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001-es BibID:	BIBFORM123054
Első szerző:	Neamah, Husam A. (mérnök)
Cím:	Optimizing Autonomous Navigation In Unknown Environments : A Novel Trap Avoiding Vector Field Histogram algorithm VFH+T / Husam A. Neamah, Elek Donát, Peter Korondi
Dátum:	2024
ISSN:	2590-1230
Megjegyzések:	The Vector Field Histogram Plus (VFH+) algorithm is a cornerstone in robotic navigation, renowned for its efficiency and straightforward implementation across a multitude of environments. Despite its widespread utility, the algorithm's inherent limitations in handling complex obstacle entrapments necessitate refinement. This paper presents an advanced iteration, designated as VFH+T, which incorporates sophisticated memory-based trap recognition and avoidance mechanisms. This enhancement facilitates dynamic adjustment of navigation strategies through the integration of geometrical rules that retrospectively inform path planning decisions. Moreover, the VFH+T algorithm intricately melds the robotic platform's kinematic and dynamic constraints, optimising real-time navigational commands based on both current sensory input and historical environmental interactions. Empirical simulations validate the enhanced algorithm`s proficiency in circumventing navigational traps, improving operational safety and efficiency. Comparative analysis with VFH+ and VFH* algorithms show up to 17% reduction in traveling distance due to the trap-avoidance technique during navigation. This advancement holds significant implications for enhancing autonomous navigation technologies in various practical applications, from self-driving vehicles to robotic aids in logistics and service industries.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Autonomous Systems Navigation Gap following Obstacle Avoidance and Mobile Robot
Megjelenés:	Results in Engineering. - 23 (2024), p. 1-29. -
További szerzők:	Donát Elek Korondi Péter (1960-) (villamosmérnök)
Pályázati támogatás:	OTKA K143595 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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