In Lab 1pre, you have already installed ROS Kinetic. The FoodTutrle simulator for this lab is adapted from the ROS TurtleBot Stage package. Install TurtleBot Stage:

sudo apt install ros-kinetic-turtlebot-stage
sudo apt install ros-kinetic-joint-state-publisher-gui

Clone this repository and set up the environment:

git clone https://github.com/AdaCompNUS/CS4278-5478-MotionPlanning
cd CS4278-5478-MotionPlanning
catkin_make
source devel/setup.bash

Check your setup:

roslaunch planner turtlebot_in_stage.launch

You should see RViz and ROS Stage.

The FoodTurtle system consists of three components: the simulator, the planner, and the controller. The code in this repository provides most of these components, including the necessary functions for setting up ROS nodes in the simulator, data communication, and robot control. You will implement the planning algorithm and some robot interfaces by filling in the template labeled # TODO: FILL ME! in base_planner.py. We recommend that you use base_planner.py as a base class and then implement your planners as derived classes.

Launch the simulator and execute the planner:

roscd planner/src
sh run.sh [your map name] start_x start_y start_theta
python your_planner.py --goal 'goal_x,goal_y' --com use_com_1_map_or_not

For example, load map1.png and set the robot start pose as (x=1, y=1, θ=0):

roscd planner/src
sh run.sh map1 1 1 0

Set the robot goal as (x=5, y=5) and run the planner:

python your_planner.py --goal '5,5' --com 0

The flag --com indicates whether the COM1 map is used, as it requires a special set of environment parameters.

Without adding any implementation, you should see the robot move forward by two tiles and then turn left.

For visualization, we recommend the ROS Stage. RViz provides 2.5-D visualization, but may be noisy due to the asynchronous communication delay of ROS.

A dataset provides 5 maps for evaluation, including 4 hand-crafted maps (map1.png to map4.png) and a simplified COM1 level-1 floor plan (com1building.png). Each map has a corresponding list of test cases with the initial state and goals specified here.

Evaluate your planners in all test cases under the three models, DSDA, CSDA and DSPA. For DSDA and CSDA, save the control actions in a txt file and name it {task}_{map}_{goal_x}_{goal_y}.txt. For DSPA, save the MDP control policy in a json file and name it {task}_{map}_{goal_x}_{goal_y}.json.

For example,

• DSDA_map2_5_5.txt for DSDA on map2.png with [5, 5] as the goal.
• CSDA_map3_9_9.txt for CSDA on map3.png with [9, 9] as the goal.
• DSPA_com1building_43_10.json for the MDP policy on com1building.png with [43, 10] as the goal.

You can find the functions to save the results in base_planner.py. Some examples of control files can be found here.

Submit a single zip archive named {MatricNo}-Lab1.zip, which contains 3 directories:

• Code: your code
• Controls: the control files for all testcases
• Report: your report