An efficient algorithm for environmental coverage with multiple robots

Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or searching the space for an object. In many cases, the use of multiple robots can greatly improve the performance of these tasks. We assume a prior map is available, but it may be inaccurate due to factors such as occlusion, age, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of environmental coverage with incomplete prior map information using k robots. To utilize related algorithms in graph theory, we represent the environment as a graph and model the coverage problem as a k-Rural Postman Problem. Using this representation, we present a graph coverage approach for plan generation that can handle graph changes online. Our approach proposes two improvements to an existing heuristic algorithm for the coverage problem. Our improvements seek to equalize the length of the k paths by minimizing the length of the maximum tour. We evaluate our approach on a set of comparison tests in simulation.

Authors

• Ling Xu
• Anthony Stentz

Keywords

• Clustering algorithms
• Heuristic algorithms
• Roads
• Robot sensing systems
• Image edge detection
• Runtime
• Multiple Robots
• Graph Theory
• Heuristic Algorithm
• Coverage Problem
• Priority Map
• Computation Time
• Centroid
• Shortest Path
• Road Network
• Nodes In The Graph
• Dynamic Test
• Original Algorithm
• Static Test
• Graph Size
• Start Location
• Cost Path
• Coverage Path