Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Yuan, Qingni; Sun, Ruitong; Du, Xiaoying

doi:10.3390/pr11010026

Cited by 13 publications

(8 citation statements)

References 40 publications

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“…The parameter of inertia weight is very important in PSO. When the value of the inertia weight is large, PSO has better global search ability and faster convergence speed, but the solution is often not optimal; when the value of the inertia weight is small, PSO has better local search ability and can obtain better solutions, but the convergence speed becomes very slow or even stagnant [37]. In order for PSO to produce better solutions, faster convergence speed, stronger global search ability, and stronger local exploitation ability, a linear decreasing adjustment is used for PSO, which can give the algorithm stronger global search ability in the early search and stronger local exploitation ability in the late search, which can expand the search space and make the particle local search for better results.…”

Section: Particle Swarm Algorithm Based On Rankingmentioning

confidence: 99%

Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Zheng

Wang

et al. 2023

Sensors

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Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.

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Section: Particle Swarm Algorithm Based On Rankingmentioning

confidence: 99%

Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Zheng

Wang

et al. 2023

Sensors

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“…It might ultimately discover the suboptimal or optimal approach for an issue over multiple iterations [37]. The enhanced PSO, which is considered an intelligent PSO during this experiment, can be abbreviated in Equations ( 17) and ( 18) using Equations ( 1) and ( 2) [38].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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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“…Optimization problems occur frequently in various practical engineering problems [1]. Thus, it is crucial to solve optimization problems efficiently [2].…”

Section: Introductionmentioning

confidence: 99%

APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

Gao,

Yang

2024

Processes

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Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants.

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Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Cited by 13 publications

References 40 publications

Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

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

APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

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