Motion planning in cluttered environments is challenging for multi-robot systems, in which each robot needs to avoid obstacles as well as other robots. This thesis presents a distributed risk-aware motion planning method for multi-robot systems in dynamic environments. For each robot navigating in a multi-robot scenario, two major risk elements are considered and formalized: a) the collision risk that is assessed using the defined "deformed distance to the centroid of free space" metric, and b) the congestion risk that is assessed via the designed "potential to goal" metric. These risk elements are incorporated into a distributed model predictive control (MPC) framework for risk-aware multi-robot motion planning, in which the collision and congestion risks of each robot are minimized. Simulation results show that the proposed method can improve the robot's safety regarding clearance to each other and obstacles comparing to the baseline method without risk minimization. Moreover, the trajectory efficiency, i.e., time to reaching goals, is also improved thanks to minimizing the congestion risk. We also validate the proposed method in real experiments with a team of Crazyflie 2.1 quadrotors.