Occupation-aware planning method for robotic monitoring missions in dynamic environments

TL;DR

This paper introduces MADA, a global planning method for robotic monitoring in dynamic environments with moving obstacles, improving mission success and obstacle avoidance.

Contribution

The work develops and evaluates a global planner that effectively navigates dynamic environments, addressing limitations of local planners by incorporating obstacle occupation data.

Findings

Effective monitoring of environments with moving obstacles

Successful avoidance of densely occupied dynamic regions

Validated through simulations and real-world experiments

Abstract

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.