Probabilistic VFH-based Obstacle Avoidance Algorithm for Unknown Environment Exploration using Swarm Robots

Abstract

This paper presents a novel probabilistic Vector Field Histogram (p-VFH) obstacle avoidance algorithm for swarm robot exploration in unknown environments. Conventional path planning algorithms, such as A* and RRT*, are not suitable for unknown environments, and existing obstacle avoidance methods for swarm robots have limitations in terms of computational cost, sensor requirements, and local minima issues. The proposed p-VFH algorithm addresses these challenges by probabilistically selecting the robot’s movement direction based on a continuously updated polar histogram of obstacle densities. The algorithm initializes the histogram values, updates them when encountering unknown obstacles, and generates a probability distribution for selecting the next movement direction. Simulation studies compare the performance of p-VFH with a simple "Back step" obstacle avoidance method and a stress-based obstacle avoidance algorithm. The results demonstrate that p-VFH improves exploration efficiency and successfully guides robots to target points while reliably avoiding obstacles, outperforming the other methods in terms of success rate and adaptability to the environment.