A Fast On-line Global Path Planning Algorithm Based on Regionalized Roadmap for Robot Navigation

Zhong, Chaoliang; Liu, Shirong; Zhang, Botao; Lu, Qiang; Wang, Jian; Wu, Qiuxuan; Gao, Farong

doi:10.1016/j.ifacol.2017.08.053

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…To the authors' knowledge, a method of path planning across a decentralised system we have defined does not exist. Similar work has been done for centralised systems in [8,9]. Therefore, we propose a modification to standard A*.…”

Section: Path Planningmentioning

confidence: 88%

“…Overcoming this downfall with A* can be combatted in several ways with many simply focusing on reducing the size of the graph on which it searches for paths. Zhong et al [8] used a modified A* path planning algorithm that segments the working region into a grid to path from the starting local region (fine) to the upper nodes (coarse) to reduce the time to calculate a path. The inverse approach was proposed in [9].…”

Section: Related Workmentioning

confidence: 99%

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Decentralised indoor smart camera mapping and hierarchical navigation for autonomous ground vehicles

Whitaker

Cunningham

Bobda

2020

IET Computer Vision

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In this work, the authors propose a novel decentralised coordination scheme for autonomous ground vehicles to enable map building and path planning with a network of smart overhead cameras. Decentralised indoor smart camera mapping and hierarchical navigation supports the automatic generation of waypoint graphs for each camera in an environment and allows path planning through the environment across multiple camera fields of view, or subviews. The proposed solution utilises the growing neural gas algorithm to learn the topology of unoccupied working space in each subview for maintaining a dynamic waypoint graph on each camera. The authors’ pathing solution leverages a modified version of the A* algorithm to compute paths in a decentralised and hierarchical fashion. Waypoint generation was simulated and analysed on a generated environment to ensure it is both effective and efficient, while path planning was simulated on various randomised hierarchical graphs to effectively compare the proposed Decentralised‐A* (D‐A*) algorithm against standard greedy search. The proposed method efficiently handles the cases where other robot navigation methods are otherwise weak and ineffective, while still providing avenues for further optimisation of resource overhead for both the smart camera network as well as the robots themselves.

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Section: Path Planningmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 99%

Decentralised indoor smart camera mapping and hierarchical navigation for autonomous ground vehicles

Whitaker

Cunningham

Bobda

2020

IET Computer Vision

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“…Navigation research dates back to the 1960s, and several techniques, including cell decomposition [11], roadmap techniques [12], and the potential field [13], have been proposed. The major weaknesses of these techniques are their ineffectiveness because of high operational costs and their inexactness as a result of the increased risk of being caught in a local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Ketafa,

Al-Darraji

2024

Iraqi Journal of Science

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Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper, present a thre Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper presents a three-stage multi-objective path-planning method. The first stage is to locate the best or near-best solution path and avoid detected obstacles using a hybrid of the red fox–gray wolf optimizer (RFO–GWO), which finds a route from the start position to the target position. In the second step, a mutation operation using an evolutionary algorithm is utilized to enhance the length, integrity, and smoothness of the route generated by the RFO–GWO algorithm. The final step of the suggested method is refined further using a multiphase technique. By integrating the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path-planning method resembles the actual world. The simulation results indicate that this strategy creates the most viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when compared to prior path-planning methods, the simulation’s outcomes indicate that the suggested RFO–GWO method is effective in terms of the route, and the strategy is extremely competitive. The results showed a significant improvement, where the total percentage convergence time (in seconds) for RFO–GWO for the three maps was 15%, 12%, and 10%, respectively, whereas it was 35%, 41%, and 43% seconds in GWO and 34%, 35%, and 37% seconds in RFO. There was also a significant improvement in the number of nodes for RFO-GWO (2%, 3%, and 2%) compared to GWO nodes (64%, 65%, and 62%), and RFO nodes (32%, 30%, and 35%) for the same three maps. Subsequently, the smoothness of the path formed by the recommended approach was enhanced using the evolutionary algorithm (EA), where the total percentage length of the path in the worst scenario for GWO was 28% and for RFO was 26% in units, but after improvement with the RFO-GWO with EA, it became 22% in units. stages multi-objective path planning method: The first stage is to locate the best or near-best solution path and avoid the detected obstacles using a hybrid of the Red Fox-Grey Wolf optimizer (RFO-GWO) method, which finds a rout from the start position to the target position. In the second step, a mutation operation by evolutionary, are utilized to enhance the length, integrity, and smooth of the rout generated by the RFO-GWO method. the final step the suggested method is refined further by using the multiphase technique. By integrating both the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path planning method resembles the actual world. The simulation results indicate that this strategy creates the best viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when contrasted with prior path-planning methods, simulation outcomes indicate that the suggested RFO-GWO in terms of route effectivity, the strategy is extremely competitive.

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Employing Natural Terrain Semantics in Motion Planning for a Multi-Legged Robot

Belter

Wietrzykowski

Skrzypczyński

2018

J Intell Robot Syst

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A Fast On-line Global Path Planning Algorithm Based on Regionalized Roadmap for Robot Navigation

Cited by 6 publications

References 9 publications

Decentralised indoor smart camera mapping and hierarchical navigation for autonomous ground vehicles

Decentralised indoor smart camera mapping and hierarchical navigation for autonomous ground vehicles

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Employing Natural Terrain Semantics in Motion Planning for a Multi-Legged Robot

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