~~NOTOC~~

====== Czech-Taiwan-Germany Robotics Workshop 2024 ======

  * Welcome to the [[https://comrob.fel.cvut.cz/|Computational Robotics Laboratory]], part of the [[https://www.aic.fel.cvut.cz/|Artificial Intelligence Center]] at the [[https://cs.fel.cvut.cz|Computer Science Department]], at the [[https://fel.cvut.cz/cs|Faculty of Electrical Engineering, Czech Technical University in Prague]].
  * Today, we shall create a simple behavior for a simulated hexapod walking robot, which would allow the robot to reactively avoid obstacles while pursuing given goal waypoint. (See the picture below.)
  * You can access this webpage and its contents also from home: [[https://cw.fel.cvut.cz/wiki/courses/crl-courses/aed23|cw.fel.cvut.cz/wiki/courses/crl-courses/ctg24]].


{{ :courses:crl-courses:aed23:t1b-react.gif?nolink&600 |}}

===== Robotics =====

  * Mechanics + Electronics + Perception (Sensing) + Control + AI + ... = Robotics
  * AI may be used to
      * plan robot actions,
      * prioritize tasks,
      * detect or distinguish data,
      * learn, self-improve,
      * cooperate,
      * react,
      * decide, ... etc.
  * A robot consists of
      * actuators (motors, servomotors, valves, optics, ...),
      * sensors (accelerometers, cameras, pressure, laser scanners, ...),
      * brains (Raspberry PI, Intel NUC, Arduino, ...),
      * energy source (batteries, external supply, solar panels, ...),
      * body (LEGO, 3D Printing, ...).

{{ :courses:crl-courses:aed23:cras-robots.jpg?nolink&800 }}

  * A robot state description may be abstracted into its **position** and **orientation** described by coordinates in a reference frame.
      * We shall use 2D coordinates, i.e., X, Y and the angle measured from positive direction of the X axis.
      * Thus, having a suitable controller, a generalized **velocity command** may be used to control any robot.
          * Such a command comprises the desired linear and angular velocity.
      * In our case, we have a 6-legged robot with 3 actuators per leg, thus in fact the controller needs to produce 18 control signals based on the two values in the velocity command.


<note tip>
The controller itself may be implemented by various aproaches, e.g., by the //Central Pattern Generator// (CPG).
The controller computes desired joint angles such that the leg is raised, moved forward, lowered and then moved backward.
The CPG produces such movements for all 6 legs, synchronizing the phases to keep always three legs on the ground.
This makes the 6-legged (or hexapod) robot stable as the center of mass is supported at least by a triangular //support polygon//.
</note>

===== Simulation =====

  * The computers in the classroom run Ubuntu OS with all the required software installed.
  * Please, log in via the credentials provided on the paper strip.
  * We shall use the [[https://www.coppeliarobotics.com/|CoppeliaSim]] app.
      * The simulator can be downloaded and run at home as well.
  * Open a terminal by ''Actr+Alt+T''.
  * Run the simulator by issuing ''/opt/CoppeliaSim_Edu_V4_3_0_Ubuntu20_04/coppeliaSim.sh'' command.
  * Download the simulation resource pack: {{ :courses:crl-courses:aed23:aed23_resource_pack.zip |}}.
      * The resource pack contains robot model, scene and driving scripts in Python.
      * Unzip the resource pack.
      * Load the scene from the resource pack. (''Files'' -> ''Open scene...'' -> ''~/Downloads/aed23_resource_pack/scenes/aed23.ttt''.) 
  * Robot is driven by a set of [[https://www.python.org/|jazyce Python]] scripts.
      * Open another terminal instance (''Ctrl+Alt+T'').
      * Run ''cd ~/Downloads/aed23_resource_pack; ./main.py''.
      * Robot should now tread in place.

===== Blind locomotion =====

  * We want the robot to navigate towards two ++ waypoints |, which can be given by some high-level mission or exploration planner++, depicted as green spheres in the simulator.
  * Robot knows its current pose and its current goal waypoint from which it needs to compute the velocity command.
  * Edit the script which controls the robot.
      * ''/opt/VSCode-linux-x64/code ~/Downloads/aed23_resource_pack/hexapod_robot/HexapodController.py''
  * First, delete the ''return result_command'' statement on line 28.
  * Robot needs to turn towards the target. Implement the desired heading as 
      * ''desired_heading_radian = math.atan2(dy,dx)''

{{ :courses:crl-courses:aed23:clash.png?nolink&600 |}}

===== Obstacle avoidance =====

  * Open the ''main.py'' and switch the controller to ''goto_reactive''.
      * This controller implements the [[https://mitpress.mit.edu/books/vehicles|Braitenberg vehicle]] concept.

^ ^  Vehicle 2a  ^  Vehicle 2b  ^  Vehicle 3a  ^  Vehicle 3b  ^
| |  {{:courses:uir:labs:breitenberg_2a.jpg?nolink&100}}  |  {{:courses:uir:labs:breitenberg_2b.jpg?nolink&100}}  |  {{:courses:uir:labs:breitenberg_3a.jpg?nolink&100}}  |  {{:courses:uir:labs:breitenberg_3b.jpg?nolink&100}}  |
| **Spojení** |  excitation |  excitation |  inhibition |  inhibition |
| **Chování** |  fear |  aggression |  love |  explore |
| **Vlastnosti** |  Avoids object. | Assaults object. |  Stops facing the object. | Stops facing away. |
 
  * We use the ++ LiDAR | (we can imagine a LiDAR as a source of dense laser beams with known direction; LiDAR measures distance traveled, e.g., via time-of-flight or the phase-shift calculations) ++ as the source of the stimulus.
      * We measure laser beams to the both sides of the robot centerline and use them to virtually repel the robot the closer it gets to an obstacle.
===== Extra Tasks =====

  * Process the LiDAR scans into 2D occupancy map.
  * Tweak walking or obstacle avoidance parameters and functions.
  * Play with the obstacles, play with the robot a bit.
  * Try some assignments from
      * [[https://cw.fel.cvut.cz/b231/courses/uir/start]] or 
      * [[https://cw.fel.cvut.cz/wiki/courses/crl-courses/redcp/start]]

/*
===== By the Way =====

This page was originally prepared for the "AI Expert for a Day" event, which aimed to expose some of the robotics for enthusiastic high-schoolers.
*/