VFH Obstacle Avoidance Algorithm Code Implementation

The Vector Field Histogram (VFH) algorithm is a classic obstacle avoidance navigation method for robotics, particularly suitable for real-time obstacle avoidance scenarios in autonomous guided vehicles. This algorithm constructs an environmental histogram by analyzing sensor data and generates safe motion vectors by combining target direction information, ultimately guiding the robot to navigate around obstacles and reach its destination.

The core algorithm logic can be divided into three key steps: First, it utilizes sensors (such as LiDAR or ultrasonic sensors) to acquire surrounding obstacle information and converts this data into a polar coordinate histogram; Next, it identifies feasible regions through threshold processing, eliminating dangerous directions; Finally, it selects the path closest to the target direction from candidate directions while considering smoothness to avoid sharp turns.

In MATLAB implementation, the code typically simulates sensor data input and represents obstacle distribution using 2D arrays. The algorithm dynamically calculates peaks and valleys in the histogram, incorporating robot physical constraints (like minimum turning radius) to generate control commands. This implementation approach is beginner-friendly, allowing users to both understand underlying mathematical principles and quickly validate obstacle avoidance performance.

VFH's advantage lies in its high computational efficiency, making it suitable for real-time systems. However, parameter tuning requires attention - for instance, settings like histogram grid size and obstacle inflation coefficient directly affect the aggressiveness of obstacle avoidance. Advanced improvements may include integrating dynamic obstacle prediction or multi-sensor data fusion to enhance robustness. From a coding perspective, key functions would involve histogram generation, threshold filtering, and direction selection algorithms with smoothing constraints.