Intelligent Search and Find System for Robotic Platform Based on Smart Edge Computing Service

In recent years, artificial intelligence has been widely used in the field of robotics. However, these robot-related tasks are difficult to migrate from the cloud to edge nodes due to the large computing and storage resource requirements. In this project, we develop a platform for a heterogeneous robotic system. The platform is built to facilitate the development of advanced robotic applications with minimal human interactions, where search and find system traversal is the main application. To support robots performing tasks in near field in real time, in this paper we introduce traversal and task division algorithms which are introduced to perform a cooperative search mission by a group of robotic agents to achieve intelligent search and find as an edge service. We evaluate the performance against the algorithm’s parameters using data obtained in controlled field experiments. The aim was to identify and study some key performance parameters impacting the traversal function in the application.

[1]  Aníbal Ollero,et al.  Distributed Service-Based Cooperation in Aerial/Ground Robot Teams Applied to Fire Detection and Extinguishing Missions , 2010, Adv. Robotics.

[2]  Ahmed Barnawi An Advanced Search and Find System (ASAFS) on IoT-Based Mobile Autonomous Unmanned Vehicle Testbed (MAUVET) , 2020 .

[3]  Mac Schwager,et al.  Decentralized, Adaptive Coverage Control for Networked Robots , 2009, Int. J. Robotics Res..

[4]  Raffaello D'Andrea,et al.  Quadrocopter Trajectory Generation and Control , 2011 .

[5]  Han-Lim Choi,et al.  Consensus-Based Decentralized Auctions for Robust Task Allocation , 2009, IEEE Transactions on Robotics.

[6]  Marios M. Polycarpou,et al.  A cooperative search framework for distributed agents , 2001, Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206).

[7]  Hong Zhang,et al.  Cooperative Decision-Making in Decentralized Multiple-Robot Systems: The Best-of-N Problem , 2009, IEEE/ASME Transactions on Mechatronics.

[8]  Matthias Vigelius,et al.  Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics , 2014, PloS one.

[9]  J.P. How,et al.  Search for dynamic targets with uncertain probability maps , 2006, 2006 American Control Conference.

[10]  E. Fernandez-Gaucherand,et al.  Cooperative control for multiple autonomous UAV's searching for targets , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[11]  Rajnikant Sharma,et al.  Multi-UAV control testbed for persistent UAV presence: ROS GPS waypoint tracking package and centralized task allocation capability , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[12]  Aníbal Ollero,et al.  A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires , 2006, J. Field Robotics.

[13]  Anis Koubaa,et al.  DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet , 2018, IEEE Access.

[14]  Fuad Bajaber,et al.  A Proposed Architecture for a Heterogeneous Unmanned Aerial Vehicles System , 2018 .

[15]  J.K. Hedrick,et al.  The software architecture of the Berkeley UAV Platform , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[16]  Marc Pollefeys,et al.  PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision , 2012, Auton. Robots.

[17]  Min Chen,et al.  AI Agent in Software-Defined Network: Agent-Based Network Service Prediction and Wireless Resource Scheduling Optimization , 2020, IEEE Internet of Things Journal.

[18]  Spring Berman,et al.  Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination , 2011, 2011 IEEE International Conference on Robotics and Automation.

[19]  Yanli Yang,et al.  Decentralized cooperative search by networked UAVs in an uncertain environment , 2004, Proceedings of the 2004 American Control Conference.

[20]  Antony Galton,et al.  Efficient generation of simple polygons for characterizing the shape of a set of points in the plane , 2008, Pattern Recognit..

[21]  Sean Luke,et al.  MASON: A Multiagent Simulation Environment , 2005, Simul..

[22]  Aníbal Ollero,et al.  Multi-UAV ground control station for gliding aircraft , 2015, 2015 23rd Mediterranean Conference on Control and Automation (MED).

[23]  P. B. Sujit,et al.  Multiple UAV area decomposition and coverage , 2013, 2013 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).

[24]  Gaurav S. Sukhatme,et al.  Adaptive teams of autonomous aerial and ground robots for situational awareness , 2007, J. Field Robotics.

[25]  A. Ollero,et al.  Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms , 2004, DARS.

[26]  Aleksis Liekna,et al.  Multi-agent robotic system architecture for effective task allocation and management , 2012 .

[27]  Vladimir J. Lumelsky,et al.  Polygon Area Decomposition for Multiple-Robot Workspace Division , 1998, Int. J. Comput. Geom. Appl..

[28]  Kai Zhang,et al.  Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction , 2012, TAAS.

[29]  Eliseo Ferrante,et al.  Swarm robotics: a review from the swarm engineering perspective , 2013, Swarm Intelligence.

[30]  Onn Shehory,et al.  Agent-Oriented Software Engineering: Reflections on Architectures, Methodologies, Languages, and Frameworks , 2014 .

[31]  Francesco Mondada,et al.  Decentralized self-selection of swarm trajectories: from dynamical systems theory to robotic implementation , 2014, Swarm Intelligence.