Rigid Body Dynamics Algorithms

By Roy Featherstone. 2007

1,411 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/9a556651213631bacdc49aaaa5c9caa6ae6c7e91

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In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

Learning force control policies for compliant manipulation (2011, background): “Another potential improvement can arise from limiting the generation of noise in force/torque space to directions constrained by contact.”
Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges (2021, background): “For , ns " 3 the model converges to the exact continuum representation, but the computational cost for the simulation will increase as ( ) O ns(2) at best [58].”
Learning agile and dynamic motor skills for legged robots (2019, positive): “However, this analytical model does not include the set of mechanisms that map the actuator commands to the generalized forces acting on the rigid-body system: the actuator dynamics, the delays in control signals introduced by multiple hardware and software layers, the low-level controller…”
Capturability-based analysis and control of legged locomotion, Part 2: Application to M2V2, a lower-body humanoid (2012, methodology): “An inverse dynamics algorithm (Featherstone 2008), augmented by PD position control in joint space is used to compute the desired torques for the swing leg joints.”
MuJoCo: A physics engine for model-based control (2012, background): “Note that contact simulation is an area of active research, unlike simulation of smooth multi-joint dynamics where the book has basically been written [4].”
Centroidal dynamics of a humanoid robot (2013, background): “Spatial notation (Featherstone 2008; Featherstone and Orin 2008) is a concise vector notation for describing rigid-body velocity, acceleration, inertia, etc.”
The Pinocchio C++ library : A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives (2019, neutral): “Indeed, from a theoretical point of view, it is known that ABA has a larger complexity in terms of number of operations than RNEA [2].”
Slip Detection and Recovery for Quadruped Robots (2015, methodology): “The robot-specific software, namely the kinematics and dynamics engine, is implemented with RobCoGen which provides an optimized C++ implementation of kinematics and dynamics [11] based on spatial-vectors algebra and state-of-the-art numerical algorithms [7].”
Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX (2015, methodology): “Early work in robot dynamics gave rise to a range of efficient recursive algorithms [1], implemented perhaps most notably in SD/FAST [2] as well as the MATLAB Robotics Toolbox [3].”
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation (2021, background): “3 Using Brax: The core physics loop Brax simulates physical interactions in maximal coordinates[33], where every independent entity in a scene that can freely move is tracked separately.”
Learning, planning, and control for quadruped locomotion over challenging terrain (2011, methodology): “This control law is just a projection of the floating-base inverse dynamics (the term in square brackets), which can be computed with efficient algorithms that scale linearly with the number of the DOFs of the robot (Featherstone 2007).”
Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots (2018, methodology): “For modeling and computation of kinematics and dynamics, we use the opensource Rigid Body Dynamics Library [20] (RBDL), which is a C++ implementation of the algorithms described in [21].”
Learning Agile Robotic Locomotion Skills by Imitating Animals (2020, background): “The corresponding target keypoint xi(qt) is determined by the robot’s pose qt, represented in generalized coordinates [15].”
Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality (2018, methodology): “ich describe the observer’s dynamics, and I com(t) 2R 3 is the rotational inertia of the robot (as a rigid body) computed at time t. On the other hand, Eq. (32) can be rewritten using spatial algebra [49], considering the whole robot as a rigid body: h˙ = d dt ( I¯ com v) = I¯ com v˙ + ¯ com = W kno”
Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control (2019, methodology): “the OC problem. For instance, we use 10 12 as a damping factor in many of our results. 2) Impulse dynamics: Transitions from non-contact to contact condition are called contact gain in the literature [22]. In these instantaneous phases, the system encounters very high forces acting instantaneously (”