{"id":13511226,"url":"https://kei18.github.io/sssp/","last_synced_at":"2025-03-30T20:32:48.502Z","repository":{"id":130695991,"uuid":"432896149","full_name":"Kei18/sssp","owner":"Kei18","description":"Quick Multi-Robot Motion Planning by Combining Sampling and Search (IJCAI-23)","archived":false,"fork":false,"pushed_at":"2023-09-24T15:01:04.000Z","size":43927,"stargazers_count":22,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-01T13:34:11.718Z","etag":null,"topics":["mapf","motion-planning","multi-agent-pathfinding","multi-robot","path-planning","sssp"],"latest_commit_sha":null,"homepage":"https://kei18.github.io/sssp/","language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Kei18.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-11-29T03:43:45.000Z","updated_at":"2024-10-16T20:48:27.000Z","dependencies_parsed_at":"2023-09-24T18:12:58.323Z","dependency_job_id":null,"html_url":"https://github.com/Kei18/sssp","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kei18%2Fsssp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kei18%2Fsssp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kei18%2Fsssp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kei18%2Fsssp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Kei18","download_url":"https://codeload.github.com/Kei18/sssp/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246379366,"owners_count":20767694,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["mapf","motion-planning","multi-agent-pathfinding","multi-robot","path-planning","sssp"],"created_at":"2024-08-01T03:00:39.201Z","updated_at":"2025-03-30T20:32:46.669Z","avatar_url":"https://github.com/Kei18.png","language":"Julia","funding_links":[],"categories":["Papers"],"sub_categories":[],"readme":"sssp\n===\n[![MIT License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](LICENCE.txt)\n[![CI](https://github.com/Kei18/sssp-private/actions/workflows/ci.yaml/badge.svg)](https://github.com/Kei18/sssp-private/actions/workflows/ci.yaml)\n\nThe code repository of the paper \"[Quick Multi-Robot Motion Planning by Combining Sampling and Search](https://kei18.github.io/sssp/)\" (SSSP; IJCAI-23).\n\n![](./assets/demo_reduced.gif)\n\n- It is written in Julia (≥v1.8).\n- The accompanied solvers are PRM [1], RRT [2], RRT-Connect [3], PP (PRM-based) [4], CBS (PRM-based) [5], and SSSP.\n\n## Setup\n\n```sh\ngit clone https://github.com/Kei18/sssp.git \u0026\u0026 cd sssp\njulia --project=. -e 'using Pkg; Pkg.instantiate()'\n```\n\n## Minimum Example\n\nHere, I give a minimal example of the MRMP library. You can implement the below with RELP or JupyterLab.\n\n#### Enter RELP\n```sh\njulia --project=.\n```\n\n#### Open JupyterLab\n\n```sh\njulia --project=. -e \"using IJulia; jupyterlab()\"\n```\n\n### Step 1. Generate Instance\n```jl\nimport Random: seed!\nusing MRMP\n\nseed!(46)\nins = MRMP.gen_random_instance_StatePoint2D(;\n    N = 5,\n    rad = 0.1,\n    num_obs = 3,\n    rad_obs = 0.1,\n)\nconfig_init, config_goal, obstacles, ins_params... = ins         # example of ins_params: radius, base positions of arms\n```\n\nThe first time may take time for JIT compiling.\nYou can visualize the generated instance as follows:\n\n```jl\nMRMP.plot_instance!(ins...)\n```\n\n![](./assets/minimum-example-ins.png)\n\n### Step 2. Define Utility Functions\n```jl\nconnect = gen_connect(config_init[1], obstacles, ins_params...)  # connection checker\ncollide = gen_collide(config_init[1], ins_params...)             # collision checker\ncheck_goal = gen_check_goal(config_goal)                         # goal judge\n```\n\n### Step 3. Solve Problem\n```jl\n@time solution, roadmaps = MRMP.Solvers.SSSP(\n    config_init,\n    config_goal,\n    connect,\n    collide,\n    check_goal;\n    TIME_LIMIT = 10,\n)\nvalidate(config_init, connect, collide, check_goal, solution)    # check validity of solution\n```\n\n### Step 4. Refine Solution\n```jl\n(TPG, solution, cost) = smoothing(solution, connect, collide; VERBOSE=1)\nprintln(cost)\n```\n\n### Step 5. Visualize Solution\n```jl\nplot_anim!(config_init, config_goal, obstacles, ins_params...; solution=solution, interpolate_depth=3, fps=30)\n```\n\nYou now get `tmp.gif` like below.\n\n![](./assets/minimum-example.gif)\n\n\n## Utilities\n\n#### Test\n```sh\njulia --project -e 'using Pkg; Pkg.test()'\n```\n\n#### Formatting\n```sh\njulia --project=. -e 'using JuliaFormatter; format(\".\")'\n```\n\n## Reproduction\n\n[![v1.2](https://img.shields.io/badge/tag-v1.2-blue.svg?style=flat)](https://github.com/Kei18/sssp/releases/tag/v1.2)\n\n```sh\njulia --project=. --threads=auto\n```\n\n### benchmark generation\n```jl\ninclude(\"scripts/benchmark_gen.jl\"); @time main(\"scripts/config/bench/capsule3d\", \"num_instances=50\")\n```\n\n### hyperparameter optimization with [Hyperopt.jl](https://github.com/baggepinnen/Hyperopt.jl)\n```jl\ninclude(\"scripts/hypraopt.jl\"); @time main(\"scripts/config/hypra/params.yaml\", \"scripts/config/hypra/point2d.yaml\")\n```\n\n### evaluate algorithms\n\n```jl\ninclude(\"./scripts/eval.jl\"); @time main(\"./scripts/config/exp/point2d.yaml\", \"time_limit_sec=300\")\n```\n\n## Notes\n- Several planning examples are available in `./notebooks`.\n- Dubins paths are computed by [Dubins.jl](https://github.com/kaarthiksundar/Dubins.jl).\n\n## Licence\nThis software is released under the MIT License, see [LICENSE.txt](LICENCE.txt).\n\n## Reference\n1. Kavraki, L. E., Svestka, P., Latombe, J. C., \u0026 Overmars, M. H. (1996).\n   Probabilistic roadmaps for path planning in high-dimensional configuration spaces.\n   IEEE transactions on Robotics and Automation.\n1. LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning.\n1. Kuffner, J. J., \u0026 LaValle, S. M. (2000).\n   RRT-connect: An efficient approach to single-query path planning.\n   In ICRA.\n1. Erdmann, M., \u0026 Lozano-Perez, T. (1987). On multiple moving objects. Algorithmica.\n1. Sharon, G., Stern, R., Felner, A., \u0026 Sturtevant, N. R. (2015).\n   Conflict-based search for optimal multi-agent pathfinding.\n   Artificial Intelligence (AIJ).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/kei18.github.io%2Fsssp%2F","html_url":"https://awesome.ecosyste.ms/projects/kei18.github.io%2Fsssp%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/kei18.github.io%2Fsssp%2F/lists"}