Autonomous Multi-Robot Collaboration in Virtual Environments to Perform Tasks in Industry 4.0

In Industry 4.0, multi-robot efficient coordination is a critical enabler for many commercial and industrial automation activities. This paper describes how a group of robots may dynamically produce actions from a common. We focus on examining the performance of autonomous multi-robot collaboration effectively and efficiently to achieve a common task and plan for the action of future task allocation dynamically, autonomously, and in a decentralized fashion. The common goal for multi-robots here is to bring the ball from the current position to predefine the desk target. Unreal Engine (UE4) is used for designing and implementing the platform of robots. The results show significant achievement in accurate trajectories for the ball taken via multi-robot collaboration in an autonomous and decentralized fashion. As a result, multi-robot collaboration can be available for numerous applications in Industry 4.0, warehouse automation, transport, risky environments, search and rescue (SAR) teams, and disaster recovery.

[1]  S. Alsamhi,et al.  Blockchain-Empowered Security and Energy Efficiency of Drone Swarm Consensus for Environment Exploration , 2023, IEEE Transactions on Green Communications and Networking.

[2]  Faris. A. Almalki,et al.  Drones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy , 2022, IEEE Transactions on Green Communications and Networking.

[3]  V. Ranga,et al.  Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope , 2021, Journal of Intelligent & Robotic Systems.

[4]  Neeraj Kumar,et al.  Blockchain for decentralized multi‐drone to combat COVID‐19 and future pandemics: Framework and proposed solutions , 2021, Trans. Emerg. Telecommun. Technol..

[5]  Sachin Kumar Gupta,et al.  A survey on recent optimal techniques for securing unmanned aerial vehicles applications , 2020, Trans. Emerg. Telecommun. Technol..

[6]  Jingchuan Wang,et al.  Coupled task scheduling for heterogeneous multi-robot system of two robot types performing complex-schedule order fulfillment tasks , 2020, Robotics Auton. Syst..

[7]  Ou Ma,et al.  Comparison Between Genetic Fuzzy Methodology and Q-learning for Collaborative Control Design , 2020, International Journal of Artificial Intelligence & Applications.

[8]  Lin Zhang,et al.  Multi-robot Cooperative Object Transportation using Decentralized Deep Reinforcement Learning , 2020, ArXiv.

[9]  S. H. Alsamhi,et al.  Convergence of Machine Learning and Robotics Communication in Collaborative Assembly: Mobility, Connectivity and Future Perspectives , 2020, J. Intell. Robotic Syst..

[10]  Shlomi Hacohen,et al.  Robotic Swarm Motion Planning for Load Carrying and Manipulating , 2020, IEEE Access.

[11]  Ou Ma,et al.  An Intelligent Approach for a Two-robot Team to Perform a Cooperative Task , 2020 .

[12]  Ou Ma,et al.  Survey on artificial intelligence based techniques for emerging robotic communication , 2019, Telecommun. Syst..

[13]  Kelly Cohen,et al.  Intelligent Approach for Collaborative Space Robot Systems , 2018, 2018 AIAA SPACE and Astronautics Forum and Exposition.

[14]  Charalampos P. Bechlioulis,et al.  Collaborative Multi-Robot Transportation in Obstacle-Cluttered Environments via Implicit Communication , 2018, Front. Robot. AI.

[15]  Javier Alonso-Mora,et al.  Distributed multi-robot formation control in dynamic environments , 2018, Auton. Robots.

[16]  Reinaldo A. C. Bianchi,et al.  Humanoid Robot Framework for Research on Cognitive Robotics , 2018, Journal of Control, Automation and Electrical Systems.

[17]  Elio Tuci,et al.  Cooperative Object Transport in Multi-Robot Systems: A Review of the State-of-the-Art , 2018, Front. Robot. AI.

[18]  Dimos V. Dimarogonas,et al.  Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments , 2017, IEEE Transactions on Control Systems Technology.

[19]  Dimos V. Dimarogonas,et al.  A Nonlinear Model Predictive Control scheme for cooperative manipulation with singularity and collision avoidance , 2017, 2017 25th Mediterranean Conference on Control and Automation (MED).

[20]  Enrico Pagello,et al.  Advanced approaches for multi-robot coordination in logistic scenarios , 2017, Robotics Auton. Syst..

[21]  Antonios Gasteratos,et al.  Robot navigation in large-scale social maps: An action recognition approach , 2016, Expert Syst. Appl..

[22]  Spring Berman,et al.  Decentralized sliding mode control for autonomous collective transport by multi-robot systems , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[23]  Vicente Parra-Vega,et al.  Cooperative redundant omnidirectional mobile manipulators: Model-free decentralized integral sliding modes and passive velocity fields , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Lucas Beyer,et al.  The STRANDS Project: Long-Term Autonomy in Everyday Environments , 2016, IEEE Robotics Autom. Mag..

[25]  Boris Otto,et al.  Design Principles for Industrie 4.0 Scenarios , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[26]  P. Fettke,et al.  Industry 4.0 , 2014, Bus. Inf. Syst. Eng..

[27]  Guoqiang Hu,et al.  Cloud robotics: architecture, challenges and applications , 2012, IEEE Network.

[28]  Julie A. McCann,et al.  A survey of autonomic computing—degrees, models, and applications , 2008, CSUR.

[29]  Raffaello D'Andrea,et al.  Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses , 2007, AI Mag..

[30]  S. Alsamhi,et al.  Blockchain for Multi-Robot Collaboration to Combat COVID-19 and Future Pandemics , 2020, ArXiv.

[31]  Ou Ma,et al.  Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning , 2020, IEEE Access.