Optimizing autonomous navigation in unknown environments: A novel trap avoiding vector field histogram algorithm VFH+T

摘要

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, optimizing 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.