Tag Archives: kinematics

Body/spine and improved leg kinematics

 

The Python test program has been updated to include the additional spine joints, transformation between the robot and world coordinates, and leg targets which take orientation into account.

This test script is used in anticipation for controlling the actual robot’s servos.

 


Spine joints

The “spine” consists of two joints which will allow the front of the robot to pitch and yaw independently of the rear. This will give it more flexibility when turning and handling uneven terrain, as well as other tasks such as aiming its sensors at the world.

Since the spine joints are quite simple, I don’t think there is any need to create IK for this section.

 

Main Assembly v114 Spine

The “spine” joints separate the body of the quadruped between mostly similar front and rear sections.

 


Body and spine orientation

The robot body now takes into account that it has to be oriented w.r.t. the “world”. This will be physically achieved by the information acquired by an IMU sensor. If the robot is tilted forwards, the targets for the legs will have to be adjusted so that the robot maintains its balance.

I have defined the kinematics in a way that if the the robot was to rotate w.r.t. the world, the whole body rotates (this can be achieved by moving the test Roll/Pitch/Yaw sliders). However if the servo joints of the spine are moved (test joint 1 / joint 2 sliders) the rear section of the robot will move w.r.t the world, and the rear legs will move with it, while the front section won’t change w.r.t. the world.

In order to achieve this, the leg IK had to be updated so that now the base frames of the front legs are linked to the front section of the robot, and the base frames of the rear legs are linked to the rear section.

You might notice while orientation will be defined by an IMU, pure translation (movement in XYZ) in the world is not considered for now, as it is meaningless without some sort of localisation capability in place. This could be achieved by a sensor (see below), but is an entirely separate challenge for a long way down the line (hint: SLAM).

Quadbot 17 Quad Kinematics_004

New leg targets: Foot roll/pitch can be attained (within limits). In addition, the robot base can be positioned with respect to an outside world frame.

Quadbot 17 Quad Kinematics_002

Original leg targets: The feet are always pointing vertical to the ground.

 


Target roll and yaw

Initially, the leg target was simply a position in 3D for the foot link, and the foot was always pointing perpendicular to the ground, which made the inverse kinematics fairly easy. In version 2, the target orientation is now also taken into account. Actually, the pitch and roll can be targeted, but yawing cannot be obtained, simply because of the mechanics of the legs. Yawing, or turning, can be done by changing the walking gait pattern alone,  but the idea is that the spine bend will also aid in steering the robot (how exactly I don’t know yet, but that will come later!).

Getting the kinematics to work were a bit trickier than the original version, mainly because the “pitching” orientation of the leg can only be achieved by the positioning of joint 4, whereas the “rolling” orientation can only be achieved by the positioning of joint 5. The available workspace of the foot is also somewhat limited, in part due to that missing yaw capability. Particularly at positions when the leg has to stretch sideways (laterally) then certain roll/pitching combinations are impossible to reach. Nevertheless, this implementation gives the feet enough freedom to be placed on fairly uneven surfaces, and not be constrained to the previously flat plane.

The next challenge that follows from this, is how can realistic target positions and orientations be generated (beyond predetermined fixed walking gaits), to match what the robot sees of the world?

To answer this, first I need to decide how the robot sees the world: Primarily it will be by means of some 3D scanner, such as the ones I’ve looked into in the past, or maybe the Intel RealSense ZR300 which has recently caught my attention. But this alone might not be sufficient, and some form of contact sensors on the feet may be required.

The big question is, should I get a RealSense sensor for this robot ??? 🙂

 


 

Updated code can be found on GitHub (single-file test script is starting to get long, might be time to split up into class files!).

 

 

Quadbot updates and sensor options

Updates

A few and bits and pieces have been added to the model, along with some updates: Longer lower leg and cover, battery and battery compartment in rear body, main electronics boards, foot base and plate, and two ideas for sensors.

The lower legs were extended as initially the “knee” and “ankle” joints were too close. I think the new arrangement gives the leg better overall proportions.

As the battery pack has significant size and weight, its best to include in the design as early as possible. Originally neglected, I have now added a Turnigy 2200 mAh battery, and updated the rear bumper and bracket to accommodate it. Heat dissipation may be an issue, but I’ll leave it like this for now.

I have also measured the placement of the actuators in order to start thinking about the maths for the kinematics.

Some images of current progress:


Sensors

I have tried two ideas for area scanners which could be the main “eyes” of the robot. One is the Kinect v2, and the other a Scanse Sweep.

The main advantages of the Sweep is that it is designed specifically for robotics, with a large range and operation at varying light levels. On its own it only scans in a plane by spinning 360°, however it can be turned into a spherical scanner with little effort. Added bonuses are a spherical scan mounting bracket designed specifically for Dynamixel servos, as well as ROS drivers! It is currently available only on pre-order on SparkFun.

The Kinect has a good resolution and is focused on tracking human subjects, being able to track 6 complete skeletons and 25 joints per person. However it only works optimally indoors and at short ranges directly in front of it (adaptor to convert to the USB interface. It is however significantly cheaper than the Sweep.

Below is a table comparing the important specs:

XBOX Kinect v2

Scanse Sweep

Technology

Time-of-Flight

LiDAR

Dimensions (mm)

249 x 66 x 67

65 x 65 x 52.8

Weight (kg)

1.4

0.12

Minimum Range (m)

0.5

~0.1

Maximum Range (m)

4.5

40

Sample rate (Hz)

30

1000

Rotation rate (Hz)

N/A

1-10

FOV (°)

70 x 60

360 (planar)

Resolution

512 x 424, ~ 7 x 7 pixels per °

1 cm

Price (£)

280

80 (32 for adaptor)

Sources:


WordPress tip: One thing I really like about WordPress.com is that there are always ways around doing things that initially only seem possible with WordPress.org. Need to add a table into your post? Use Open Live Writer, make the table then copy-paste the table’s generated HTML source code!


Hardware costs

The current estimated hardware costs are quite high, at around £2100. However about half this budget (£955) is due to the fact that I calculated the costs for the custom 3D printed parts by getting quotes from Shapeways. Getting them printed on a homemade 3D printer would reduce the cost significantly. The other large cost is naturally the 22 AX actuators at £790.

For anyone interested, the preliminary BOM can be downloaded here:

Quadbot 17 BOM – 2017-02-26.xlsx


That’s it for now!