA implementation, which we
demonstrate to achieve computation of over 1 billion elements per second on
consumer-grade GPU cards. Dynamic physical accuracy of the system is validated
by comparing results to Euler-Bernoulli beam theory, natural frequency
predictions, and empirical data of a soft structure under large deformation
Recommended from our members
Analysis and synthesis of bipedal humanoid movement : a physical simulation approach
textAdvances in graphics and robotics have increased the importance of tools for synthesizing humanoid movements to control animated characters and physical robots. There is also an increasing need for analyzing human movements for clinical diagnosis and rehabilitation. Existing tools can be expensive, inefficient, or difficult to use. Using simulated physics and motion capture to develop an interactive virtual reality environment, we capture natural human movements in response to controlled stimuli. This research then applies insights into the mathematics underlying physics simulation to adapt the physics solver to support many important tasks involved in analyzing and synthesizing humanoid movement. These tasks include fitting an articulated physical model to motion capture data, modifying the model pose to achieve a desired configuration (inverse kinematics), inferring internal torques consistent with changing pose data (inverse dynamics), and transferring a movement from one model to another model (retargeting). The result is a powerful and intuitive process for analyzing and synthesizing movement in a single unified framework.Computer Science
Multibody dynamics model of a full human body for simulating walking
Indiana University-Purdue University Indianapolis (IUPUI)Khakpour, Zahra M.S.M.E., Purdue University, May 2017. Multibody Dynamics Model of A Full Human Body For Simulating Walking, Major Professor: Hazim El-Mounayri.
Bipedal robotics is a relatively new research area which is concerned with creating walking robots which have mobility and agility characteristics approaching those of humans. Also, in general, simulation of bipedal walking is important in many other applications such as: design and testing of orthopedic implants; testing human walking rehabilitation strategies and devices; design of equipment and facilities for human/robot use/interaction; design of sports equipment; and improving sports performance & reducing injury. One of the main technical challenges in that bipedal robotics area is developing a walking control strategy which results in a stable and balanced upright walking gait of the robot on level as well as non-level (sloped/rough) terrains.
In this thesis the following aspects of the walking control strategy are developed and tested in a high-fidelity multibody dynamics model of a humanoid body model:
1. Kinematic design of a walking gait using cubic Hermite splines to specify the motion of the center of the foot.
2. Inverse kinematics to compute the legs joint angles necessary to generate the walking gait.
3. Inverse dynamics using rotary actuators at the joints with PD (Proportional-Derivative) controllers to control the motion of the leg links.
The thee-dimensional multibody dynamics model is built using the DIS (Dynamic Interactions Simulator) code. It consists of 42 rigid bodies representing the legs, hip, spine, ribs, neck, arms, and head. The bodies are connected using 42 revolute joints with a rotational actuator along with a PD controller at each joint. A penalty normal contact force model along with a polygonal contact surface representing the bottom of each foot is used to model contact between the foot and the terrain. Friction is modeled using an asperity-based friction model which approximates Coulomb friction using a variable anchor-point spring in parallel with a velocity dependent friction law.
In this thesis, it is assumed in the model that a balance controller already exists to ensure that the walking motion is balanced (i.e. that the robot does not tip over).
A multi-body dynamic model of the full human body is developed and the controllers are designed to simulate the walking motion. This includes the design of the geometric model, development of the control system in kinematics approach, and the simulation setup
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
Muscle-actuated control is a research topic that spans multiple domains,
including biomechanics, neuroscience, reinforcement learning, robotics, and
graphics. This type of control is particularly challenging as bodies are often
overactuated and dynamics are delayed and non-linear. It is however a very well
tested and tuned actuation mechanism that has undergone millions of years of
evolution with interesting properties exploiting passive forces and efficient
energy storage of muscle-tendon units. To facilitate research on
muscle-actuated simulation, we release a 3D musculoskeletal simulation of an
ostrich based on the MuJoCo physics engine. The ostrich is one of the fastest
bipeds on earth and therefore makes an excellent model for studying
muscle-actuated bipedal locomotion. The model is based on CT scans and
dissections used to collect actual muscle data, such as insertion sites,
lengths, and pennation angles. Along with this model, we also provide a set of
reinforcement learning tasks, including reference motion tracking, running, and
neck control, used to infer muscle actuation patterns. The reference motion
data is based on motion capture clips of various behaviors that we preprocessed
and adapted to our model. This paper describes how the model was built and
iteratively improved using the tasks. We also evaluate the accuracy of the
muscle actuation patterns by comparing them to experimentally collected
electromyographic data from locomoting birds. The results demonstrate the need
for rich reward signals or regularization techniques to constrain muscle
excitations and produce realistic movements. Overall, we believe that this work
can provide a useful bridge between fields of research interested in muscle
actuation.Comment: https://github.com/vittorione94/ostrichr
- …