A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

Lei, Tingjun; Chintam, Pradeep; Luo, Chaomin; Liu, Lantao; Jan, Gene Eu

doi:10.3390/s23115103

Cited by 15 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [28] aimed to solve the slow convergence properties of fuzzy-base neural network algorithms by utilising a particle swarm optimisation training scheme to reduce computational effort. Through the use of neural networks, Yu et al [29] proposed a hybrid hierarchical reinforcement learning algorithm to solve the slow convergence rate of autonomous vehicle planned path and path planning algorithms [30]. 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.…”

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

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.

show abstract

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

show abstract

“…In this approach, the area is partitioned into 'n' clusters through AP, with each cluster subsequently assigned to a robot. A new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model by Lei [37].…”

Section: Introductionmentioning

confidence: 99%

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

View full text Add to dashboard Cite

The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.

show abstract

“…This not only reduces risks for operators but also significantly enhances task execution efficiency and precision. Autonomous robots offer a low-cost, non-contact disinfection method, minimizing human exposure risks [ 10 , 12 ]. The research community has explored integrating ultraviolet light with autonomous robots [ 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Coverage Planning for UVC Irradiation: Robot Surface Disinfection Based on Swarm Intelligence Algorithm

Guo,

Luo,

et al. 2024

Sensors

View full text Add to dashboard Cite

Ultraviolet (UV) radiation has been widely utilized as a disinfection strategy to effectively eliminate various pathogens. The disinfection task achieves complete coverage of object surfaces by planning the motion trajectory of autonomous mobile robots and the UVC irradiation strategy. This introduces an additional layer of complexity to path planning, as every point on the surface of the object must receive a certain dose of irradiation. Nevertheless, the considerable dosage required for virus inactivation often leads to substantial energy consumption and dose redundancy in disinfection tasks, presenting challenges for the implementation of robots in large-scale environments. Optimizing energy consumption of light sources has become a primary concern in disinfection planning, particularly in large-scale settings. Addressing the inefficiencies associated with dosage redundancy, this study proposes a dose coverage planning framework, utilizing MOPSO to solve the multi-objective optimization model for planning UVC dose coverage. Diverging from conventional path planning methodologies, our approach prioritizes the intrinsic characteristics of dose accumulation, integrating a UVC light efficiency factor to mitigate dose redundancy with the aim of reducing energy expenditure and enhancing the efficiency of robotic disinfection. Empirical trials conducted with autonomous disinfecting robots in real-world settings have corroborated the efficacy of this model in deactivating viruses.

show abstract

A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

Cited by 15 publications

References 49 publications

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

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

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Coverage Planning for UVC Irradiation: Robot Surface Disinfection Based on Swarm Intelligence Algorithm

Contact Info

Product

Resources

About