An Improved Multiobjective Shortest Path Algorithm

Casas, Pedro Maristany de las; Borndörfer, Ralf

doi:10.1016/j.cor.2021.105424

Cited by 26 publications

(38 citation statements)

References 21 publications

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“…The path planning of AMR is a constrained optimization problem. The algorithms include Genetic algorithm 4 , Probabilistic Roadmap 5 , Rapidly-exploring-random Tree 3 , 6 , Dijkstra algorithm 7 , A* algorithm 8 – 10 , Machine learning algorithm 11 – 13 , Ant Colony algorithm 14 , Particle Swarm Optimization 15 , Artificial potential field algorithm 16 , 17 and Breath First Search algorithm 18 , and so on.…”

Section: Introductionmentioning

confidence: 99%

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Xiang

Lin

Ouyang

et al. 2022

Sci Rep

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With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.

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Section: Introductionmentioning

confidence: 99%

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Xiang

Lin

Ouyang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In Section 2.1 to Section 2.4 we discuss our first main contributions: how to reduce the size of G. The resulting One-to-One MOSP instance defined on the reduced transition graph is used in our second main contribution: the Implicit Graph Multiobjective Dijkstra Algorithm (IG-MDA) introduced in Section 3. It features multiple recent techniques to efficiently solve large scale MOSP instances [Pulido et al, 2014, Sedeño-Noda and Colebrook, 2019, Maristany de las Casas et al, 2021b. We also use these techniques in our implementation of the BN algorithm.…”

Section: Literature Review and Outlinementioning

confidence: 99%

“…Further details on how the original T-MDA proceeds, its correctness, and further speedup techniques in the implementation can be read in [Maristany de las Casas et al, 2021a]. We now explain how G is build and handled in the IG-MDA.…”

Section: New Dynamic Programming Algorithmmentioning

confidence: 99%

“…A technique used by Pulido et al [2014] to speedup the ⪯ D dominance checks is called dimensionality reduction. We already used it in [Maristany de las Casas et al, 2021a] to design the T-MDA and obtained a considerable speedup. Dimensionality reduction is used in conjunction with lexicographic sorting of explored paths.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…This sorting implies γ 1 (p) ≤ γ 1 (p ′ ) if a path p ′ is extracted from the queue after a path p. Thus, to determine whether p ′ is dominated by p, it suffices to compare the remaining (d − 1) cost dimensions. Further implications of this technique are discussed in detail in [Pulido et al, 2014, Maristany de las Casas et al, 2021a. Note that in [Santos et al, 2018] the authors achieved their fastest running times using the sum of cost components as the sorting criteria for explored paths.…”

Section: Implementation Detailsmentioning

confidence: 99%

See 2 more Smart Citations

An FPTAS for Dynamic Multiobjective Shortest Path Problems

2021

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The Dynamic Multiobjective Shortest Path problem features multidimensional costs that can depend on several variables and not only on time; this setting is motivated by flight planning applications and the routing of electric vehicles. We give an exact algorithm for the FIFO case and derive from it an FPTAS for both, the static Multiobjective Shortest Path (MOSP) problems and, under mild assumptions, for the dynamic problem variant. The resulting FPTAS is computationally efficient and beats the known complexity bounds of other FPTAS for MOSP problems.

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Targeted multiobjective Dijkstra algorithm

2023

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We introduce the Targeted Multiobjective Dijkstra Algorithm (T‐MDA), a label setting algorithm for the One‐to‐One Multiobjective Shortest Path (MOSP) Problem. It is based on the recently published Multiobjective Dijkstra Algorithm (MDA) and equips it with A*‐like techniques. For any explored subpath, a label setting MOSP algorithm decides whether the subpath can be discarded or must be stored as part of the output. A major design choice is how to store subpaths from the moment they are first explored until the mentioned final decision can be made. The T‐MDA combines the polynomially bounded size of the priority queue used in the MDA and a lazy management of paths that are not in the queue. The running time bounds from the MDA remain valid. In practice, the T‐MDA outperforms known algorithms from the literature and the increased memory consumption is negligible. In this paper, we benchmark the T‐MDA against an improved version of the state of the art One‐to‐One MOSP algorithm from the literature on a standard testbed.

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An Improved Multiobjective Shortest Path Algorithm

Cited by 26 publications

References 21 publications

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

An FPTAS for Dynamic Multiobjective Shortest Path Problems

Targeted multiobjective Dijkstra algorithm

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