Adaptive Vector Field Histogram Plus (VFH+) Algorithm using Fuzzy Logic in Motion Planning for Quadcopter

Khitam Mohammed, Ali Aliedani, Alaa Al-Ibadi

Abstract


This work introduces the adaptive version of the vector field histogram plus (VFH+) motion planning algorithm, which is designed for unmanned aerial vehicles, particularly quadcopters, to enhance its performance in navigation tasks. The method suggests incorporating fuzzy control to adaptively modify the VFH+ look-ahead distance parameter by analysis continuous environmental and motion conditions. Simulation tests were completed using different scenarios that varied in obstacle quantity, density, distribution, and size and waypoint quantity. Simulation results showed the successful outcomes of this strategy in enhancing quadcopter motion performance in various contexts. The results indicated notable enhancements in obstacle avoidance, smoother motion trajectories, and decreased travel time compared to the traditional VFH+ method. One of the most important aspects of creating real-time motion planning systems is handling uncertainty. This is accomplished by incorporating a fuzzy system knowledge base for automatic algorithmic modification into the planning process and employing advanced motion-planning techniques. The adaptive algorithm improves the quadcopter's ability to deal with high uncertainty levels by incorporating fuzzy logic for dynamic parameter adjustment, allowing for accurate and efficient navigation in various environments, even in uncertain conditions.

Keywords


Quadcopter; Motion Planning; Fuzzy logic; Vector Field Histogram Plus VFH+ Algorithm; Parameters Tuning.

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References


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DOI: https://doi.org/10.18196/jrc.v5i2.21540

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