  This paper presents a method for robotic monitoring missions in the presence
of moving obstacles. Although the scenario map is known, the robot lacks
information about the movement of dynamic obstacles during the monitoring
mission. Numerous local planners have been developed in recent years for
navigating highly dynamic environments. However, the absence of a global
planner for these environments can result in unavoidable collisions or the
inability to successfully complete missions in densely populated areas, such as
a scenario monitoring in our case. This work addresses the development and
evaluation of a global planner, $MADA$ (Monitoring Avoiding Dynamic Areas),
aimed at enhancing the deployment of robots in such challenging conditions. The
robot plans and executes the mission using the proposed two-step approach. The
first step involves selecting the observation goal based on the environment's
distribution and estimated monitoring costs. In the second step, the robot
identifies areas with moving obstacles and obtains paths avoiding densely
occupied dynamic regions based on their occupation. Quantitative and
qualitative results based on simulations and on real-world experimentation,
confirm that the proposed method allows the robot to effectively monitor most
of the environment while avoiding densely occupied dynamic areas.
