Multi-robot Area Coverage Research Articles

A Multirobot Cooperative Area Coverage Search Algorithm Based on Bioinspired Neural Network in Unknown Environments

This article proposes a novel approach based on a bioinspired neural network (BIN) for multirobot area coverage search in unknown environments. We obtain the dynamic environment information in the search process of the multirobot by combining the BIN model with the grid map. We, then, design a search objective function to guide robots to explore the task area by applying the distributed model prediction control policy. Moreover, we further improve the search efficiency through a collaborative decision-making mechanism among robots. Finally, we conduct simulation studies, which demonstrate the effectiveness and superiority of the proposed approach.

Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

Editorial: Annals of Mathematics and Artificial Intelligence special issue on multi-robot coverage, search, and exploration

Research in multi robot area coverage, search, and exploration has been receiving consistent attention in recent years, due to the increasing number of real-world applications, such as vacuuming, lawn mowing, demining, surveillance, search and rescue operations, mapping, planetary exploration, etc. All of these applications require that the area of interest be covered by the robots sensors or end-effectors for various purposes. The use of multiple robots potentially provides redundancy and offers opportunities for increasing efficiency. The problem of multi robot area coverage imposes great challenges to researchers in robotics and AI area. This special issue explores the new research frontiers that emerge as new applications are identified and new technologies in robots are introduced. This special issue follows in the footsteps of the highly successful 2001 special issue on coverage. However, the objective of this issue has been broaden to include the adjacent and related fields of multi-robot search and multi-robot exploration. This special issue contains nine research papers that were carefully selected to be both of high quality and to deal with a wide verity of problems related to multi-robot coverage, search, and exploration. The first paper by Rekleitis et al. concerns with the multi-robot coverage problem. The paper presents an algorithm that solves the problem online by sending scout robots to explore the area. The second paper by Agmon, Hazon, and Kaminka deals with the same problem, but here the authors took a different approach by circumnavigating a spanning tree. The third paper by Sarid and Shapiro analyze the competitive complexity of a multi-robot search