Optimal Multi-robot Path Finding Algorithm Based on A*

Erokhin, A. N.; Erokhin, V. I.; Sotnikov, Sergey; Gogolevsky, A.S.

doi:10.1007/978-3-030-00184-1_16

Cited by 5 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In Table 4, a metric evaluation of the RA-RRT* algorithm with the A*, RRT*, and Dijkstra algorithms regarding speed and accuracy is shown. It is found that the A* algorithm is more efficient in a directional and known environment, whereas the Dijkstra and RRT* algorithms are more feasible in an undirected environment (Erokhin et al, 2018). Based on the experimental results, the proposed RA-RRT* is proven to be more efficient due to the requirement to simultaneously search for the charging stations and the minimum optimal path in an undirected environment because of the tree structure.…”

Section: The Comparison Among Path Planning Algorithmsmentioning

confidence: 96%

Path planning for EVs based on RA-RRT* model

Muhammad

Zhou

2023

Front. Energy Res.

View full text Add to dashboard Cite

The optimal path planning for EVs (electric vehicles) has gained great attention during the last decade due to the zero pollution emission characteristics and limited power capacity of EV batteries. In this paper, an optimal route search is proposed considering multiple charging stations in a dynamic urban environment, while it is still applicable when the initial available amount of the battery fails to cover a certain travel range. The TRDP (transit route design problem) and TNDP (transit node design problem) are used to search for the most feasible routes based on time and driving range via the improved route-assisted rapid random tree (RA-RRT*) algorithm. Considering the status of charge of an EV’s battery during optimal routes search, three states are investigated between the destination and the aggregators: (1) bypassing the aggregators, (2) stopping over a single aggregator, and (3) stopping over multiple aggregators. During the states (2) and (3), it is required that the EVs be charged at the charging stations obtained by the RA-RRT* algorithm while approaching the destination. The proposed algorithm is tested on a random dataset under certain conditions, that is, traffic flow with congestion and assigned target locations from a given map data, with comparison experiments for efficacy verification.

show abstract

Section: The Comparison Among Path Planning Algorithmsmentioning

confidence: 96%

Path planning for EVs based on RA-RRT* model

Muhammad

Zhou

2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…However, the algorithm proposed in this paper can only be used in static environments and cannot be applied to dynamic scenes. In [58], an improved A* algorithm is proposed for coordinated path planning of a robotic swarm. This algorithm assigns dynamic values to each node, such that when a robot's path passes through a node, its assigned value changes dynamically.…”

Section: ) Search-based Multi-robot Path Planning Algorithmmentioning

confidence: 99%

Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

Gao

Bai

Quan

2023

IEEE Trans. Control Netw. Syst.

View full text Add to dashboard Cite

This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.

show abstract

“…The work Erokhin et al (2018) implemented an A*-based algorithm with modifications; to apply it to the resolution of the path planning problem in MRS. For this reason, in this work a dynamic calculation of the value of the costs associated with the network nodes is performed. As the path of a robot passes through a node, the value of that node changes, so that the rest of the robots consider that predefined path, and generate their path accordingly.…”

Section: A* Algorithmmentioning

confidence: 99%