Visibility-Based Persistent Monitoring of Piecewise Linear Features on a Terrain Using Multiple Aerial and Ground Robots

Persistent monitoring on terrains using mobile robotic sensors requires coordinated planning. Terrain features add visibility obstacles and limited fuel capacity of aerial robots leads to range restrictions that make the problem challenging. We address the visual-monitoring problem on piecewise linear features within a terrain using multiple mobile robots for persistent operations. The planner must account for visual coverage, refueling aerial robots during the mission, and placement of refueling depots while also utilizing the available sensor diversity to minimize overall costs for the monitoring mission. Building on previous works on visibility in specific classes of polygons and fuel-constrained routing, we develop a discrete representation of the problem that allows the design and application of discrete optimization techniques to find optimal solutions. We develop a mixed-integer linear programming (MILP) formulation and discuss a branch-and-cut implementation to compute exact solutions. We also develop a construction heuristic based on the idea of competitive construction of robot paths using a step-increment strategy. We report the results from computational simulations and illustrate proof of concept using experiments on real robots.
Note to Practitioners
—This article is motivated by the need to perform persistent monitoring in applications, such as border patrol and perimeter surveillance. Unmanned aerial and ground robots can be used to perform these activities uninterruptedly. However, aerial robots have limited fuel capacity and need periodic refueling. Hence, the number of refueling depots and their placement within the environment also affects the monitoring task. Also, due to terrain variation, robots are subject to limited visibility. Therefore, we need to consider refueling constraints and terrain visibility aspects while planning optimal routes for the robots to perform visual monitoring. In this article, we present a general optimal routing formulation to compute exact solutions. We also present a fast heuristic for real-time applications that produce feasible solutions. The algorithms are validated in simulations. We also show a proof of concept using experiments in limited outdoor settings.