dim
Positive integer path or robot dimension.
copp.Robot
Native robot model and station-indexed constraint buffer.
Syntax
OBJ = copp.Robot(DIM)
OBJ = copp.Robot(DIM, CAPACITY)
OBJ = copp.Robot(DIM, Capacity=CAPACITY)
Description
Robot owns a native CoppRobot handle. The handle stores station samples, sampled path derivatives, and constraints used by TOPP/COPP solvers. This MATLAB class exposes the workflow-oriented surface used by TOPP and COPP solvers: station append, path derivative input, limit helpers, raw constraints, inverse dynamics callbacks, and solver metadata.
Matrix convention: MATLAB inputs use \(\mathrm{dim} \times N\) column-major matrices for robot/path data. Here dim is robot.dim and N is the number of station samples covered by the call. Each column corresponds to one station sample. One-dimensional station/profile inputs may be row or column vectors, but solver/profile outputs are returned as double column vectors.
Constraint convention: Per-axis limit vectors have shape \(\mathrm{dim} \times 1\) or 1-by-dim and are broadcast over the selected station interval. Per-axis station-varying limit matrices have shape \(\mathrm{dim} \times N\). Raw constraint matrices have shape R-by-N, where R is the number of inequality rows per station.
Input Arguments
capacity
Input value for copp.Robot. See Description for accepted shape and constraints. Default: 0
Name-Value Arguments
Capacity
Name-value option for copp.Robot. Default: capacity
Properties
dim
Robot/path dimension.
len
Number of logical station samples currently stored.
capacity
Allocated native station-buffer capacity.
constraints
Lightweight facade that forwards to Robot constraint APIs.
Methods
Robot
Construct a native robot and empty constraint buffer.
delete
Release the native robot handle during MATLAB cleanup.
release
Explicitly release the native CoppRobot handle.
is_valid
Return whether this object still owns a live native robot.
append_s
Append strictly increasing path station samples.
set_q_2nd
Store q, dq/ds, and d2q/ds2 over a station interval.
set_q_3rd
Store q and derivatives through third order.
set_q_from_path_2nd
Sample a Path and store second-order derivatives.
set_q_from_path_3rd
Sample a Path and store third-order derivatives.
add_velocity_limits
Add axial velocity bounds.
add_acceleration_limits
Add axial acceleration bounds.
add_jerk_limits
Add axial jerk bounds for third-order solvers.
add_torque_limits
Add axial torque bounds using inverse dynamics.
add_raw_constraint_1st
Add rows a <= amax over a station interval.
add_raw_constraint_2nd
Add rows acc_a*a + acc_b*b <= acc_max.
add_raw_constraint_3rd
Add third-order raw jerk inequalities.
clear_constraints
Clear station samples, derivatives, and constraints.
pop_front_n
Remove N logical stations from the front.
pop_back_n
Remove N logical stations from the back.
set_inverse_dynamics
Install a MATLAB inverse-dynamics callback.
clear_inverse_dynamics
Restore default point dynamics \(\tau = \ddot{q}\).
Method Details
Robot
Syntax
OBJ = copp.Robot(DIM)
OBJ = copp.Robot(DIM, CAPACITY)
OBJ = copp.Robot(DIM, Capacity=CAPACITY)
creates an empty DIM-dimensional robot. OBJ = copp.Robot(DIM, CAPACITY) also gives the native station buffer an initial capacity hint. CAPACITY may be zero. OBJ = copp.Robot(DIM, Capacity=CAPACITY) is the name-value equivalent, matching the style used by the rest of the MATLAB facade.
delete
Syntax
delete(obj)
release
Syntax
RELEASED = release(OBJ)
is_valid
Syntax
tf = is_valid(obj)
append_s
Syntax
obj = append_s(obj, s)
appends a real finite vector of station values. S may be \(1 \times N\) or \(N \times 1\). S must be strictly increasing, and if OBJ already stores stations then S(1) must be greater than the current last station.
The method returns OBJ to support chaining.
set_q_2nd
Syntax
This mirrors Python Robot.set_q(..., dddq=None), while MATLAB
writes second-order geometric path derivatives starting at station 1. Q, DQ, and DDQ must have the same \(\mathrm{dim} \times N\) shape. Column k corresponds to station start_idx_s + k - 1.
Name-value options: start_idx_s: 1-based global station index where the first column is written. Defaults to 1.
This mirrors Python Robot.set_q(..., dddq=None), while MATLAB keeps the explicit second-order name for readability.
set_q_3rd
Syntax
obj = set_q_3rd(obj, q, dq, ddq, dddq)
obj = set_q_3rd(obj, q, dq, ddq, dddq, start_idx_s=start_idx_s)
writes third-order geometric path derivatives starting at station 1. All matrices must have the same \(\mathrm{dim} \times N\) shape, with one column per station.
Name-value options: start_idx_s: 1-based global station index where the first column is written. Defaults to 1.
set_q_from_path_2nd
Syntax
set_q_from_path_2nd(..., idx_s_from=I, idx_s_to=J)
evaluates PATH at the station values already stored in OBJ and writes q, dq/ds, and d2q/ds2 from station 1 through OBJ.len.
PATH must remain valid only for the duration of this call; the sampled derivatives are copied into the native Robot.
set_q_from_path_3rd
Syntax
set_q_from_path_3rd(..., idx_s_from=I, idx_s_to=J)
evaluates PATH at OBJ's stored station values and writes q, dq/ds, d2q/ds2, and d3q/ds3 from station 1 through OBJ.len.
add_velocity_limits
Syntax
obj = add_velocity_limits(obj, upper, lower)
obj = add_velocity_limits(obj, upper, lower, start_idx_s=start_idx_s, length=length)
adds per-axis bounds over the interval from station 1 through OBJ.len. Vector inputs must have length dim (\(\mathrm{dim} \times 1\) or 1-by-dim) and are broadcast over the interval. Matrix inputs must have shape \(\mathrm{dim} \times N\) and specify station-varying limits, where N is the selected interval length.
Name-value options: start_idx_s: 1-based first station receiving the limits. Defaults to 1. length: Number of stations for broadcast vector limits. When omitted, length is inferred as OBJ.len - start_idx_s + 1.
The method returns OBJ to support chaining.
add_acceleration_limits
Syntax
obj = add_acceleration_limits(obj, upper, lower)
obj = add_acceleration_limits(obj, upper, lower, start_idx_s=start_idx_s, length=length)
This method has the same vector/matrix shape, indexing, Inf, and length inference rules as add_velocity_limits(), but it writes second-order acceleration constraints used by TOPP2/COPP2.
The method returns OBJ to support chaining.
add_jerk_limits
Syntax
obj = add_jerk_limits(obj, upper, lower)
obj = add_jerk_limits(obj, upper, lower, start_idx_s=start_idx_s, length=length)
This method has the same vector/matrix shape, indexing, Inf, and length inference rules as add_velocity_limits(), but it writes third-order jerk constraints used by TOPP3/COPP3 solvers. The robot must already contain third-order path derivative data over the target interval.
add_torque_limits
Syntax
obj = add_torque_limits(obj, upper, lower)
obj = add_torque_limits(obj, upper, lower, start_idx_s=start_idx_s, length=length)
This method has the same vector/matrix shape, indexing, Inf, and length inference rules as add_velocity_limits(): vector bounds have length dim and are broadcast, while matrix bounds are \(\mathrm{dim} \times N\) with one column per station. Torque evaluation uses the currently installed inverse-dynamics callback, or the native default point dynamics \(\tau = \ddot{q}\) when no callback is installed.
add_raw_constraint_1st
Syntax
obj = add_raw_constraint_1st(obj, amax)
obj = add_raw_constraint_1st(obj, amax, start_idx_s=start_idx_s)
AMAX may be a vector of length N for one raw row per station, or an R-by-N matrix for R raw inequality rows per station. Column k corresponds to station start_idx_s + k - 1.
add_raw_constraint_2nd
Syntax
obj = add_raw_constraint_2nd(obj, acc_a, acc_b, acc_max)
obj = add_raw_constraint_2nd(obj, acc_a, acc_b, acc_max, start_idx_s=start_idx_s)
ACC_A, ACC_B, and ACC_MAX must have identical shapes. Use a length-N vector for one row per station, or an R-by-N matrix for R raw rows per station. Columns are station samples.
add_raw_constraint_3rd
Syntax
obj = add_raw_constraint_3rd(obj, jerk_a, jerk_b, jerk_c, jerk_d, jerk_max)
obj = add_raw_constraint_3rd(obj, jerk_a, jerk_b, jerk_c, jerk_d, jerk_max, start_idx_s=start_idx_s)
JERK_A, JERK_B, JERK_C, JERK_D, and JERK_MAX must have identical shapes. Use a length-N vector for one row per station, or an R-by-N matrix for R raw rows per station. Columns are station samples.
clear_constraints
Syntax
obj = clear_constraints(obj)
obj = clear_constraints(obj, keep_idx_s=keep_idx_s)
keep_idx_s=true keeps the \(1 \times N\)/\(N \times 1\) station vector S and clears derivative and constraint buffers. keep_idx_s=false clears the station vector as well.
pop_front_n
Syntax
obj = pop_front_n(obj, n)
pop_back_n
Syntax
obj = pop_back_n(obj, n)
set_inverse_dynamics
Syntax
tau = callback(q, dq, ddq)
CALLBACK is called pointwise, not batched:
clear_inverse_dynamics
Syntax
obj = clear_inverse_dynamics(obj)