A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms

Neural Network Controller Application on a Visual based Object Tracking and Following Robot

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods

Navigation control of an automated mobile robot robot using neural network technique

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue is the provision of sensors to study the earth. As neural networks (NNs) are described by adaptability and a fitness for managing non-linear problems, they are conceived to be useful when utilized on navigation robots. In this exploration our computerized reasoning framework is focused around neural network model for control of an Automated motion robot in eccentric and unsystematic nature. Hence the back propagation algorithm has been utilized for controlling the direction of the mobile robot when it experiences by an obstacle in the left, right and front directions. The recreation of the robot under different deterrent conditions is carried out utilizing Arduino which utilizes C programs for usage

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

A Survey on Obstacles Avoidance Mobile Robot in Static Unknown Environment

Autonomous mobile robots have in recent times gained interest from many researchers. This is due to wide range of mobile robot application. Numerous robots especially in navigation, obstacle avoidance and path following are currently under development. A reliable collision avoidance methodology is needed for effective navigation. Normally robots are fitted with transducers such as ultrasonic sensors, infrared and cameras for detecting environment. Various methods have been established in the past years to resolve navigational problems associated with mobile robots. They include fuzzy logic, potential fields, genetic algorithm, neural network and vision base approaches. Fuzzy logic demonstrates to be an appropriate tool for handling uncertainty that emerge from imprecise knowledge during route finding

A generalized laser simulator algorithm for optimal path planning in constraints environment

Path planning plays a vital role in autonomous mobile robot navigation, and it has thus become one of the most studied areas in robotics. Path planning refers to a robot's search for a collision-free and optimal path from a start point to a predefined goal position in a given environment. This research focuses on developing a novel path planning algorithm, called Generalized Laser Simulator (GLS), to solve the path planning problem of mobile robots in a constrained environment. This approach allows finding the path for a mobile robot while avoiding obstacles, searching for a goal, considering some constraints and finding an optimal path during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating a wave of points in all directions towards the goal point with adhering to constraints. A simulation study employing the proposed approach is applied to the grid map settings to determine a collision-free path from the start to goal positions. First, the grid mapping of the robot's workspace environment is constructed, and then the borders of the workspace environment are detected based on the new proposed function. This function guides the robot to move toward the desired goal. Two concepts have been implemented to find the best candidate point to move next: minimum distance to goal and maximum index distance to the boundary, integrated by negative probability to sort out the most preferred point for the robot trajectory determination. In order to construct an optimal collision-free path, an optimization step was included to find out the minimum distance within the candidate points that have been determined by GLS while adhering to particular constraint's rules and avoiding obstacles. The proposed algorithm will switch its working pattern based on the goal minimum and boundary maximum index distances. For static obstacle avoidance, the boundaries of the obstacle(s) are considered borders of the environment. However, the algorithm detects obstacles as a new border in dynamic obstacles once it occurs in front of the GLS waves. The proposed method has been tested in several test environments with different degrees of complexity. Twenty different arbitrary environments are categorized into four: Simple, complex, narrow, and maze, with five test environments in each. The results demonstrated that the proposed method could generate an optimal collision-free path. Moreover, the proposed algorithm result are compared to some common algorithms such as the A* algorithm, Probabilistic Road Map, RRT, Bi-directional RRT, and Laser Simulator algorithm to demonstrate its effectiveness. The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared methods. The GLS is 7.8 and 5.5 times faster than A* and LS, respectively, generating a path 1.2 and 1.5 times shorter than A* and LS. The mean value of the path cost achieved by the proposed approach is 4% and 15% lower than PRM and RRT, respectively. The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. Finally, to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out using an existing WMR platform in labs and roads. The experimental work investigates complete autonomous WMR path planning in the lab and road environments using live video streaming. The local maps were built using data from live video streaming s by real-time image processing to detect the segments of the lab and road environments. The image processing includes several operations to apply GLS on the prepared local map. The proposed algorithm generates the path within the prepared local map to find the path between start-to-goal positions to avoid obstacles and adhere to constraints. The experimental test shows that the proposed method can generate the shortest path and best smooth trajectory from start to goal points in comparison with the laser simulator

A review on multi-robot systems: current challenges for operators and new developments of interfaces

[ES] Los sistemas multi-robot están experimentando un gran desarrollo en los últimos tiempos, ya que mejoran el rendimiento de las misiones actuales y permiten realizar nuevos tipos de misiones. Este artículo analiza el estado del arte de los sistemas multi-robot, abordando un conjunto de temas relevantes: misiones, flotas, operadores, interacción humano-sistema e interfaces. La revisión se centra en los retos relacionados con factores humanos como la carga de trabajo o la conciencia de la situación, así como en las propuestas de interfaces adaptativas e inmersivas para solucionarlos.[EN] Multi-robot systems are experiencing great development in recent times, since they are improving the performance of current missions and allowing new types of missions. This article analyzes the state of the art of multi-robot systems, addressing a set of relevant topics: missions, fleets, operators, human-system interaction and interfaces. The review focuses on the challenges related to human factors such as workload and situational awareness, as well as the proposals of adaptive and immersive interfaces to solve them.Esta investigación ha recibido fondos de los proyectos SAVIER (Situational Awareness VIrtual EnviRonment) de Airbus; RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/ NMT-4331, financiado por los Programas de Actividades I+D de la Comunidad de Madrid y confinanciado por los Fondos Estructurales de la UE; y DPI2014-56985-R (Protección Robotizada de Infraestructuras Críticas) financiado por el ministerio de Economía y Competitividad del Gobierno de España.Roldan-Gómez, JJ.; De León Rivas, J.; Garcia-Aunon, P.; Barrientos, A. (2020). Una revisión de los sistemas multi-robot: desafíos actuales para los operadores y nuevos desarrollos de interfaces. Revista Iberoamericana de Automática e Informática industrial. 17(3). https://doi.org/10.4995/riai.2020.13100OJS305173Abbadi, A. and Prenosil., 2015. Safe path planning using cell decomposition approximation. Distance Learning, Simulation and Communication, 8.Adams, B. and Suykens, F., 2013 Astute: Increased Situational Awareness through proactive decision support and adaptive map-centric user interfaces. 2013 IEEE European Intelligence and Security Informatics Conference (EISIC), 289-293. https://doi.org/10.1109/EISIC.2013.74Almeida, L., Menezes, P. and Dias, J., 2017. Improving robot teleoperation experience via immersive interfaces. In 2017 4th IEEE Experiment@ International Conference (exp. at'17), 87-92. https://doi.org/10.1109/EXPAT.2017.7984414Arnold, K. P., 2016. The UAV Ground Control Station: Types, Components, Safety, Redundancy, and Future Applications. International Journal of Unmanned Systems Engineering., 4(1), 37.Ayaz, H., Shewokis, P. A., Bunce, S., Izzetoglu, K., Willems, B. and Onaral, B., 2012. Optical brain monitoring for operator training and mental workload assessment. Neuroimage, 59(1), 36-47. https://doi.org/10.1016/j.neuroimage.2011.06.023Beer, J., Fisk, A. D. and Rogers, W. A., 2014. Toward a framework for levels of robot autonomy in human-robot interactions. Journal of Human-Robot Interaction, 3(2), 74. https://doi.org/10.5898/JHRI.3.2.BeerBourguet, M. L., 2003. Designing and Prototyping Multimodal Commands. Interact, 3, 717-720.Brutschy, A., Pini, G., Pinciroli, C., Birattari, M. and Dorigo, M., 2014. Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Autonomous agents and multi-agent systems, 28(1), 101-125. https://doi.org/10.1007/s10458-012-9212-yCantelli, L., Mangiameli, M., Melita, C. D. and Muscato, G., 2013. UAV/UGV cooperation for surveying operations in humanitarian demining. 2013 IEEE international symposium on safety, security, and rescue robotics (SSRR),1-6). https://doi.org/10.1109/SSRR.2013.6719363Chang, H. and Jin, T., 2013. Command fusion based fuzzy controller design for moving obstacle avoidance of mobile robot. Future information communication technology and applications, Springer, 905-913. https://doi.org/10.1007/978-94-007-6516-0_99Chen, J. Y. C., Haas, E. C. and Barnes, M. J., 2007. Human performance issues and user interface design for teleoperated robots. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1231-1245. https://doi.org/10.1109/TSMCC.2007.905819Chen, J. Y. C., Barnes, M. J. and Harper-Sciarini, M., 2011. Supervisory control of multiple robots: Human-performance issues and user-interface design. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications an Reviews), 41(4), 435-454. https://doi.org/10.1109/TSMCC.2010.2056682Clark, C. M., 2005. Probabilistic road map sampling strategies for multi-robot motion planning. Robotics and Autonomous Systems, 53(3), 244-264. https://doi.org/10.1016/j.robot.2005.09.002Cummings, M. L., Bruni, S., Mercier, S. and Mitchell, P. J., 2007. Automation architecture for single operator, multiple UAV command and control. Tech. Rep DTIC Document.Cummings, M. L. and Mitchell P. J., 2008. Predicting controller capacity in supervisory control of multiple UAVs. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(2), 451-460. https://doi.org/10.1109/TSMCA.2007.914757Cummings, M. L., Mastracchio, C., Thornburg, K. M. and Mkrtchyan, A., 2013. Boredom and distraction in multiple unmanned vehicle supervisory control. Interacting with computers, 25(1), 34-47. https://doi.org/10.1093/iwc/iws011De Cubber, G., Doroftei, D., Serrano, D., Chintamani, K., Sabino, R. and Ourevitch, S., 2013. The EU-ICARUS project: developing assistive robotic tools for search and rescue operations. 2013 IEEE international symposium on safety, security, and rescue robotics (SSRR), 1-4. https://doi.org/10.1109/SSRR.2013.6719323De Greeff, J., Hindriks, K., Neerincx, M. A. and Kruijff-Korbayova, I., 2015. Human-robot teamwork in USAR environments: the TRADR project. Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, pp. 151-152. https://doi.org/10.1145/2701973.2702031Dezfoulian, S. H., Wu, D. and Ahmad, I. S., 2013. A generalized neural network approach to mobile robot navigation and obstacle avoidance. Intelligent Autonomous Systems, Springer, 12, 25-52. https://doi.org/10.1007/978-3-642-33926-4_3Di, B., Zhou, R. and Duan, H., 2015. Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance. Aerospace Science and Technology, 46, 386-397. https://doi.org/10.1016/j.ast.2015.08.006Dixon, S. R., Wickens, C. D. and Chang, D, 2005. Mission control of multiple unmanned aerial vehicles: A workload analysis. Human Factors: The Journal of the Human Factors and Ergonomics Society 47(3), 479-487. https://doi.org/10.1518/001872005774860005Donmez, B., Nehme, C. and Cummings, M.L., 2010. Modeling workload impact in multiple unmanned vehicle supervisory control. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(6), 1180-1190. https://doi.org/10.1109/TSMCA.2010.2046731Drury, J. L., Scholtz, J. and Yanco, H.A., 2003. Awareness in human-robot interactions. IEEE International Conference on Systems, Man and Cybernetics, 1, 912-918.Drury, J. L., Riek, L. and Rackliffe, N., 2006A. A decomposition of UAV-related situation awareness. Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction, 88-94. https://doi.org/10.1145/1121241.1121258Drury, J. L., Richer, J., Rackliffe, N. and Goodrich, M. A., 2006B. Comparing situation awareness for two unmanned aerial vehicle human interfaces approaches. Technical rpeort, Mitre Corp Bedford MA.Endsley, M. R., 1988A. Design and evaluation for situation awareness enhancement. Proceedings of the human factors and ergonomics society annual meeting, SAGE Publications, 32(2), 97-101. https://doi.org/10.1177/154193128803200221Endsley, M. R., 1988B. Situation awareness global assessment technique (SAGAT). Proceedings of the IEEE 1988 National Aerospace and Electronics Conference (NAECON), 789-795.Endsley, M. R. and Garland, D. J., 2000. Situation awareness analysis and measurement. CRC Press. https://doi.org/10.1201/b12461Endsley M. R., 1999. Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 42(3), 462-492. https://doi.org/10.1080/001401399185595Flushing, E. F., Gambardella, L. and Di Caro, G. A., 2012. Gis-based mission support system for wilderness search and rescue with heterogeneous agents. Proc 2nd Workshop on robots and sensors integration in future rescue INformation system (ROSIN), IEEE/RSJ Int Conference on Intelligent Robots and Systems (IROS).Foit, K., 2014. Mixed reality as a tool supporting programming of the robot. Advanced Materials Research, Trans Tech Publications, 1036, 737-742. https://doi.org/10.4028/www.scientific.net/AMR.1036.737Frische, F. and Lüdtke, A., 2013. SA-tracer: A tool for assessment of UAV swarm operator SA during mission execution. 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 203-211. https://doi.org/10.1109/CogSIMA.2013.6523849Fuchs, C., Borst, C., de Croon, G. C., Van Paassen, M. M. and Mulder, M., 2014. An ecological approach to the supervisory control of UAV swarms. International Journal of Micro Air Vehicles, 6(4), 211-229. https://doi.org/10.1260/1756-8293.6.4.211Galceran, E. and Carreras, M., 2013. A survey on coverage path planning for robotics. Robotics and Autonomous Systems, 61(12), 1258-1276. https://doi.org/10.1016/j.robot.2013.09.004García, J. C., Patrão, B., Pérez, J., Seabra, J., Menezes, P., Dias, J. and Sanz, P.J., 2015. Towards an immersive and natural gesture controlled interface for intervention underwater robots. IEEE OCEANS 2015-Genova, 1-5. https://doi.org/10.1109/OCEANS-Genova.2015.7271501García, S. E., Slawiñski, E., Mut, V., and Penizzotto, F., 2018. Collision avoidance method for multi-operator multi-robot teleoperation system. Robotica, 36(1), 78-95. https://doi.org/10.1017/S0263574717000169Garzón, M., Valente, J., Zapata, D. and Barrientos, A., 2013. An aerial-ground robotic system for navigation and obstacle mapping in large outdoor areas. Sensors, 13(1), 1247-1267. https://doi.org/10.3390/s130101247Garzón, M., Valente, J., Roldán, J. J., Cancar, L., Barrientos, A. and Del Cerro, J., 2016. A multirobot system for distributed area coverage and signal searching in large outdoor scenarios. Journal of Field Robotics, 33(8), 1087-1106. https://doi.org/10.1002/rob.21636Garzón, M., Valente, J., Roldán, J. J., Garzón-Ramos, D., de León, J., Barrientos, A. and del Cerro, J., 2017. Using ros in multi-robot systems: Experiences and lessons learned from real-world field tests. In Robot Operating System (ROS), Springer, Cham, 449-483. https://doi.org/10.1007/978-3-319-54927-9_14Goerzen, C., Kong, Z. and Mettler, B., 2009. A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Selected papers from the 2nd International Symposium on UAVs, Reno, Nevada, USA, Springer, 64-100. https://doi.org/10.1007/978-90-481-8764-5_5Goodrich, M. A. and Schultz, A. C., 2007. Human-robot interaction: a survey. Foundations and trends in human-computer interaction, 1(3), 203-275. https://doi.org/10.1561/1100000005Gregory, J., Fink, J., Stump, E., Twigg, J., Rogers, J., Baran, D., Fung, N. and Young, S., 2016. Application of multi-robot systems to disaster-relief scenarios with limited communication. In Field and Service Robotics, Springer, Cham, 639-653. https://doi.org/10.1007/978-3-319-27702-8_42Haas, E. C., Pillalamarri, K., Stachowiak, C. C. and Fields, M., 2011. Multimodal controls for soldier/swarm interaction. IEEE RO-MAN, 223-228. https://doi.org/10.1109/ROMAN.2011.6005227Hagiwara, Y., 2015. Cloud based VR system with immersive interfaces to collect multimodal data in human-robot interaction. 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), 256-259. https://doi.org/10.1109/GCCE.2015.7398709Hart, S. G. and Staveland E.L., 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology, 52, 139-183. https://doi.org/10.1016/S0166-4115(08)62386-9Hart, S. G., 2006. NASA-task load index (NASA-TLX); 20 years later. Proceedings of the human factors and ergonomics society annual meeting, Sage Publications Sage CA, Los Angeles, CA, 50(9), 904-908. https://doi.org/10.1177/154193120605000909Hobbs, A. and Herwitz, S. R., 2014. Human factors in the maintenance of unmanned aerial vehicle (UAV) swarm management. Applied Ergonomics, 58, 66-80. https://doi.org/10.1016/j.apergo.2016.05.011Hocraffer, A. and Nam, C. S. A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management. Applied Ergonomics, 58, 66-80. https://doi.org/10.1016/j.apergo.2016.05.011Hong, A., 2016. Human-Robot Interactions for Single Robots and Multi-Robot Teams. PhD thesis, Department of Mechanical and Industrial Engineering, University of Toronto.Janchiv, A., Batsaikhan, D., hwan Kim, G. and Lee, S. G., 2011. Complete coverage path planning for multi-robots based on. 2011 11th IEEE International Conference on Control, Automation and Systems, 824-827.Jasper, P., Sibley, C. and Coyne, J. Using Heart Rate Variability to Assess Operator Mental Workload in a Command and Control Simulation of Multiple Unmanned Aerial Vehicles. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, SAGE Publications Sage CA, Los Angeles, CA, 60(1), 1125-1129. https://doi.org/10.1177/1541931213601264Jia, X. and Meng, M. Q. H., 2013. A survey and analysis of task allocation algorithms in multi-robot systems. 2013 IEEE International Conference on Robotics and biomimetics (ROBIO), 2280-2285. https://doi.org/10.1109/ROBIO.2013.6739809Jiang, Y., 2016. A survey of task allocation and load balancing in distributed systems. IEEE Transactions on Parallel and Distributed Systems, 27(2), 585-599. https://doi.org/10.1109/TPDS.2015.2407900Johannsmeier, L. and Haddadin, S., 2016. A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robotics and Automation Letters, 2(1), 41-48. https://doi.org/10.1109/LRA.2016.2535907Kapoutsis, A. C., Chatzichristofis, S. A., Doitsidis, L., de Sousa, J. B., Pinto, J., Braga, J. and Kosmatopoulos, E. B., 2016. Real-time adaptive multi-robot exploration with application to underwater map construction. Autonomous robots, 40(6), 987-1015. https://doi.org/10.1007/s10514-015-9510-8Kavitha, S., Veena, S. and Kumaraswamy, R., 2015. Development of automatic speech recognition system for voice activated Ground Control system. 2015 IEEE International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15), pp. 1-5. https://doi.org/10.1109/ITACT.2015.7492684Khaleghi, A. M., Xu, D., Minaeian, S., Li, M., Yuan, Y., Liu, J., Son, Y. J., Vo, C., Mousavian, A. and Lien, J. M., 2014. A comparative study of control architectures in UAV/UGV-based surveillance system. Proceedings of the IIE Annual Conference, Institute of Industrial and Systems Engineers (IISE), 3455.Khamis, A., Hussein, A. and Elmogy, A., 2015. Multi-robot Task Allocation: A Review of the State-of-the-Art. Cooperative Robots and Sensor Networks, Springer, 31-51. https://doi.org/10.1007/978-3-319-18299-5_2Kirchner, E. A., Kim, S. K., Tabie, M., Wöhrle, H., Maurus, M. and Kirchner, F., 2016. An intelligent man-machine interface - Multi-robot control adapted for task engagement based on single-trial detectability of P300. Frontiers in human neuroscience, 10, 291. https://doi.org/10.3389/fnhum.2016.00291Kolling, A., Nunnally, S. and Lewis, M., 2012. Towards human control of robot swarms. Proceedings of the seventh annual ACM/IEEE international conference on human-robot interaction, 89-96. https://doi.org/10.1145/2157689.2157704Kothari, M., Postlethwaite, I. and Gu, D. W. Multi-UAV path planning in obstacle rich environment using rapidly-exploring random trees. Proceedings of the 48 IEEE Conference on Decision and Control, 3069-3074.Kruijff-Korbayová, I., Colas, F., Gianni, M., Pirri, F., de Greeff, J., Hindriks, K., Neerincx, M., Ögren, P., Svoboda, T. and Worst, R., 2015. Tradr project: Long-term human-robot teaming for robot assisted disaster response. KI-Künstliche Intelligenz, 29(2), 193-201. https://doi.org/10.1007/s13218-015-0352-5Kurniawan, H., Maslov, A. V. and Pechenizkiy, M., 2013. Stress detection from speech and galvanic skin response signals. IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), 209-214. https://doi.org/10.1109/CBMS.2013.6627790Lang, R. G., da Silva, I. N., Romero, R. A. F., 2014. Development of distributed control architecture for multi-robot systems. IEEE 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol, 163-168. https://doi.org/10.1109/SBR.LARS.Robocontrol.2014.49Larochelle, B., Kruijff, G. J. M., Smets, N., Mioch, T. and Groenewegen, P., 2011. Establishing human situation awareness using a multi-modal operator control unit in an urban search & rescue human-robot team, IEEE, 229-234. https://doi.org/10.1109/ROMAN.2011.6005237Lesire, C., Infantes, G., Gateau, T. and Barbier, M., 2016. A distributed architecture for supervision of autonomous multi-robot missions. Autonomous Robots, 40(7), 1343-1362. https://doi.org/10.1007/s10514-016-9603-zLindemuth, M., Murphy, R., Steimle, E., Armitage, W., Dreger, K., Elliot, T., Hall, M., Kalyadin, D., Kramer, J., Palankar, M. and Pratt, K., 2011. Sea robot-assisted inspection. IEEE robotics & automation magazine, 18(2), 96-107. https://doi.org/10.1109/MRA.2011.940994Lysaght, R. J., 1989. Operator workload: Comprehensive review and evaluation of operator workload methodologies. Tech. Rep. DTIC Document. https://doi.org/10.21236/ADA212879Mantecón, T., del Blanco, C. R., Jaureguizar, F. and García, N., 2014. New generation of human machine interfaces for controlling UAV through depth-based gesture recognition. Unmanned Systems Technology XVI, International Society for Optics and Photonics, 9084. https://doi.org/10.1117/12.2053244Marino, A., Parker, L. E., Antonelli, G. and Caccavale, F., 2013. A decentralized architecture for multi-robot systems based on the null-space-behavioral control with application to multi-robot border patrolling. Journal of Intelligent & Robotic Systems, 71(3-4), 423-444. https://doi.org/10.1007/s10846-012-9783-5Martín-Barrio, A., Terrile, S., Barrientos, A. and del Cerro, J., 2018. Hyper-Redundant Robots: Classification, State-of-the-Art and Issues. Revista Iberoamericana de Automática e Informática Industrial, 15(4), 351-362. https://doi.org/10.4995/riai.2018.9207Martín-Barrio, A., Roldán, J. J., Terrile, S., del Cerro, J. and Barrientos, A., 2019. Application of Immersive Technologies and Natural Language to Hyper-Redundant Robot Teleoperation. Virtual Reality, 1-15. https://doi.org/10.1007/s10055-019-00414-9Martins, H., Oakley, I. and Ventura, R., 2015. Design and evaluation of a head-mounted display for immersive 3D teleoperation of field robots. Robotica, 33(10), 2166-2185. https://doi.org/10.1017/S026357471400126XMatellán, V. and Borrajo, D., 2001. ABC2 an agenda based multi-agent model for robots control and cooperation. Journal of Intelligent & Robotic Systems, 32(1), 93-114. https://doi.org/10.1023/A:1012009429991McCarley, J.S. and Wickens, C. D., 2005. Human factors implications of UAVs in the national airspace. University of Illinois at Urbana-Champaign, Aviation Human Factors Division.McDuff, D., Gontarek, S. and Picard, R., 2014. Remote measurement of cognitive stress via heart rate variability. 36th Annual International Conference on the IEEE Engineering in Medicine and Biology Society (EMBC), 2957-2960. https://doi.org/10.1109/EMBC.2014.6944243Menda, J., Hing, J. T., Ayaz, H., Shewokis, P. A., Izzetoglu, K., Onaral, B. and Oh, P., 2011. Optical brain imaging to enhance UAV operator training, evaluation, and interface development. Journal of intelligent & robotic systems, 61(1-4), 423-443. https://doi.org/10.1007/s10846-010-9507-7Monajjemi, V. M., Pourmehr, S., Sadat, S. A., Zhan, F., Wawerla, J., Mori, G. and Vaughan, R., 2014. Integrating multi-modal interfaces to command UAVs. Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, 106-106. https://doi.org/10.1145/2559636.2559646Moore, J., Wolfe, K. C., Johannes, M. S., Katyal, K. D., Para, M. P., Murphy, R. J., Hatch, J., Taylor, C. J., Bamberger, R. J. and Tunstel, E., 2016. Nested marsupial robotic system for search and sampling in increasingly constrained environments. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2279-2286 https://doi.org/10.1109/SMC.2016.7844578Mosteo, A. R. and Montano, L., 2010. A survey of multi-robot task allocation. Technical Report No. AMI-009-10-TEC, Instituto de Investigación en Ingeniería de Aragón, University of Zaragoza, Zaragoza, Spain.Murphy, R. R., 2004. Human-robot interaction in rescue robotics. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(2), 138-153. https://doi.org/10.1109/TSMCC.2004.826267Nagi, J., Giusti, A., Di Caro, G. A. and Gambardella, L. M., 2014. Human control of UAVs using face pose estimates and hand gestures. 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 1-2. https://doi.org/10.1145/2559636.2559644Nam, C. S., Johnson, S., Li, Y. and Seong, Y., 2009. Evaluation of human-agent user interfaces in multi-agent systems. International Journal of Industrial Ergonomics, 39(1), 192-201. https://doi.org/10.1016/j.ergon.2008.08.008Nestmeyer, T., Giordano, P. R., Bülthoff, H. H. and Franchi, A., 201

Autonomous navigation using image processing

In the recent years, the pursuit intelligent and self-operated machines have increased. The human user is to be completely eliminated or minimized as much as possible. It is also popular now to observe machines that are able to do variety of tasks, instead of just one. A division of intelligent robotics is autonomous navigation. Google, Tesla, Honda, and many other large corporations are trying to master this field, since the search for a self-driving vehicle is profitable as much as it is difficult. The research presented in this thesis is autonomous navigation for mobile robots in an indoor environment, but not limited to. Some explored algorithms focus on navigating in environment that has been already explored and mapped. Algorithms such as modified A-Star and goal-based navigational vector field were tested for how effectively a path is planned from one point to another. The algorithms were compared and analyzed for how well the robot avoided obstacles and the length of the path taken. Other algorithms were also developed and tested for navigation without a map. The navigational algorithms are simulated on different artificial environments as well as on real environments. Machine learning is used to learn and adapt to the robot’s motion behaviours, which enables the robot to perform movements as intended by the implemented navigational algorithms. Referred to in this thesis as the intelligence engine, a feed-forward artificial neural network was created to predict power delivery to the motors. Back-propagation algorithm is used alongside the neural network to enable supervised learning. Similar to human vision, the algorithm relies mainly on image processing to obtain data about the surrounding environment. The data human eyes provide helps one perceive and understand the surroundings. Similarly, a Kinect sensor is used in this thesis to get 2-dimensional colour data as well as depth data. A program was implemented to process and understand this arbitrary sequential array of numbers in terms of quantifiable values. The robot in return is capable of understanding target, obstructions, and is capable of navigation. All external data are gathered from one optical sensor. Many different algorithms were implemented and tested to efficiently detect and track a target. The idea is to make an artificial robot perceive its’ surrounding using 3-Dimentional image data and intelligently navigate the local surroundings