Monitoring Access to User Defined Areas with Multi-Agent Team in Urban Environments

We present an algorithm that determines where the members of a multi-agent team or swarm should be deployed in order to efficiently monitor access to a user specified region of interest. Our algorithm attempts to minimize the number of agents required to guarantee that any incursion into the region of interest is detected. The algorithm works by analyzing the geometric structure of the environment, and placing agents at advantageous positions in the environment, such as bottlenecks, to create a defensive perimeter of agents alongside physical obstacles (e.g. buildings). We demonstrate the usefulness of the algorithm through experimental simulations in an urban environment, and show how the min-cuts subroutine (used to reduce the number of agents required) can be implemented in a distributed way across the multi-agent team to enable better solutions to be found more quickly.

[1]  Dong Sun,et al.  Cooperation Strategy of Unmanned Air Vehicles for Multitarget Interception , 2005 .

[2]  Mechthild Stoer,et al.  A simple min-cut algorithm , 1997, JACM.

[3]  Yunfeng Zhang,et al.  Mission planning of autonomous UAVs for urban surveillance with evolutionary algorithms , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[4]  Emilio Frazzoli,et al.  Fast Collision Checking: From Single Robots to Multi-Robot Teams , 2013, ArXiv.

[5]  Lali Barrière,et al.  Capture of an intruder by mobile agents , 2002, SPAA '02.

[6]  David R. Karger,et al.  A new approach to the minimum cut problem , 1996, JACM.

[7]  Scott A. Bortoff,et al.  Path planning for UAVs , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[8]  Liang Cheng,et al.  Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data , 2014 .

[9]  Jens Wawerla,et al.  Fractal trajectories for online non-uniform aerial coverage , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Antonis A. Argyros,et al.  Fast positioning of limited-visibility guards for the inspection of 2D workspaces , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Yaohong Qu,et al.  Multi-UAV Cooperative Positioning Based on Delaunay Triangulation , 2010, 2010 International Conference on Computational Aspects of Social Networks.

[12]  Tae-Yong Kuc,et al.  A search and coverage algorithm for mobile robot , 2011, 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[13]  David R. Karger,et al.  An Õ(n2) algorithm for minimum cuts , 1993, STOC.

[14]  Ran Dai,et al.  Path Planning and State Estimation for Unmanned Aerial Vehicles in Hostile Environments , 2010 .

[15]  Nikhil Kamalkumar Advani.  Decentralized Control of an Energy Constrained Heterogeneous Swarm for Persistent Surveillance , 2017 .

[16]  Francesco Bullo,et al.  Continuous graph partitioning for camera network surveillance , 2012, Autom..

[17]  Albert Solé Ribalta,et al.  Voronoi-based space partitioning for coordinated multi-robot exploration , 2007 .

[18]  David A. Anisi,et al.  Minimum Time Multi-UGV Surveillance , 2009 .

[19]  Jun Ota,et al.  Multiple mobile robot surveillance in unknown environments , 2007, Adv. Robotics.

[20]  Stefano Carpin,et al.  The GRAPH-CLEAR problem: definition, theoretical properties and its connections to multirobot aided surveillance , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Petter Ögren,et al.  Demonstration of multi-robot search and secure , 2010, ICRA 2010.

[22]  Geoffrey A. Hollinger,et al.  Search and pursuit-evasion in mobile robotics , 2011, Auton. Robots.

[23]  Michal Pechoucek,et al.  Autonomous UAV Surveillance in Complex Urban Environments , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[24]  C. Gold,et al.  Delete and insert operations in Voronoi/Delaunay methods and applications , 2003 .

[25]  Daniel Pack,et al.  Maximizing Search Coverage Using Future Path Projection for Cooperative Multiple UAVs with Limited Communication Ranges , 2009 .