What it does ¶
This API is dedicated to make robot move to places.
There are 3 high-level ways to control the locomotion.
|Use ...||To set ...|
||a target pose on the ground plane, that the robot will walk to.|
||the robot’s instantaneous velocity (direction and intensity) in SI units (typically used to control the walk from a loop, with external input such as a visual tracker).|
||the robot’s instantaneous normalized velocity (direction and intensity) interactively (typically used to control the robot from a joystick, when the input gets normalized anyway).|
In addition, Move config parameters allows altering the default walk settings.
Performance and Limitations ¶
NAO ‘s walk is stabilized using feedback from his joint sensors. This makes the walk robust against small disturbances and absorbs torso oscillations in the frontal and lateral planes.
NAO is able to walk on multiple floor surfaces such as carpet, tiles and wooden floors.
He can transition between these surfaces while walking. However, large obstacles can still make him fall, as he assumes that the ground is more or less flat.
How it works ¶
|||S. Kajita and K. Tani. Experimental study of biped dynamic walking in the linear inverted pendulum mode. IEEE Int. Conf. on Robotics and Automation, 1995.|
|||P-B. Wieber. Trajectory-free linear model predictive control for stable walking in the presence of strong perturbation. IEEE Int. Conf. on Humanoids, 2006.|
A foot position is defined by an ALMath::Pose2D ( libalmath API reference ) relative to the last position of the other foot.
The foot planner is used by the walk process, irrespective of the walk control
used. When using
, the planner chooses the best foot placement.
If you plan the robot footsteps on your own and feed them to ALMotion using
, the builtin planner clips
the given footsteps to ensure the resulting motions are both collision
These parameters are used by the planner when planning the footsteps and/or clipping them. The parameters which are marked as user-settable are part of the Move config which can be passed on by the user.
|MaxStepX||maximum forward translation along X (meters)||0.040||0.001||0.080 ||yes|
|MinStepX||maximum backward translation along X (meters)||-0.040||no|
|MaxStepY||absolute maximum lateral translation along Y (meters)||0.140||0.101||0.160||yes|
|MaxStepTheta||absolute maximum rotation around Z (radians)||0.349||0.001||0.524||yes|
|MaxStepFrequency||maximum step frequency (normalized, unit-less)||
|MinStepPeriod||minimum step duration (seconds)||0.42||no|
|MaxStepPeriod||maximum step duration (seconds)||0.6||no|
|StepHeight||peak foot elevation along Z (meters)||0.020||0.005||0.035||yes|
|TorsoWx||peak torso rotation around X (radians)||0.000||-0.122||0.122||yes|
|TorsoWy||peak torso rotation around Y (radians)||0.000||-0.122||0.122||yes|
|FootSeparation||alter distance between both feet along Y (meters)||0.1||no|
|MinFootSeparation||minimum distance between both feet along Y (meters)||0.088||no|
|||we recommend 0.060 meters for StepX for more stability. Better use 0.080 meters only when walking on flat hard floors.|
Programmatically, you can access some of the gait parameters (Default, Max,
Min), using the
The clipping is done in three successive steps, which are explained here with
python functions implementing the same algorithms as ALMotion’s planner.
You can download them here:
For python-related troubleshooting, see the Python SDK - Installation Guide section.
The clipping algorithms uses the extremal values from the gait parameters table , and not the default or user-provided ones.
Clip with maximum outreach
Here, the python clipping algorithm is presented:
def clipFootStepOnGaitConfig(footMove, isLeftSupport): ''' Clip the foot move so that it does not exceed the maximum size of steps. footMove is an almath.Pose2D (x, y, theta position). isLeftSupport must be set to True if the move is on the right leg (the robot is supporting itself on the left leg). ''' def clipFloat(minValue, maxValue, value): ''' Clip value between two extremes. ''' clipped = value if (clipped < minValue): clipped = minValue if (clipped > maxValue): clipped = maxValue return clipped # Clip X. clippedX = clipFloat(minStepX, maxStepX, footMove.x) footMove.x = clippedX # Clip Y. if not isLeftSupport: clippedY = clipFloat(minFootSeparation, maxStepY, footMove.y) else: clippedY = clipFloat(-maxStepY, - minFootSeparation, footMove.y) footMove.y = clippedY # Clip Theta. clippedTheta = clipFloat(-maxStepTheta, maxStepTheta, footMove.theta) footMove.theta = clippedTheta
Clip with ellipse
To avoid singularities in the inverse kinematics, we clip the foot step data with an ellipsoid.
We use the AL::Math:clipFootWithEllipse function ( libalmath API reference ).
The figure below gives an illustration of the allowed zone (blue in the picture).
Here, the python clipping algorithm with ellipse is presented:
def clipFootStepWithEllipse(footMove): ''' Clip the foot move inside an ellipse defined by the foot's dimansions. footMove is an almath.Pose2D (x, y, theta position). ''' # Apply an offset to have Y component of foot move centered on 0. if (footMove.y < -minFootSeparation): footMove.y = footMove.y + minFootSeparation elif (footMove.y > minFootSeparation): footMove.y = footMove.y - minFootSeparation else: return # Clip the foot move to an ellipse using ALMath method. if footMove.x >= 0: almath.clipFootWithEllipse(maxStepX, maxStepY - minFootSeparation, footMove) else: almath.clipFootWithEllipse(minStepX, maxStepY - minFootSeparation, footMove) # Correct the previous offset on Y component. if footMove.y >=0: footMove.y = footMove.y + minFootSeparation else: footMove.y = footMove.y - minFootSeparation
Clip to avoid collision
The last clipping adjust pTheta to avoid collision between the feet using the AL::Math:avoidFootCollision function ( libalmath API reference ).
The picture below illustrates this clipping. (The grey left foot print was the one given by the user and the black left foot print is the result of the clipping).
Here, the python clipping algorithm with ellipse is presented:
def clipFootStepToAvoidCollision(footMove, isLeftSupport): """ Clip the foot move to avoid collision with the other foot. footMove is an almath.Pose2D (x, y, theta position). isLeftSupport must be set to True if the move is on the right leg (the robot is supporting itself on the left leg). """ # Bounding boxes of NAO's feet. rFootBox = [almath.Position2D(0.11, 0.038), # rFootBoxFL almath.Position2D(0.11, -0.050), # rFootBoxFR almath.Position2D(-0.047, -0.050), # rFootBoxRR almath.Position2D(-0.047, 0.038)] # rFootBoxRL lFootBox = [almath.Position2D(0.11, 0.050), # lFootBoxFL almath.Position2D(0.11, -0.038), # lFootBoxFR almath.Position2D(-0.047, -0.038), # lFootBoxRR almath.Position2D(-0.047, 0.050)] # lFootBoxRL # Use ALMath method. almath.avoidFootCollision(lFootBox, rFootBox, isLeftSupport, footMove)
The python program above illustrates the relationship between normalized step length and frequency and absolute velocity in SI units. The gait parameters used in the program are either the default values (see gait parameters ), or the one provided in a Move config , as explained in the next section.
def normalized_length_to_si_length(x_n, y_n, theta_n, f_n, mc, is_left_support): """ Convert the normalized step length and frequency into SI units. mc is the move config """ max_abs_step_x = mc.MaxStepX if (x_n >= 0) else (-mc.MinStepX) x_si = x * max_abs_step_x if is_left_support: y_si = min(y * (mc.MaxStepY - mc.FootSeparation)-mc.FootSeparation, -MinFootSeparation) else: y_si = max(y * (mc.MaxStepY - mc.FootSeparation)+mc.FootSeparation, MinFootSeparation) theta_si = x * mc.MaxStepTheta f_si = 1/(mc.MaxStepPeriod + f_n * mc.MaxStepFrequency * \ (mc.MinStepPeriod - mc.MaxStepPeriod)) return (x_si, y_si, theta_si, f_si) def normalized_length_to_si_velocity(x_n, y_n, theta_n, f_n, mc_l, mc_r): """ Convert the normalized step length and frequency into velocity in SI units. We consider a full walk cycle: double support, left support, double support, right support. mc is the move config """ (x_l, y_l, theta_l, f_l) = normalized_length_to_si_length( x_n, y_n, theta_n, f_n, gc_l, False) (x_r, y_r, theta_r, f_r) = normalized_length_to_si_length( x_n, y_n, theta_n, f_n, gc_r, True) period = 1/f_l + 1/f_r return ((x_l + x_r)/period, (y_l + y_r)/period, (theta_l + theta_r)/period)
It is possible to define custom gait parameters for the walk, by giving a custom Move config .
This allows you to modify the robot gait while still using the usual motion
API. This custom configuration can be used for all
For example, you can make NAO lift his feet higher to go over some small cables, etc.
For compatibility reasons, the way to pass per-foot config depends on the method called:
ALMotionProxy::moveToaccepts a single key-value list parameter, which is applied to both feet.
ALMotionProxy::moveTowardaccept a single key-value list parameter, whose keys may be prefixed with either “Left” or “Right” to target a single foot. For instance, one would do:
motionProxy.move(X, Y, Theta, [ ["LeftStepHeight", 0.02], ["RightStepHeight", 0.005], ["RightTorsoWx", -7.0*almath.TO_RAD], ["TorsoWy", 5.0*almath.TO_RAD] ] )
in order to set
TorsoWyto 5 degrees for both feet, and to set
StepHeighton a per-foot basis. Note that, for the left foot, the default value (0.) will be used for
TorsoWx. Note also that, in addition to the gait parameters, these two methods accept a
Frequencyparameter, which indicates a preferred normalized step frequency.
The following examples give some custom gait for NAO :
Torso height trajectory
Since NAOqi version 1.12, the torso height is automatically adjusted to avoid singularities and to enable longer steps.
Beware that using the same gait parameters than in NAOqi version 1.10 together with this new feature will create torso oscillations and could increase the torque needed in the legs joints. This will in turn reduce the battery life and make the joint temperature increase faster.
During walk, the arm are used to improve the walk stability and look. The arms motion amplitude during walk is dependent on the step frequency and length.
The arms motion can be activated or deactivated at any moment during a move. Moreover, any user commands for the arms will have priority over the default arm motions during a walk. This enables you to control the arms as you wish. If the default arm movements are enabled, they resume automatically once you have finished controlling arm movements.
# Example showing how to disable left arm motions during a move leftArmEnable = False rightArmEnable = True proxy.setMoveArmsEnabled(leftArmEnable, rightArmEnable) # disable right arm motion after 10 seconds time.sleep(10) rightArmEnable = False proxy.setMoveArmsEnabled(leftArmEnable, rightArmEnable)
# Example showing how to get the enabled flags for the arms print 'LeftArmEnabled: ', proxy.getMoveArmsEnabled("LArm") print 'RightArmEnabled: ', proxy.getMoveArmsEnabled("RArm") print 'ArmsEnabled: ', proxy.getMoveArmsEnabled("Arms")
In addition, passing an optional Move config key-value list, allows you to alter the default drive parameters for the wheeled base. These parameters are listed in the following table.
Getting started ¶
This section describes some key points to deal with move control.
Walk Initialization ¶
Before launching the walk process, the robot checks if:
- he is not in a singular configuration
- both its feet are flat on the ground.
If it is not the case, the robot first performs an initialization movement. This movement duration is dependant on the robot actual configuration and is inderterminate.
One can call
to trigger the initialization
And by calling this function before the move process, you make sure that the
move process will not take an indeterminate time before actually moving.
Robot Position ¶
Due to the very nature of preview control, the footsteps are stored in a queue, where the few first ones cannot be changed.
So, if the move process runs and you send a new target, your change is applied after the unchangeable footSteps.
method let you know
the future position of the robot after the last unchangeable foot step.
The figure below illustrates this phenomenon. You can also look at the robot position Tutorial .
# created a walk task motionProxy.moveInit() motionProxy.post.moveTo(0.2, 0.0, 0.1) # wait that the move process start running time.sleep(0.1) # get robotPosition and nextRobotPosition robotPosition = motionProxy.getRobotPosition(False) nextRobotPosition = motionProxy.getNextRobotPosition(False)
method can be used to
block your script/code execution until the move task is finished.
# Start a move proxy.post.moveTo(1.0, 0.0, 0.0, 1.0) # Wait for it to finish proxy.waitUntilMoveIsFinished() # Then do something else
method returns True while the move
task is active.
# start a 1 meter move proxy.post.moveTo(1.0, 0.0, 0.0, 1.0) while proxy.moveIsActive(): # do something # sleep a little time.sleep(1) # when finished do something else
Stopping the walk
method ends the walk task as soon as
the robot is in a relatively safe position, meaning that the two feet are on
the ground. This method is slower but safer than killing the walk, and is
faster than setting target velocity to 0.
method ends the walk task brutally,
without attempting to return to a balanced state. If the robot has one foot in
the air, he could easily fall.
# End the walk suddenly (~20ms) proxy.killMove()
To end the walk more gracefully, set the target velocity to zero.
# End the walk cleanly (~0.8s) proxy.moveToward(0.0, 0.0, 0.0)
Walk protection ¶
Kill the walk task when the robot is lifted
To stop the robot walking in the air, the FSRs are read to see if there is ground contact. When there is no ground contact, the walk task is killed if it is running, and not allowed to start if absent. This feature relies on the FSR extractor of the Sensors module, which is responsible for updating the following memory key:
By default, this feature is active. To remove this feature, follow this procedure:
# Deactivate the foot contact protection proxy.setMotionConfig([["ENABLE_FOOT_CONTACT_PROTECTION", False]]) # Or for change the default value, # define the new value of the key ENABLE_FOOT_CONTACT_PROTECTION # in ALMotion.xml preference file
Kill the walk task when the stiffness is low on at least one leg joint
If the move process is launched and the robot has stiffness off, our algorithm to solve the dynamics of the move fails. This is due to the closed loop, where we inject sensor information into the algorithm. When there is no stiffness, the command and sensor values diverge. To prevent this, the move process will be killed or prevented from launching, if one joint of the legs has a stiffness equal or less than 0,6.
By default, this feature is active. To remove this feature, follow this procedure:
# Deactivate the foot contact protection proxy.setMotionConfig([["ENABLE_STIFFNESS_PROTECTION", False]]) # Or for change the default value, # define the new value of the key ENABLE_STIFFNESS_PROTECTION # in ALMotion.xml preference file
Use Cases ¶
Case 1: Velocity Control ¶
Velocity control enables the move to be controlled reactively, allowing behaviors such as target tracking. It can be called as often as you like, as the most recent command overrides all previous commands.
The walk uses a preview controller to guarantee stability. This uses a preview of time of 0.8s, so the walk will take this time to react to new commands. At maximum frequency this equates to about two steps.
- x [-1.0 to 1.0]: specifies the length of a step along the x axis (forwards and backwards) as a fraction of maxStepX.
- y [-1.0 to 1.0]: specifies the length of a step along the y axis (lateral) as a fraction of maxStepY.
- theta [-1.0 to 1.0]: specifies the angle between the feet as a fraction of maxStepTheta. A positive value result in a left turn(anti-clockwise) and a negative value results in a right turn (clockwise).
The frequency parameter [0.0 to 1.0] of the move config specifies the step frequency as a fraction of linear interpolation between minimumStepFrequency and maximumStepFrequency. One cycle is considered to be a phase of double leg support followed by a phase of single leg support.
# Example showing the use of moveToward # The parameters are fractions of the maximum Step parameters # Here we are asking for full speed forwards # with maximum step frequency x = 1.0 y = 0.0 theta = 0.0 moveConfig = [["Frequency", 1.0]] proxy.moveToward(x, y, theta, moveConfig) # If we don't send another command, he will walk forever # Lets make him slow down (step length) and turn after 10 seconds time.sleep(10) x = 0.5 theta = 0.6 proxy.moveToward(x, y, theta, moveConfig) # Lets make him slow down(frequency) after 5 seconds time.sleep(5) moveConfig = [["Frequency", 0.5]] proxy.moveToward(x, y, theta, moveConfig) # After another 10 seconds, we'll make him stop time.sleep(10) proxy.moveToward(0.0, 0.0, 0.0) # Note that at any time, you can use a moveTo command # to move a precise distance. The last command received, # of velocity or position always wins
Case 2: Destination Control ¶
method is a generalized implementation of
walk patterns. An
is used to compute the path.
It is a blocking function until walk process is finished. If you pCalled this
function, the last command received overrides all previous commands.
- x: the distance in meters along the X axis (forwards and backwards).
- y: the distance in meters along the Y axis (lateral motion).
- theta: the final robot orientation in radians relative to the current orientation.
# Example showing the moveTo command. # As length of path is less than 0.4m # the path will use an SE3 interpolation # The units for this command are meters and radians x = 0.2 y = 0.2 # pi/2 anti-clockwise (90 degrees) theta = 1.5709 proxy.moveTo(x, y, theta)
Case 3: Step Control ¶
It is possible to control NAO’s steps individually, through a footstep planner. This allows to implement for example dance-like movements, where the footsteps need to be placed precisely:
- each leg can be controlled independently, giving the possibility of non symmetric behavior (for example, making the robot “limp”)
- each footstep has its own position (X, Y, Theta)
The footsteps planner either associates footsteps with the time they occur
(in that case, this is a
non-blocking call) or with a normalized footstep speed in
(this is a blocking call).
If you want to modify the footsteps planner, you may want to clear the footstep planner and add your new steps or to add the new footsteps at the end of the planner. To do so, you can use the clearExistingFootsteps parameter. If it is set to true , all footsteps that can be cleared are removed and the new footsteps are added, else the new footsteps are simply added at the end.
#! /usr/bin/env python # -*- encoding: UTF-8 -*- """Example: setFootStep - Small example to make Nao execute""" import qi import argparse import sys def main(session): """ Use setFootStep - Small example to make Nao execute The Cha Cha Basic Steps for Men Using setFootStep API. http://www.dancing4beginners.com/cha-cha-steps.htm This example is only compatible with NAO. """ # Get the services ALMotion & ALRobotPosture. motion_service = session.service("ALMotion") posture_service = session.service("ALRobotPosture") # Wake up robot motion_service.wakeUp() # Send robot to Stand Init posture_service.goToPosture("StandInit", 0.5) ############################### # First we defined each step ############################### footStepsList =  # 1) Step forward with your left foot footStepsList.append([["LLeg"], [[0.06, 0.1, 0.0]]]) # 2) Sidestep to the left with your left foot footStepsList.append([["LLeg"], [[0.00, 0.16, 0.0]]]) # 3) Move your right foot to your left foot footStepsList.append([["RLeg"], [[0.00, -0.1, 0.0]]]) # 4) Sidestep to the left with your left foot footStepsList.append([["LLeg"], [[0.00, 0.16, 0.0]]]) # 5) Step backward & left with your right foot footStepsList.append([["RLeg"], [[-0.04, -0.1, 0.0]]]) # 6)Step forward & right with your right foot footStepsList.append([["RLeg"], [[0.00, -0.16, 0.0]]]) # 7) Move your left foot to your right foot footStepsList.append([["LLeg"], [[0.00, 0.1, 0.0]]]) # 8) Sidestep to the right with your right foot footStepsList.append([["RLeg"], [[0.00, -0.16, 0.0]]]) ############################### # Send Foot step ############################### stepFrequency = 0.8 clearExisting = False nbStepDance = 2 # defined the number of cycle to make for range_counter in range( nbStepDance ): for i in range( len(footStepsList) ): try: motion_service.setFootStepsWithSpeed( footStepsList[i], footStepsList[i], [stepFrequency], clearExisting) except Exception, errorMsg: print str(errorMsg) print "This example is not allowed on this robot." exit() motion_service.waitUntilMoveIsFinished() # Go to rest position motion_service.rest() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--ip", type=str, default="127.0.0.1", help="Robot IP address. On robot or Local Naoqi: use '127.0.0.1'.") parser.add_argument("--port", type=int, default=9559, help="Naoqi port number") args = parser.parse_args() session = qi.Session() try: session.connect("tcp://" + args.ip + ":" + str(args.port)) except RuntimeError: print ("Can't connect to Naoqi at ip \"" + args.ip + "\" on port " + str(args.port) +".\n" "Please check your script arguments. Run with -h option for help.") sys.exit(1) main(session)
At any moment, you can retrieve the planned footsteps using
. This method gives:
- the footsteps that cannot be cleared (current steps and immediate followers), and
- the ones that can be safely removed (See robot position Tutorial ).