This paper studies the cooperative hunting problem, where a group of agents encircle a target while avoiding collisions with each other and with obstacles in the environment. The paper deals with obstacle rich environments and dynamic (moving obstacle) environments by formulating the problem as both a control problem and a planning problem. A model predictive control (MPC) method is proposed which integrates a multi-agent planner with the cooperative hunting objective while also accounting for UAV dynamics. The effectiveness of the proposed method is verified through a comparative analysis with optimal reciprocal collision avoidance (ORCA), and then validated through experiments with quadrotor UAVs. Using the proposed method, agents no longer get stuck in local minima for obstacle rich environments and capture the target faster with shorter trajectories in moving obstacle environments.