Energy- and Time-Aware Data Acquisition for Mobile Robots Using Mixed Cognition Particle Swarm Optimization

Xie, Mingshan; Bai, Yong; Huang, Mengxing; Deng, Yanfang; Hu, Zhuhua

doi:10.1109/jiot.2020.2991198

Cited by 9 publications

(13 citation statements)

References 33 publications

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“…The distribution function of information for the surveillance area is described by F(x, y). The quantity of information is given in Equation ( 6), for the entire surveillance area is denoted by I S [36].…”

Section: Path Planning For Surveillance Tasksmentioning

confidence: 99%

“…The method of fitting is to develop a function of mathematics that most accurately fits with a collection containing discrete points of information. The minimization problem is derived from the relationship between 𝐹(𝑥, 𝑦) 𝑎𝑛𝑑 𝜌 in Equation ( 5) [35,36]:…”

Section: Path Planning For Surveillance Tasksmentioning

confidence: 99%

“…The mathematical optimization for grids transforms the informational domain of the entire area, and the information related to the places is used for its representation. The quantity of data acquired by the MR is abbreviated in Equation ( 8) [36]:…”

Section: Path Planning For Surveillance Tasksmentioning

confidence: 99%

See 2 more Smart Citations

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Raj,

Kos

2023

Applied Sciences

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Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.

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Section: Path Planning For Surveillance Tasksmentioning

confidence: 99%

Section: Path Planning For Surveillance Tasksmentioning

confidence: 99%

See 1 more Smart Citation

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Raj,

Kos

2023

Applied Sciences

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“…Miao et al [ 19 ] proposed an improved adaptive ant colony algorithm, which balances the convergence problem through adaptive adjustment factors to obtain a more stable path. Xie et al [ 20 ] proposed a mixed cognition PSO, which calculated fitness through minimum-maximum normalization and transformed the multiobjective path optimization problem into a single-objective path optimization problem. Xiong et al [ 21 ] applied the improved A ∗ algorithm to intelligent vehicle trajectory planning, which improved the accuracy of path tracking.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Song

Yang

et al. 2021

Computational Intelligence and Neuroscience

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The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

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“…The constrained multi-objective optimal path problem has a wide range of applications in different engineering fields such as the Internet of Things [1] and transportation network [2]. In this regard, traditional network algorithms approximate the actual network to a static model, and the solution obtained is far from the actual optimal solution.…”

Section: Introductionmentioning

confidence: 99%

A store-and-forward neural network to solve multicriteria optimal path problem in time-dependent networks

Liu

Chen²,

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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This paper introduces the constrained multi-objective optimal path problem in time-dependent networks. In the existing literatures, the constraints are all imposed on the objective function while the problem constraints are related to the non-objective function. It is the difference that makes the traditional algorithm unable to get a better solution quality. In this light, we propose a store-and-forward neural network (SFNN) that finds the better result. In the design of SFNN, the topology of neural network is the same as that of time-varying network, and each node is designed as store-and-forward neuron. Each neuron transmits information to other neurons by sending signals. The experimental results show that compared with the traditional methods, the accuracy is significantly improved when the calculation time is acceptable.

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Energy- and Time-Aware Data Acquisition for Mobile Robots Using Mixed Cognition Particle Swarm Optimization

Cited by 9 publications

References 33 publications

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

A store-and-forward neural network to solve multicriteria optimal path problem in time-dependent networks

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