Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing

Kiadi, Morteza; Villar, José R.; Tan, Qing

doi:10.1007/978-3-030-57802-2_56

Cited by 5 publications

(3 citation statements)

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“…As a result, A* tends to deliver superior performance in scenarios involving expansive environments. Several works utilized the A* algorithm in finding the optimal path for multi-robot systems [35][36][37][38]. The authors in [35] used the A* path planning technique in order to find the shortest path in an environment with static obstacles only.…”

Section: Cell Decomposition Methodsmentioning

confidence: 99%

“…However, classical path planning approaches continue to be relevant and serve as a valuable foundation for modern path planning techniques. A variety of deterministic path planning techniques have been applied, including cell decomposition techniques [35][36][37][38][39][40][41], artificial potential field techniques [46][47][48], and sampling-based techniques [44,45]. The subsequent sections will explore the three subdivisions of traditional methods.…”

Section: Deterministic Approachesmentioning

confidence: 99%

“…UGV [36,37,39], [41,45,57], [62,69,71], [47,59,61], [66,67,70], [73] Centralized Humanoid [64,72] -Single point of failure.…”

Section: Communication Style Robot Type Papers Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

AbuJabal,

Rabie,

Baziyad

et al. 2024

Electronics

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A vast amount of research has been conducted on path planning over recent decades, driven by the complexity of achieving optimal solutions. This paper reviews multi-robot path planning approaches and presents the path planning algorithms for various types of robots. Multi-robot path planning approaches have been classified as deterministic approaches, artificial intelligence (AI)-based approaches, and hybrid approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. However, multi-robot systems suffer from well-known problems such as the number of robots in the system, energy efficiency, fault tolerance and robustness, and dynamic targets. Deploying systems with multiple interacting robots offers numerous advantages. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot systems.

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