Graph-based robot optimal path planning with bio-inspired algorithms

Lei, Tingjun; Sellers, Timothy; Luo, Chaomin; Carruth, Daniel W.; Bi, Zhuming

doi:10.1016/j.birob.2023.100119

Cited by 11 publications

(2 citation statements)

References 32 publications

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“…Sampling-based methodologies often require a significant amount of computational resources and time to converge to an optimal solution, making them less suitable for real-time or time-critical applications. On the other hand, graph-based methodologies are sensitive to changes in the graph structure or edge weights [31]. Minor modifications in the underlying graph can result in significantly different outcomes, making them less robust in dynamic or uncertain environments.…”

Section: Related Workmentioning

confidence: 99%

Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction

Lei,

Sellers,

Luo

et al. 2024

IET Cyber-Syst and Robotics

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The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation, with path planning emerging as a critical aspect. However, existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance. Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance, while neglecting road construction information, leading to potential time and energy inefficiencies in real‐world scenarios involving infrastructure development. To address this issue, a digital twin‐embedded multi‐objective autonomous vehicle navigation is proposed under the condition of infrastructure construction. The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extraction and modelling of the autonomous vehicle workspace. Additionally, a wavelet neural network is developed to predict real‐time traffic flow, considering its inherent characteristics. Moreover, a multi‐objective brainstorm optimisation (BSO)‐based method for path planning is introduced, which optimises total time‐cost and energy consumption objective functions. To ensure optimal trajectory planning during infrastructure construction, the algorithm incorporates a real‐time updated digital twin throughout autonomous vehicle operations. The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction. The results highlight the improved performance and reliability of the autonomous vehicle system when equipped with the authors’ approach, demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.

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Section: Related Workmentioning

confidence: 99%

Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction

Lei,

Sellers,

Luo

et al. 2024

IET Cyber-Syst and Robotics

Self Cite

View full text Add to dashboard Cite

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“…Heuristic and evolutionary algorithms are commonly employed to discover the best solution to this issue, particularly in extensive and complicated settings. One primary constraint in previous research is that many studies represent the context with discrete grids to determine the most effective grid configuration for determining the optimum path [15]. The primary limitation of this approach is the predetermined grid positions, which restrict path design flexibility.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

TANG,

ZHAO

2024

ijacsa

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Path planning is a prominent and essential part of mobile robot navigation in robotics. It allows robots to determine the optimal path from a given beginning point to a desired end goal. Additionally, it enables robots to navigate around obstacles, recognize secure pathways, and select the optimal route to follow, considering multiple aspects. The Whale Optimization Algorithm (WOA) is a frequently adopted approach to planning mobile robot paths. However, conventional WOA suffers from drawbacks such as a sluggish convergence rate, inefficiency, and local optimization traps. This study presents a novel methodology integrating WOA with Lévy flight and Differential Evolution (DE) to plan robot paths. As WOA evolves, the Levy flight promotes worldwide search capabilities. On the other hand, DE enhances WOA's ability to perform local searches and exploitation while also maintaining a variety of solutions to avoid getting stuck in local optima. The simulation results demonstrate that the proposed approach offers greater planning efficiency and enhanced route quality.

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