Being trajectory tracking key for safe mobile robot navigation, Fuzzy Logic (FL) has been useful in tackling uncertainty and imprecision to realize robust and smooth trajectory tracking. In this paper, we present the Z-number based Fuzzy Logic control for trajectory tracking of differential wheeled mobile robots. The unique point of our approach lies in the ability to encode constraint and reliability in multi-input and multi-output rules, whose antecedent universe considers only the instantaneous measurements of distance and the orientation gaps, and whose consequent universe is computed by the interpolative reasoning and the graded mean integration approach. As a consequence, not only our approach avoids the complexity of encoding error gradients, but also is advantageous to model versatile control rules able to cope with missing observations and noisy inducements on actuators. Our experiments using both physics-based simulations and real-world tests based on a Pioneer 3DX robot architecture have elucidated the superior efficacy and the feasibility of the proposed controller regarding accuracy, robustness, and smoothness compared to other well-known related frameworks such as Fuzzy Logic Type 1, Fuzzy Logic Type 2 and Fuzzy Logic with PID. Our results provide unique insights to realize generalizable algorithms aided by FL and Z-number towards robust trajectory tracking.
In this paper, a proposed algorithm for surface reconstruction from uniform or non-uniform point sets is introduced. The points are typically acquired with multiple range scans of any 3D object. The proposed algorithm follows the advancing front paradigm to build the reconstructed surface employing a variable radius moving ball that expands and shrinks continuously based on the sampling density. Starting with a user-specified initial radius, this initial ball may touch three points without containing any other point forming a seed triangle. For any edge, another point is found to form a ball with minimum radius generating another triangle. The process continues until generating all possible edges. The algorithm is theoretically proved under certain sampling criteria on the input data set. The proposed algorithm was applied on different datasets and compared favorably with the most eminent techniques. The key issues for comparisons were the reconstructed surface quality, the memory usage and the execution time. The present algorithm bested others in treating non uniform samples, samples with sharp edges and samples with small holes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.