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

2024-08-01

Robotics Auton. Syst.

AI Summary

For robotic monitoring tasks under unknown dynamic obstacle motion patterns, this paper proposes MADA, an occupation-aware global planning framework that jointly models dynamic region occupancy and optimizes monitoring utility—its first such integration. The method employs a two-stage strategy: (1) target selection guided by environmental distribution and dynamic occupancy estimation, and (2) an enhanced A* path planner incorporating obstacle-avoidance robustness. Unlike conventional static planners, MADA explicitly models spatiotemporal occupancy intensity of dynamic regions, enabling safe and efficient full-coverage monitoring atop known static maps. Extensive simulations and real-world robot experiments demonstrate that MADA achieves a 23.6% improvement in monitoring coverage, reduces collision rate to 0.8%, and attains a 94.3% task completion rate in high-density dynamic scenarios—substantially outperforming baseline approaches.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Adaptive Planning
• Mobile Obstacles
• Collision Avoidance

Innovation

• MADA Global Planner
• Two-step Planning Approach
• Mobile Obstacle Avoidance