Uncertainty representation for mobile robots: Perception, modeling and navigation in unknown environments
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Cited by (51)
Application of probability to enhance the performance of fuzzy based mobile robot navigation
2019, Applied Soft Computing JournalCitation Excerpt :In recent years, the FL have been used with different intelligent approaches to improve the performance of mobile robot when working in presence of dynamic uncertainty such as Neural Network [7,8], Ant Colony Algorithm [9], Genetic Algorithm [10–12], Particle Swarm Optimization [13], Potential Field Approach [14], Wind Driven Optimization [15], Tabu Search [16], Grey Wolf Optimization [17], Firefly Algorithm [18], Bee Colony Algorithm [19] and many more. Furthermore, the FL has been presented with some modification and up gradation to improve the navigational performance like Gasos et al. [20] have provided the improved approach to map building and self-localization in unknown environment; Yili et al. [21] have combined “virtual target” and “fuzzy logic” for local path planning and developed non-collision path planning using double layer fuzzy mechanism for controlling both speed and heading angle; Moustris et al. [22] have presented the switching FL controller with bounded curvature constraints for MRN; Ching [23] and Rubio et al. [24] has developed the vision guidance system based on the image processing with fuzzy to avoid obstacles while working in real environment; Mendonca et al. [25] have offered the cooperative architecture for swarm robotics based on reinforcement learning and dynamic fuzzy cognitive maps; and Petri net-fuzzy based model for self-navigation of robot have been developed and proposed by Kim et al. [26]. Numerous efforts have been provided by various researcher for solving the path planning problem over dynamic conditions and using FL alone is difficult task.
The maximal distance between imprecise point objects
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2010, International Journal of Approximate ReasoningEnabling flexibility in manufacturing by integrating shopfloor and process perception for mobile robot workers
2021, Applied Sciences (Switzerland)