<p>Flocking control is essential for studying the collective behavior of agents in multi-agent systems (MASs). Most existing flocking control algorithms assumed that the motion of the agent was influenced by consensus, gradient-based, and navigation feedback terms. However, the interaction of these terms might trap the agent in dynamic equilibrium state with near-zero acceleration and velocity under complex obstacles. This prevented the agent from bypassing the obstacles and tracking the group target. In this paper, we propose a vector field histogram plus (VFH+) flocking control algorithm. The algorithm maps the obstacle information into a simple binary polar histogram. Using this histogram, the expected velocity is calculated to construct the consensus term, which can guide the agent in escaping from the dynamic equilibrium state and bypassing obstacles. Then, a non-increasing piecewise function is formulated to dynamically alter the magnitude of the gradient-based term, thereby reducing the possibility of collisions. Additionally, the navigation feedback term is developed to track the group target. Stability analysis provides sufficient conditions that there are no collisions between agents or with obstacles. Subsequently, simulations conducted on combination, U-shaped and multiple obstacles demonstrate the effectiveness and environmental adaptability of the VFH+ flocking control algorithm.</p>