Autonomous Vehicles and Automated Warehousing Systems for Industry 4.0

The rapid development of new technologies that enabled the emergence of important development segments such as the Internet of Things, Cyber Physical Systems, Information and Communication Technologies, Enterprise Architecture, and Enterprise Integration, have led to completely new manufacturing paradigms, which is called under the common name – Industry 4.0. The constantly growing use of autonomous vehicles and associated logistics solutions is among the most influential factors that foster this novel intelligent production framework. This paper describes the results of the latest research activities of the Laboratory for Robotics and Intelligent Control Systems in the Industry 4.0 domain where the focus lies on the shop floor digitalization and advanced control concepts that enable the transfer of technology and delivery of high-scalable logistic solutions

Autonomous Vehicles and Automated Warehousing Systems for Industry 4.0

The rapid development of new technologies that enabled the emergence of important development segments such as the Internet of Things, Cyber Physical Systems, Information and Communication Technologies, Enterprise Architecture, and Enterprise Integration, have led to completely new manufacturing paradigms, which is called under the common name – Industry 4.0. The constantly growing use of autonomous vehicles and associated logistics solutions is among the most influential factors that foster this novel intelligent production framework. This paper describes the results of the latest research activities of the Laboratory for Robotics and Intelligent Control Systems in the Industry 4.0 domain where the focus lies on the shop floor digitalization and advanced control concepts that enable the transfer of technology and delivery of high-scalable logistic solutions

Interacting with a multi AGV system

This paper introduces a novel Human Machine Interface (HMI) that allows users to interact with a fleet of Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. The interface is developed for providing operators with information regarding the fleet of AGVs, and the status of the industrial environment. Information is provided in an intuitive manner, utilizing a three-dimensional representation of the elements in the environment. The HMI also allows operators to influence the behavior of the fleet of AGVs, manually inserting missions to be accomplished

A Survey on Passing-through Control of Multi-Robot Systems in Cluttered Environments

This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.Comment: 18 pages, 19 figure

Optimization and Mathematical Modelling for Path Planning of Co-operative Intra-logistics Automated Vehicles

Small indoor Autonomous Vehicles have revolutionized the operation of pick-pack-and-ship warehouses. The challenges for path planning and co-operation in this domain stem from uncontrolled environments including workspaces shared with humans and human-operated vehicles. Solutions are needed which scale up to the largest existing sites with thousands of vehicles and beyond. These challenges might be familiar to anyone modelling road traffic control with the introduction of Autonomous Vehicles, but key differences in the level of decision autonomy lead to different approaches to conflict-resolution. This thesis proposes a decomposition of site-wide conflict-free motion planning into individual shortest paths though a roadmap representing the free space across the site, zone-based speed optimization to resolve conflicts in the vicinity of one intersection and individual path optimization for local obstacles. In numerical tests the individual path optimization based on clothoid basis functions created paths traversable by different vehicle configurations (steering rate limit, lateral acceleration limit and wheelbase) only by choosing an appropriate maximum longitudinal speed. Using two clothoid segments per convex region was sufficient to reach any goal, and the problem could be solved reliably and quickly with sequential quadratic programming due to the approximate graph method used to determine a good sequence of obstacle-free regions to the local goal. A design for zone-based intersection management, obtained by minimizing a linear objective subject to quadratic constraints was refined by the addition of a messaging interface compatible with the path adaptations based on clothoids. A new approximation of the differential constraints was evaluated in a multi-agent simulation of an elementary intersection layout. The proposed FIFO ordering heuristic converted the problem into a linear program. Interior point methods either found a solution quickly or showed that the problem was infeasible, unlike a quadratic constraint formulation with ordering flexibility. Subsequent tests on more complex multi-lane intersection geometries showed the quadratic constraint formulation converged to significantly better solutions than FIFO at the cost of longer and unpredictable search time. Both effects were magnified as the number of vehicles increased. To properly address site-wide conflict-free motion planning, it is essential that the local solutions are compatible with each other at the zone boundaries. The intersection management design was refined with new boundary constraints to ensure compatibility and smooth transitions without the need for a backup system. In numerical tests it was found that the additional boundary constraints were sufficient to ensure smooth transitions on an idealized map including two intersections

Multi-AGV transport of a load: state of art and centralized proposal

[EN] An automatic guided vehicle is a battery powered fully automated industrial transport system. These vehicles are widely used in the industrial sector to substitute manual forklifts and conveyors. The challenge of using AGVs as transport agents in industrial environments goes through providing them with enough intelligence to develop collaborative tasks. Among these collaborative tasks the multi-AGV transport of one object is differentiated from the multi-object multi-AGV transport. This work presents the state of art of cooperative transport solutions of one object between several AGVs. The theoretical fundaments are revised and several proposals for its resolution are classified and described. Finally, an own proposal of one-object multi-AGV transport with omnidirectional AGVs based on centralized remote control is presented.[ES] Un vehículo de guiado automático (Automatic Guided Vehicle –AGV-en inglés) es un sistema de transporte industrial completamente automatizado y alimentado por baterías. Estos vehículos son ampliamente utilizados en el sector industrial para sustituir a carretillas manuales y cintas transportadoras. El reto de la utilización de AGVs como agentes de transporte en entornos industriales pasa por dotarles de la inteligencia suficiente para desarrollar tareas colaborativas. Dentro de estas tareas colaborativas se diferencia el transporte multi-AGV de un objeto del transporte multi-AGV de múltiples objetos. Este trabajo presenta el estado del arte de las soluciones de transporte cooperativo de un objeto entre varios AGVs. Para ello, se revisan los fundamentos teóricos y se clasifican y describen varias propuestas para su resolución. Finalmente se propone una solución de control remoto centralizado para el transporte de una carga con AGVs omnidireccionales.Este trabajo ha sido apoyado parcialmente por la Junta de Castilla y León bajo el proyecto 10/16/BU/0014 y la empresa ASTI Mobile Robotics.Espinosa, F.; Santos, C.; Sierra-García, JE. (2020). Transporte multi-AGV de una carga: estado del arte y propuesta centralizada. Revista Iberoamericana de Automática e Informática industrial. 18(1):82-91. https://doi.org/10.4995/riai.2020.12846OJS8291181Adăscăliţei, F., and Doroftei, I. 2011. Practical Applications for Mobile Robots based on Mecanum Wheels - A Systematic Survey. The Romanian Review Precision Mechanics, Optics & Mechatronics, nº 40.Alonso-Mora, J., Barker, S. and Rus, D. 2017. Multi-robot formation control and object transport in dynamic environments via constrained optimization. The International Journal of Robotics Research. August 10. https://doi.org/10.1177/0278364917719333Adreasson, H., Bourguerra, A., Driankov, D. and Karlsson. L. 2015. Autonomous Transport Vehicles: Where We Are and What Is Missing. IEEE Robotics & Automation Magazine · March 2015. https://doi.org/10.1109/MRA.2014.2381357Amoozgar, M. and Zhang, Y. 2012. Trajectory tracking of wheeled mobile robots: A kinematical approach. Mechatronics and Embedded Systems and Applications (MESA), 2012 IEEE/ASME International Conference on, 2012, pp. 275- 280. https://doi.org/10.1109/MESA.2012.6275574Bahíllo, A. y otros, 2019. Libro blanco sobre espacios inteligentes y tecnologías de posicionamiento y navegación en entornos de interior. Editorial: Universidad de Alcalá. ISBN: 978-84-17729-47-9.Berman, S. and Edan Y. 2002. Decentralized autonomous AGV system for material handling. International Journal of Production Research 40(15):3995-4006 https://doi.org/10.1080/00207540210146990Borenstein, J., 1995. Control and Kinematic Design of Multi-Degree-ofFreedom Mobile Robots with Compliant Linkage. IEEE Trans. On Robotis and Automation. Vol. 1 I , nº I. https://doi.org/10.1109/70.345935Borenstein, J., 2000. The OmniMate: a guidewire and beacon-free AGV for highly reconfigurable applications. Int. Journal of Production Research. Vol. 38, nº 9, June 15, 2000. https://doi.org/10.1080/002075400188456Bostel, A.J. and Sagar, V,K. 1996. Dynamic control systems for AGVs. Engineering. https://doi.org/10.1049/cce:19960403Brown, R., and Jennings, J., 1995. A pusher/steerer model for strongly cooperative mobile robot manipulation. In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots 3, 562-568Butdee, S., Vignat, F., Suebsomran, A. and Yarlagadda, P.K. 2009. Estimation and control of an automated guided vehicle. International Journal of Mechatronics and Manufacturing Systems 2(3). https://doi.org/10.1504/IJMMS.2009.026053Cameron, S. and Probert, P. 1994. Advanced Guided Vehicles: Aspects of the Oxford AGV Project. ISBN: 981-02-1393-X. https://doi.org/10.1142/2022Chen, X. and Li, Y., 2006. "Cooperative Transportation by Multiple Mobile Manipulators Using Adaptive NN Control". In 2006 International Joint Conference on Neural Networks.Chiacchio P. and Chiaverini S., 1997. Complex Robotic Systems. Springer. https://doi.org/10.1007/BFb0035182Choi, S.K., Easterday, O.T. 2001. An Underwater Vehicle Monitoring System and Its Sensors. Lecture Notes in Control and Information Sciences. Experimental robotics. Springer-Verlag. Pp551-560. ISBN 3-540-42104-1. https://doi.org/10.1007/3-540-45118-8_55Digani, V., Sabattini, L., Secchi, C., Fantuzzi, C., 2014. Hierarchical Traffic Control for Partially De-centralized Coordination of Multi AGV Systems in Industrial Environments. IEEE Inter-national Conference on Robotics & Automation (ICRA). https://doi.org/10.1109/ICRA.2014.6907764Esposito, J. M., Feemster, M. G., Smith, E., 2008. Cooperative manipulation on the water using a swarm of autonomous tugboats. in Proc. 2008 IEEE Int. Conf. on Robotics and Automation, pp. 1501-1506. https://doi.org/10.1109/ROBOT.2008.4543414Habibi, G., Kingston, Z., Xie, W., Jellins, M., McLurkin, J., 2015. Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems. IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA.2015.7139356Seattle, Washington Hasimoto, M. and Oba, F., 1993. Dynamic control approach for motion coordination of multiple wheeled mobile robots transporting a single object. Proceedings of the 1993 IEEE./RSJ lntemational Conference on lntelligent Robots and Systems Yokohama, Japan July 26-30, 1993Hichri, B., Adouane, L., Fauroux, J.C., Mezouar, Y. and Doroftei, I. Cooperative Mobile Robot Control Architecture for Lifting and Transportation of Any Shape Payload. Chapter book of Distributed Autonomous Robotic Systems pp 177-191. ISBN 978-4-431-55877-4. https://doi.org/10.1007/978-4-431-55879-8_13Hirata, Y., Kosuge, K., 2001. Motion Control of Distributed Robot Helpers Transporting a Single Object in Cooperation with a Human. Lecture Notes in Control and Information Sciences. Experimental robotics. SpringerVerlag. Pp. 313-322. ISBN 3-540-42104-1 https://doi.org/10.1007/3-540-45118-8_32Karim, N.A. and Ardestani, M.A. 2016. Takagi-Sugeno Fuzzy formation control of non-holonomic robots. 4th International Conference on Control, Instrumentation, and Automation (ICCIA), Qazvin, 2016, pp. 178-183. https://doi.org/10.1109/ICCIAutom.2016.7483157Kosuge, K., Oosumi, T., 1996. Decentralized Control of Multiple Robots Handling an Object. Proc. Of 1996 IEEE Int. Conf. on Intelligent Robots and Systems, pp.318-323.Kosuge, K., Sato., M., 1999. Transportation of a Single Object by Multiple Decentralized- Controlled Nonholonomic Mobile Robots. Proceedings of the 1999 IEEVRSJ International Conference on Intelligent Robots and Systems.Krnjak, A., and others. 2015. Decentralized control of free ranging AGVs in warehouse environments. IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA.2015.7139465Li, P.Y., 1999. Adaptive Passive Velocity Field Control. American Control Conference. June, 1999. https://doi.org/10.1109/ACC.1999.783145Li, P.Y., Horowitz, R., 2001. Passive Velocity Field Control (PVFC): Part I, Geometry and Robustness. IEEE Trans on Automatic Control. Vol 46, no 9. https://doi.org/10.1109/9.948463Li, P.Y., Horowitz, R., 2001. Passive Velocity Field Control (PVFC): Part II, Application to contour following. IEEE Trans on Automatic Control. Vol 46, no 9, 2001. https://doi.org/10.1109/9.948464Liu, Z., Hou, L., Shi, Y., Zheng X., Teng, H., 2018. A co-evolutionary design methodology for complex AGV system. Neural Computing and Applications 29:959-974. Springer. https://doi.org/10.1007/s00521-016-2495-1Meissner, H., Ilsen, R. and Aurich, J.C. 2017. Analysis of control architectures in the context of Industry 4.0. Proc CIRP 2017; 62:165-9. https://doi.org/10.1016/j.procir.2016.06.113Mellinger, D., Shomin, M., Michael, N., Kumar, V., 2010. Cooperative grasping and transport using multiple quadrotors. in Proc. Distributed Autonomous Robotic Systems, Lusanne, pp 545-558. https://doi.org/10.1007/978-3-642-32723-0_39Neumann, M.A., Chin, M.H., Kitts, C.A., 2014. Object Manipulation through Explicit Force Control Using Cooperative Mobile Multi-Robot Systems" Proceedings of the World Congress on Engineering and Computer Science 2014 Vol I WCECS 2014, 22-24 October, 2014, San Francisco, USAOhashi, F., Kaminishi, K., Figueroa, J.D., Kato, H., Ogata, T., Hara T., Ota, J., 2016. Realization of heavy object transportation by mobile robots using handcarts and outrigger. Robomech Journal. https://doi.org/10.1186/s40648-016-0066-yON5G, 2020. 5G e industria 4.0: retos y oportunidades de la cuarta revolución industrial. Observatorio Nacional 5G. https://on5g.es/wp-content/uploads/2020/01/INFORME-ON5G-NDUSTRIA4.0-DIGITAL.pdf. Accesible el 31/03/2020.Owen-Hill. A., 2018. Why we're entering the age of robotic logistics. Robotiq. https://blog.robotiq.com/why-were-entering-the-age-of-robotic-logisticsParker, L. E., 2008. Multiple mobile robot systems. Springer Handbook of Robotics. https://doi.org/10.1007/978-3-540-30301-5_41Peng, T., Qian, J., Zi, B., Liu, J., Wang, X., 2016. Mechanical Design and Control System of an Omnidirectional Mobile Robot for Material Conveying. International Conference on Digital Enterprise Technology DET-2016. DOI: 10.1016/j.procir.2016.10.068. Springer. https://doi.org/10.1007/s00521-016-2495-1Pereira, G.A.S., Pimentel, B.S., Chaimowicz, L., Campos, M.F.M., 2002. Coordination of multiple mobile robots in an object carrying task using implicit communication. Proceedings of the 2002 IEEE International Conference on Robotics & Automation" May 2002. https://doi.org/10.1109/ROBOT.2002.1013374Quinn, M., 2004. The evolutionary design of controllers for minimallyequipped homogeneous multi-robot systems. Ph.D. thesis. Brighton: University of SussexReister, D. B., 1991. A New Wheel Control System for the Omnidirectional HERMIES-III Robot. Proceedings of the IEEE Conference on Robotics and Automation Sacramento, California, April 7-12, pp. 2322-2327.Ria, 2019. Robotic Industries Association. "Logistic Robots" https://www.robotics.org/service-robots/logistics-robots, available on June 7th, 2019.Saha, S.K. and Angeles, J. 1989. Kinematics and dynamics of a three-wheeled 2-DOF AGV. ICRA 1989. https://doi.org/10.1109/ROBOT.1989.100202Santos, C., Espinosa, F., Martinez-Rey, M., Gualda, D. and Losada, C. 2019. Self-Triggered Formation Control of Nonholonomic Robots. Sensors 2019, 19(12), 2689; https://doi.org/10.3390/s19122689Solaque, L.E., Avendaño, D.R., Molina, M.A., Pulido, C.A. 2015. Sistema de transporte cooperativo desarrollado para un grupo de robots móviles noholonómicos usando el método Líder Virtual. Congreso internacional 264 de ingeniería mecatrónica y automatización - CIIMA 2015Suh, J.H., Lee, Y.J., Lee, K.S., 2005. Object-transportation control of cooperative AGV systems based on virtual-passivity decentralized control algorithm. Journal of Mechanical Science and Technology. Vol 19 n09, pp. 1720-1735. https://doi.org/10.1007/BF02984184Tan, W. 2002. Modeling and Control Design of an AGV. Proceedings of the 41st IEEE Conference on Decision and Control. 2002. https://doi.org/10.1109/CDC.2002.1184623Tuci, E., Alkilabi, M. H., & Akanyeti, O., 2018. Cooperative Object Transport in Multi-robot Systems: A Review of the State-of-the-Art. Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2018.00059Ullrich, G., 2015. Automated Guided Vehicle Systems. A Primer with Practical Applications. Springer. ISBN 978-3-662-44813-7 DOI 10.1007/978-3-662-44814-4Wada, M. 1996. Holonomic and omnidirectional vehicle with conventional tires. IEEE Int. Conference on Robotics and Automation. May, 1996.Wada, M., Torii, R., 2013. Cooperative transportation of a single object by omnidirectional robots using potential method. 16th International Conference on Advanced Robotics (ICAR). https://doi.org/10.1109/ICAR.2013.6766543Wang, Z., Nakano, E., and Matsukawa, T., 1994. Cooperating multiple behavior based robots for object manipulation. in Proc. of the IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems, Vol. 3 1524-1531 https://doi.org/10.1007/978-4-431-68275-2_33Wang, 2016 Z. Wang and M. Schwager. Chapter book: "Multi-robot manipulation without communication". Book: Distributed autonomous robotic systems. Editors: N.Y. Chong and Y.J. ISBN 978-4-431-55877-4 DOI 10.1007/978-4-431-55879-8Wang, T.M., Tao, Y., Liu, H., 2018. Current researches and future development trend of intelligent robot: a review. International Journal of Automation & Computing. Vol 15, no 5, pp. 525-548. https://doi.org/10.1007/s11633-018-1115-1Yan, Z., Jouandeau, N., and Cherif, A. A., 2013. A Survey and Analysis of Multi-Robot Coordination. Int. Journal of Advanced Robotic Systems 10 (12), 399. https://doi.org/10.5772/57313Yang, X., Watanabe, K., Kiguchi, K., Izumi, K., 2003. Coordinated transportation of a single object by two nonholonomic mobile robots. Artif Life Robotics. ISAROB 2003. https://doi.org/10.1007/BF02480885Yufka, A., Ozkan, M., 2015. Formation-based Control Scheme for Cooperative Transportation by Multiple Mobile Robots. International Journal of Advanced Robotic Systems, 2015. https://doi.org/10.5772/60972Zhan, M. and Yu, K. 2018. Wireless Communication Technologies in Automated Guided Vehicles: Survey and Analysis. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. https://doi.org/10.1109/IECON.2018.859278

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms

Using a mobile robot for hazardous substances detection in a factory environment

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndustries that work with toxic materials need extensive security protocols to avoid accidents. Instead of having fixed sensors, the concept of assembling the sensors on a mobile robot that performs the scanning through a defined path is cheaper, configurable and adaptable. This work describes a mobile robot, equipped with several gas sensors and LIDAR, that follows a trajectory based on waypoints, simulating a working Autonomous Guided Vehicle (AGV). At the same time, the robot keeps measuring for toxic gases. In other words, the robot follows the trajectory while the gas concentration is under a defined value. Otherwise, it starts the autonomous leakage search based on a search algorithm that allows to find the leakage position avoiding obstacles in real time. The proposed methodology is verified in simulation based on a model of the real robot. Therefore, three path plannings were developed and their performance compared. A Light Detection And Ranging (LIDAR) device was integrated with the path planning to propose an obstacle avoidance system with a dilation technique to enlarge the obstacles, thus, considering the robot’s dimensions. Moreover, if needed, the robot can be remotely operated with visual feedback. In addition, a controller was made for the robot. Gas sensors were embedded in the robot with Finite Impulse Response (FIR) filter to process the data. A low cost AGV was developed to compete in Festival Nacional de Robótica (Portuguese Robotics Open) 2019 - Gondomar, describing the robot’s control and software solution to the competition.As indústrias que trabalham com materiais tóxicos necessitam de extensos protocolos de segurança para evitar acidentes. Ao invés de ter sensores estáticos, o conceito de instalar sensores em um robô móvel que inspeciona através de um caminho definido é mais barato, configurável e adaptável. O presente trabalho descreve um robô móvel, equipado com vários sensores de gás e LIDAR, que percorre uma trajetória baseada em pontos de controle, simulando um AGV em trabalho. Em simultâneo são efetuadas medidas de gases tóxicos. Em outras palavras, o robô segue uma trajetória enquanto a concentração de gás está abaixo de um valor definido. Caso contrário, inicia uma busca autônoma de vazamento de gás com um algoritmo de busca que permite achar a posição do gás evitando os obstáculos em tempo real. A metodologia proposta é verificada em simulação. Três algoritmos de planejamento de caminho foram desenvolvidos e suas performances comparadas. Um LIDAR foi integrado com o planejamento de caminho para propôr um sistema de evitar obstáculos. Além disso, o robô pode ser operado remotamente com auxílio visual. Foi feito um controlador para o robô. Sensores de gás foram embarcados no robô com um filtro de resposta ao impulso finita para processar as informações. Um veículo guiado automático de baixo custo foi desenvolvido para competir no Festival Nacional de Robótica 2019 - Gondomar. O controle do veículo foi descrito com o programa de solução para a competição

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201