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whoenig

libMultiRobotPlanning

Library with search algorithms for task and path planning for multi robot/agent systems.

AI EngineeringPrompt & ScaffoldC++
Open on GitHub
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+0stars in 24 hours0.0%
1.04k
Stars
239
Forks
+1
This week
4
Contributors
Created 2017-11-06 · Updated 2026-10-11 · #11928 today
Top developers
whoenigct2034kylevedder

whoenig/libMultiRobotPlanning is ranked 11,928 with 1,042 stars.

Same-category comparison

whoenig/libMultiRobotPlanning is ranked 11,928 among these 8 peers; kunal12203/GrapeRoot is ranked 11,931.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageC++
CategoryAI Engineering
SubcategoryPrompt & Scaffold
KeywordsPlanning, Search
Stars1,042
Forks239
Contributors4
Created date2017-11-06
Updated date2026-10-11
Stars in 1 day0
Stars in 7 days1
Rank11,928
Previous rank11,841
Rank change-87
7-day stars rank10,267
Previous 7-day stars rank8,357
7-day stars rank change-1,910
Total stars rank10,824
Previous total stars rank10,799
Total stars rank change-25
CountryGermany
README summary

libMultiRobotPlanning libMultiRobotPlanning is a library with search algorithms primarily for task and path planning for multi robot/agent systems. It is written in C++(14), highly templated for good performance, and comes with useful examples. The following algorithms are currently supported: Single-Robot Algorithms A* A* epsilon (also known as focal search) SIPP (Safe Interval Path Planning) Multi-Robot Algorithms Conflict-Based Search (CBS) Enhanced Conflict-Based Search (ECBS) Conflict-Based Search with Optimal Task Assignment (CBS-TA) Enhanced Conflict

-Based Search with Optimal Task Assignment (ECBS-TA) Prioritized Planning using SIPP (example code for SIPP) Assignment Algorithms Minimum sum-of-cost (flow-based; integer costs; any number of agents/tasks) Best Next Assignment (series of optimal solutions) Building Tested on Ubuntu 16. Targets : Build examples, only : build doxygen documentation : Re-format all source files : Run linter & static code analyzer : Run unit-tests Run specific tests Run example instances ECBS python3 . /cbs_roadmap -i mapf_simple1_roadmap_annotated.

Frequently asked questions

How many stars and forks are recorded?

whoenig/libMultiRobotPlanning has 1,042 stars and 239 forks.

What is the repository rank?

whoenig/libMultiRobotPlanning is ranked 11,928.

What language and category are recorded?

whoenig/libMultiRobotPlanning has 1,042 stars, uses C++ and belongs to AI Engineering.

Which repository is closest in rank?

whoenig/libMultiRobotPlanning is compared with kunal12203/GrapeRoot, ranked 11,931 with 1,041 stars.

README

libMultiRobotPlanning

libMultiRobotPlanning is a library with search algorithms primarily for task and path planning for multi robot/agent systems. It is written in C++(14), highly templated for good performance, and comes with useful examples.

The following algorithms are currently supported:

  • Single-Robot Algorithms

    • A*
    • A* epsilon (also known as focal search)
    • SIPP (Safe Interval Path Planning)

  • Multi-Robot Algorithms

    • Conflict-Based Search (CBS)
    • Enhanced Conflict-Based Search (ECBS)
    • Conflict-Based Search with Optimal Task Assignment (CBS-TA)
    • Enhanced Conflict-Based Search with Optimal Task Assignment (ECBS-TA)
    • Prioritized Planning using SIPP (example code for SIPP)

  • Assignment Algorithms

    • Minimum sum-of-cost (flow-based; integer costs; any number of agents/tasks)
    • Best Next Assignment (series of optimal solutions)

Building

Tested on Ubuntu 16.04.

mkdir build
cd build
cmake ..
make

Targets

  • make: Build examples, only
  • make docs: build doxygen documentation
  • make clang-format: Re-format all source files
  • make clang-tidy: Run linter & static code analyzer
  • make run-test: Run unit-tests

Run specific tests

python3 ../test/test_next_best_assignment.py TestNextBestAssignment.test_1by2

Run example instances

ECBS

./ecbs -i ../benchmark/32x32_obst204/map_32by32_obst204_agents10_ex1.yaml -o output.yaml -w 1.3
python3 ../example/visualize.py ../benchmark/32x32_obst204/map_32by32_obst204_agents10_ex1.yaml output.yaml

Generalized Roadmaps

CBS works on generalized graphs, with a particular focus on optional wait actions (e.g., this can be used with motion primitives as well). However, the roadmap annotation and visualization step currently assume a 2D Euclidean embedding and straight-line edges.

python3 ../tools/annotate_roadmap.py ../test/mapf_simple1_roadmap_to_annotate.yaml mapf_simple1_roadmap_annotated.yaml
./cbs_roadmap -i mapf_simple1_roadmap_annotated.yaml -o output.yaml
python3 ../example/visualize_roadmap.py mapf_simple1_roadmap_annotated.yaml output.yaml
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