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It's a fuzzy path.
The fuzzy controller supports a fuzzy path tracking algorithm introduced by the authors.
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In order to improve the performance of the canonical decision tree, this paper proposes the soft binary decision tree structure which is able to establish several fuzzy paths from the root to multiple leaves.
The results and analysis indicate that the proposed two-stage programming with fuzzy shortest path surpasses the performance of shortest path problem with time windows and capacity constraint (SPPTWCC) in terms of less computational time and CPU memory consumption.
In the first, we utilize fuzzy shortest paths and K-shortest paths to generate preferred and absorbing paths, and then we find the flow on them by solving a classic MCMFP.
In addition, the fuzzy shortest path is developed to solve the problem complexity in terms of the multi-criteria of lead time and capacity with an efficient computational method.
This paper studies a fuzzy-supervised path tracking system designed and implemented for driving large unmanned vehicles at high speed.
In the proposed genetic-fuzzy approach, optimal path and gaits are generated by using fuzzy logic controllers (FLCs) and genetic algorithms (GAs) are used to find optimized FLCs.
In this paper, a Neuro-fuzzy and fuzzy probabilistic coordination and path planning for multiple mobile robots are presented.
Finally, numerical examples with quantitative comparison are provided to illustrate the performance of the nonlinear single-input fuzzy controller for 3D path following of an underactuated AUV exposed to unknown environmental disturbances.
First, a new technique is devised for the addition of various fuzzy numbers in a path using α-cuts by proposing a least squares model to obtain membership functions for the considered additions.
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