Heuristic Search for Path Finding With Refuelling
This letter considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSPs...
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| Vydáno v: | IEEE robotics and automation letters Ročník 10; číslo 4; s. 3230 - 3237 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | This letter considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSPseeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While GSPis polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since it requires simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This letter develops a heuristic search algorithm called <inline-formula><tex-math notation="LaTeX">\text{Refuel A}^*</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">\text{RF-A}^*</tex-math></inline-formula>) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic while leveraging dominance rules for pruning during planning. <inline-formula><tex-math notation="LaTeX">\text{RF-A}^*</tex-math></inline-formula>is guaranteed to find an optimal solution and often runs 2 to 8 times faster than the existing approaches in large city maps with several hundreds of gas stations. |
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| AbstractList | This letter considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSPseeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While GSPis polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since it requires simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This letter develops a heuristic search algorithm called [Formula Omitted] ([Formula Omitted]) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic while leveraging dominance rules for pruning during planning. [Formula Omitted]is guaranteed to find an optimal solution and often runs 2 to 8 times faster than the existing approaches in large city maps with several hundreds of gas stations. This letter considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSPseeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While GSPis polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since it requires simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This letter develops a heuristic search algorithm called <inline-formula><tex-math notation="LaTeX">\text{Refuel A}^*</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">\text{RF-A}^*</tex-math></inline-formula>) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic while leveraging dominance rules for pruning during planning. <inline-formula><tex-math notation="LaTeX">\text{RF-A}^*</tex-math></inline-formula>is guaranteed to find an optimal solution and often runs 2 to 8 times faster than the existing approaches in large city maps with several hundreds of gas stations. |
| Author | Ren, Zhongqiang Rathinam, Sivakumar Choset, Howie Zhao, Shizhe Nandy, Anushtup |
| Author_xml | – sequence: 1 givenname: Shizhe surname: Zhao fullname: Zhao, Shizhe email: shizhe.zhao@sjtu.edu.cn organization: UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China – sequence: 2 givenname: Anushtup orcidid: 0009-0005-3143-7285 surname: Nandy fullname: Nandy, Anushtup email: anandy@andrew.cmu.edu organization: Carnegie Mellon University, Pittsburgh, PA, USA – sequence: 3 givenname: Howie orcidid: 0000-0002-5434-7945 surname: Choset fullname: Choset, Howie email: choset@andrew.cmu.edu organization: Carnegie Mellon University, Pittsburgh, PA, USA – sequence: 4 givenname: Sivakumar orcidid: 0000-0002-9223-7456 surname: Rathinam fullname: Rathinam, Sivakumar email: srathinam@tamu.edu organization: Department of Mechanical Engineering and Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA – sequence: 5 givenname: Zhongqiang orcidid: 0000-0003-2880-8653 surname: Ren fullname: Ren, Zhongqiang email: zhongqiang.ren@sjtu.edu.cn organization: UM-SJTU Joint Institute and Department of Automation, Shanghai Jiao Tong University, Shanghai, China |
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| SubjectTerms | Apexes Autonomous aerial vehicles Costs Dynamic programming Fuels Graph theory Heuristic Mathematical programming Motion and path planning Motion planning Path planning Polynomials Refueling Robot kinematics robotics in under-resourced settings Runtime scheduling and coordination Search algorithms Service stations Training Urban areas |
| Title | Heuristic Search for Path Finding With Refuelling |
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