Logistics (15 minutes)

• Discussion of first PA. How did it go. What did you learn.
• When2Meet: https://www.when2meet.com/?18557355-5pigl
• Please be flexible. We can’t satisfy everyone’s whim (“I am keeping my friday’s open from school work”)
• Reference: Robotics Lab Web Site

• Prev: 5: Actions
• Next: 6a: Simulators

Robots are made up from many Subsystems

• Subsystems are ‘represented’ in the software
• Locomotion (e.g. wheels)
• Actuation (e.g. arms)
• Sensing (e.g. cameras, lidar, buttons, encoders)
• Computing (e.g. single-board computer SBC, microcontroller)

Concepts in Actuation

• Holonomic - non-Holonomic
• Holonomic - can move in any direction
• Degrees of freedom
• Ambiguity in defining DoF

2 Wheel scenario

• Differential drive: Turtlebot3, Platform, Bullet
• Turning by different speed on two or more wheels
• Sometimes there is a caster
• Can only move forward and backward or pivot in place

4 Wheel scenario

• Not up and down or (directly) sideways
• With more than 2 wheels, or a track - skid steering
• Cars have “Akkerman” steering (4 wheels, 2 that can steer)

Tracked Vehicles

• Skid steering
• Problem with odometry

Manipulator Arms

• Degrees of freedom
• Still doesn’t tell the whole story - how fast, in what order, or simulatanuous?
• Coordinate transforms
• Forward and reverse Kinematics

Quadruped

• Gait - trot, gallop, etc
• Sensing vs. Non Sensing issues

Common case: Differential drive

Lower Level

• Subscribes to cmd_vel
  • Desired translation and rotation speeds
  • Reverse kinematics tells us desired rotations per second for each wheel
• Publishes to odom
  • Forward kinematics tells us estimated actual motion (position and speed)
  • ‘Dead Reckoning’

Supporting hardware

• Two wheels + 1 or 2 casters
• Each wheel has motor and encoder
• Motor will go “faster” when given more current
• Encoder counts up for each rotation

Algorithm

• PID control to minimize delta between desired and actual

Sensors

LiDAR

• Rotates and uses a laser to measure distance to nearest obstacle
• Returns a vector of values corresponding to the directions
• LiDAR’s can operate in 2d (scan) or 3d (point cloud), ours is 2d

Visual Cameras

• Webcam sees a color picture
• Matrix of dots, each dot has a color
• Dot[x,y] = {r,g,b}
• Data can be processed for example with opencv
• A lot of data and a lot of processing
• Bandwidth limits

Depth Cameras

• Like a Microsoft Kinect
• In addition to {r,g,b} also has distance so we get {r,g,b,d}
• Useful for getting range measurements of an object detected through camera

Computation

• Distributed environment, typically:
  1. roscore running onboard. on Raspberry pi
  2. Some additional ROS nodes running on Raspberry Pi
  3. More ROS nodes running on a laptop or other computer
• Workload is truly distributed across all of those
• Eventually we may have to have a much beefier computer running all three
• This would make the robot much more independent

Our actual Robots

1. Differential Drive - 2 powered wheels and one (or two) casters
2. Arduino or Arduino-like computer to control the motor.
3. Motor Controller and IMU directly connected to Arduino
4. Raspberry Pi which runs Ubuntu and ROS, connected to Arduino via a USB cable
5. Lidar connected to Raspberry Pi connected via USB cable

Thank you. Questions?  (random Image from picsum.photos)