Robot control
Forward dynamics
Definition
Forward dynamics predicts a robot's acceleration from its current configuration, velocity, applied joint forces or torques, and external forces. It uses the robot's mass, inertia, and other modeled dynamic properties.
Also known as: Robot forward dynamics
Updated
Predicting what applied effort will do
Given the current joint positions and velocities, a dynamics model accounts for inertia, gravity, and motion-dependent forces. It then solves for the acceleration produced by the supplied joint effort and external loading.
Modern Robotics demonstrates solving this problem using quantities assembled from inverse dynamics.
Acceleration becomes a simulated trajectory
A simulator integrates the calculated acceleration to update velocity and position over time. Repeating that process predicts a trajectory for chosen inputs. This is different from forward kinematics, which calculates a pose directly from known joint positions.
The integration method and time step matter. The Modern Robotics example discusses energy drift caused by numerical integration even when the modeled system has no dissipation.
Simulation reflects the model's scope
The reference lesson shows an arm swinging with zero commanded motor torque. Omitting joint friction makes the motion look different from a real arm; adding a friction model changes the prediction.
Contact, actuator behavior, and inertial parameters likewise have to be represented when relevant to the intended prediction. System identification can help estimate model parameters, but a successful simulation alone does not establish that the physical robot will follow the same trajectory.
Sources
Related terms
Inverse dynamics
Inverse dynamics calculates the joint forces or torques required for specified joint positions, velocities, and accelerations under a dynamics model. The result also depends on gravity and specified external loading.
System identification
System identification estimates a model of a physical system from measured inputs and outputs. In robotics, it can recover parameters such as inertia and friction or learn a more general model of how actions change the system state.
Trajectory optimization
Trajectory optimization finds a time-varying motion, and often control inputs, that minimizes an objective while satisfying specified constraints. Robot applications can include geometric, kinematic, and dynamic constraints.