krr-up / mapf-instance-generator Goto Github PK

Hi, I cannot get it to run:

At uni:

 > ll ./gen.py 
-rwxr--r-- 1 torsten csstaff 4027 Aug 12 13:21 ./gen.py

 > ./gen.py random -c 10 -s 20 -a 5

doesn't terminate

The same for
python3 ./gen.py random -c 10 -s 20 -a 5

Write own configuration files to fix non random choices like for: (didn't work well)
Let python take care of random stuff like for:

• room: choosing if a wall has a door or not and where the door is placed

• random: choosing vertices

• agents: choosing position of starts and goals

Also:

• Implement the python part into the .lp files instead

Add option -d DOORS --doors DOORS to the generator (default=1)
Noticed that movingai has a room instance with up to two doors per wall:

replace the workaround version of the maze encoding with a proper one.

option for the agent.lp encoding that ensures minimum start-to-goal-distance (manhatten)

save instances in folder "generated _instances"

better readme with e.g. install instructions

exit with error when: number of agents > number of vertices for:

• just adding agents
• adding agents with minimal start to goal manhatten-distance

implement warehouse generator encoding

Also get and add meta information like shortest paths and their lengths

• load an instance with or without agents, starts and goals and:
  • just create meta data for instance
    or
  • clean and refill instance with agents

add options to generate the two types

save meta infos in seperate file instead of instance

gen IMAGE_PATH -s SIZE

converts image to instance of given size

Examples:

Image	Instance

• Cross Sides (CS): all agents start on one side and traverse to the other side
• Swap Sides (SS): half the agents start on one side and the other half start on the other side, targets are randomly selected in the side opposite of their sources
• Inside Out (IO): agents start near the center of the grid and are assigned targets near the outer edges of the grid
• Outside In (OI): agents start near the outer edges of the grid and are assigned targets near the center of the grid
• Tight to Tight (TT): agents start together and are assigned targets that are as close as possible elsewhere on the grid
• Tight to Wide (TW): agents start close together and are assigned targets that are spread out in the same general area of the grid

enable generation of variations of instances of same size and type
Now possible if programm is run in loop with the iterator as suffix

• cut out part of instance
• resize instance

For the DL work, it would be good to know whether there are acyclic solutions:

#edge ((A,U),(A,V)) : move(A,U,V,_), agent(A).

replace getoutput clingo calls by using python clingo module to improve performance

change argparser and encodings to allow for non-quadratic instance shapes

replace current way to retrieve horizon with multishot solving