As a core carrier of urban underground infrastructure, underground utility tunnels have an increasingly urgent demand for AAV swarm technology in operation and maintenance tasks such as internal pipeline inspection and fault diagnosis. However, the special environment of utility tunnels, characterized by GPS signal loss, narrow and enclosed space, and complex types of obstacles (including fixed pipeline supports and temporary construction components), poses severe challenges to AAV swarm operations. In this scenario, while maintaining the predetermined formation configuration, AAV swarms are required to complete three tasks simultaneously: accurately avoiding complex static obstacles in the tunnels, real-time evading other AAVs within the swarm to prevent intra-formation collisions, and strictly maintaining a safe distance from the tunnel walls. The superposition of these three constraints and the narrow operating space greatly increases the difficulty of swarm control. Traditional control methods often suffer from problems such as formation collapse, jerky obstacle avoidance, or wall scraping. To address the above pain points, this paper proposes an AAV swarm Model Predictive Control (MPC) method integrated with the Adaptive Perceptual Cauchy Mutation Pigeon-Inspired Optimization (APCMPIO) algorithm. This method optimizes the receding horizon parameters and constraint weights of model predictive control through the APCMPIO algorithm to achieve rapid response to complex dynamic scenarios, and embeds a dynamic safe distance threshold mechanism to balance formation integrity and individual safety. A simulation experiment was built for a utility tunnel inspection scenario with 6 AAVs. It significantly improves the operational adaptability and robustness of AAV swarms in constrained utility tunnel environments, providing reliable technical support for the swarm-based operation and maintenance of underground infrastructure.