A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

Tao, Yong; He, Gaohong; Ren, Fan; Chen, Chaoyong; Wang, Tianmiao; Xiong, Hegen; Jiang, Shan

doi:10.3390/app11083605

Cited by 52 publications

(19 citation statements)

References 33 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzy rules are the most significant part of the fuzzy controller, which have a great influence on the output [ 31 , 32 ]. The rule base of the fuzzy controller is based on the operator’s knowledge and experiences.…”

Section: Methodsmentioning

confidence: 99%

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

et al. 2022

View full text Add to dashboard Cite

During the radial–axial ring rolling (RARR) process of super-large rings, abnormal deformation states such as instability and out of circularity often lead to rolling termination and quality fluctuation of ring products. In this work, an intelligent fuzzy closed-loop control method for RARR process of super-large rings is proposed, i.e., the ring’s offset adaptive fuzzy control (ROAFC) based on the regulation of the axial roll’s rotational speed and the ring’s circularity fuzzy control (RCFC) based on the regulation of the mandrel’s feed speed. In addition, a recursive average filtering algorithm is added to smooth the axial roll’s rotational speed and the mandrel’s feed speed according to the actual situation. Using the ABAQUS/Explicit software and its subroutine VUAMP, the intelligent fuzzy controller of the ring’s offset and circularity in the RARR process is designed, and the finite element (FE) model for RARR process of a Φ10 m super-large ring with an integrated intelligent fuzzy control algorithm is established. The variation laws of the ring’s offset and circularity error in the RARR process are studied with regard to different control methods such as conventional planning control (CPC), ROAFC, RCFC, and comprehensive control of ROAFC combined with RCFC (ROAFC + RCFC). The results obtained show that, compared with the CPC, the ring’s offset is reduced by 84.6% and the circularity error is decreased by 51.9% in the RARR process utilizing comprehensive control of ROAFC + RCFC. The research results provide methodological guidance for realizing the intelligent forming of super-large rings.

show abstract

Section: Methodsmentioning

confidence: 99%

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Reconstructing the environment grid map determines the obstacle value of each grid. The grid's environmental obstacle value is calculated, according to Equation (1). If the grid's environmental obstacle value is not 0, it becomes the sum of this grid and adjacent grids.…”

Section: Grid Map Optimization Based On the Rules Of The Chess Lively...mentioning

confidence: 99%

“…The innovation and optimization of artificial intelligence technology promotes the gradual development of mobile robots in the direction of automation and intelligence [1]. At the same time, with the demand of humans, mobile robots will move from the laboratory environment to outdoor environment.…”

Section: Introductionmentioning

confidence: 99%

Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

Zhuang

Dong

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

show abstract

“…Wall-climbing robots, as a kind of specialized robot, now play an increasingly important role in many special scenarios that can hardly be accomplished by humans, such as hull cleaning, bridge inspection, and wall cleaning. Path planning, an important branch in mobile robot research, aims to construct a collisionless optimal path from the starting point to the target point [2]. Path planning mainly includes global planning and local planning.…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Tao

Wen

et al. 2022

Electronics

Self Cite

View full text Add to dashboard Cite

A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.

show abstract

A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

Cited by 52 publications

References 33 publications

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Contact Info

Product

Resources

About