Cooperative human-robot haptic navigation

International audienceThis paper proposes a novel use of haptic feedback for human navigation with a mobile robot. Assuming that a path-planner has provided a mobile robot with an obstacle-free trajectory, the vehicle must steer the human from an initial to a desired target position by only interacting with him/her via a custom-designed vibro-tactile bracelet. The subject is free to decide his/her own pace and a warning vibrational signal is generated by the bracelet only when a large deviation with respect to the planned trajectory is detected by the vision sensor on-board the robot. This leads to a cooperative navigation system that is less intrusive, more flexible and easy-to-use than the ones existing in literature. The effectiveness of the proposed system is demonstrated via extensive real-world experiments

Cooperative human-robot haptic navigation

International audienceThis paper proposes a novel use of haptic feedback for human navigation with a mobile robot. Assuming that a path-planner has provided a mobile robot with an obstacle-free trajectory, the vehicle must steer the human from an initial to a desired target position by only interacting with him/her via a custom-designed vibro-tactile bracelet. The subject is free to decide his/her own pace and a warning vibrational signal is generated by the bracelet only when a large deviation with respect to the planned trajectory is detected by the vision sensor on-board the robot. This leads to a cooperative navigation system that is less intrusive, more flexible and easy-to-use than the ones existing in literature. The effectiveness of the proposed system is demonstrated via extensive real-world experiments

Identification of Haptic Based Guiding Using Hard Reins

This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower's dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment

Graceful User Following for Mobile Balance Assistive Robot in Daily Activities Assistance

Numerous diseases and aging can cause degeneration of people's balance ability resulting in limited mobility and even high risks of fall. Robotic technologies can provide more intensive rehabilitation exercises or be used as assistive devices to compensate for balance ability. However, With the new healthcare paradigm shifting from hospital care to home care, there is a gap in robotic systems that can provide care at home. This paper introduces Mobile Robotic Balance Assistant (MRBA), a compact and cost-effective balance assistive robot that can provide both rehabilitation training and activities of daily living (ADLs) assistance at home. A three degrees of freedom (3-DoF) robotic arm was designed to mimic the therapist arm function to provide balance assistance to the user. To minimize the interference to users' natural pelvis movements and gait patterns, the robot must have a Human-Robot Interface(HRI) that can detect user intention accurately and follow the user's movement smoothly and timely. Thus, a graceful user following control rule was proposed. The overall control architecture consists of two parts: an observer for human inputs estimation and an LQR-based controller with disturbance rejection. The proposed controller is validated in high-fidelity simulation with actual human trajectories, and the results successfully show the effectiveness of the method in different walking modes

Evaluation of a predictive approach in steering the human locomotion via haptic feedback

In this paper, we present a haptic guidance policy to steer the user along predefined paths, and we evaluate a predictive approach to compensate actuation delays that humans have when they are guided along a given trajectory via sensory stimuli. The proposed navigation policy exploits the nonholonomic nature of human locomotion in goal directed paths, which leads to a very simple guidance mechanism. The proposed method has been evaluated in a real scenario where seven human subjects were asked to walk along a set of predefined paths, and were guided via vibrotactile cues. Their poses as well as the related distances from the path have been recorded using an accurate optical tracking system. Results revealed that an average error of 0.24 m is achieved by using the proposed haptic policy, and that the predictive approach does not bring significant improvements to the path following problem for what concerns the distance error. On the contrary, the predictive approach achieved a definitely lower activation time of the haptic interfaces

Haptic wrist guidance using vibrations for Human-Robot teams

Human-Robot teams can efficiently operate in several scenarios including Urban Search and Rescue (USAR). Robots can access areas too small or deep for a person, can begin surveying larger areas that people are not permitted to enter and can carry sensors and instruments. One important aspect in this cooperative framework is the way robots and humans can communicate during rescue operation. Vision and audio modalities may result not efficient in case of reduced visibility or high noise. A promising way to guarantee effective communications between robot and human in a team is the exploitation of haptic signals. In this work, we present a possible solution to let a robot guide the position of a human operator’s hand by using vibrations. We demonstrate that an armband embedding four vibrating motors is enough to guide the wrist of an operator along a predefined path or in a target location. The results proposed can be exploited in human-robot teams. For instance, when the robot detects the position of a sensible target, it can guide the wrist of the operator in such position following an optimal path

Haptic Guidance in Dynamic Environments Using Optimal Reciprocal Collision Avoidance

Human guidance in situations where the users cannot rely on their main sensory modalities, such as assistive or search-and-rescue scenarios, is a challenging task. In this letter, we address the problem of guiding users along collision-free paths in dynamic environments, assuming that they cannot rely on their main sensory modalities. In order to safely guide the subjects, we adapt the optimal reciprocal collision avoidance to our specific problem. The proposed algorithm takes into account the stimuli which can be displayed to the users and the motion uncertainty of the users when reacting to the provided stimuli. The proposed algorithm was evaluated in three different dynamic scenarios. A total of 18 blindfolded human subjects were asked to follow haptic cues in order to reach a target area while avoiding real static obstacles and moving users. Three metrics such as time to reach the goal, length of the trajectories, and minimal distance from the obstacles are considered to compare results obtained using this approach and experiments performed without visual impairments. Experimental results reveal that blindfolded subjects are successfully able to avoid collisions and safely reach the targets in all the performed trials. Although in this letter we display directional cues via haptic stimuli, we believe that the proposed approach can be general and tuned to work with different haptic interfaces and/or feedback modalities

Operator awareness in human–robot collaboration through wearable vibrotactile feedback

In industrial scenarios, requiring human–robot collaboration, the understanding between the human operator and his/her robot coworker is paramount. On the one side, the robot has to detect human intentions, and on the other side, the human needs to be aware of what is happening during the collaborative task. In this letter, we address the first issue by predicting human behavior through a new recursive Bayesian classifier, exploiting head, and hand tracking data. Human awareness is tackled by endowing the human with a vibrotactile ring that sends acknowledgments to the user during critical phases of the collaborative task. The proposed solution has been assessed in a human–robot collaboration scenario, and we found that adding haptic feedback is particularly helpful to improve the performance when the human–robot cooperation task is performed by nonskilled subjects. We believe that predicting operator's intention and equipping him/her with wearable interface, able to give information about the prediction reliability, are essential features to improve performance in a human–robot collaboration in industrial environments

Improved mutual understanding for human-robot collaboration: Combining human-aware motion planning with haptic feedback devices for communicating planned trajectory

In a collaborative scenario, the communication between humans and robots is a fundamental aspect to achieve good efficiency and ergonomics in the task execution. A lot of research has been made related to enabling a robot system to understand and predict human behaviour, allowing the robot to adapt its motion to avoid collisions with human workers. Assuming the production task has a high degree of variability, the robot's movements can be difficult to predict, leading to a feeling of anxiety in the worker when the robot changes its trajectory and approaches since the worker has no information about the planned movement of the robot. Additionally, without information about the robot's movement, the human worker cannot effectively plan own activity without forcing the robot to constantly replan its movement. We propose a novel approach to communicating the robot's intentions to a human worker. The improvement to the collaboration is presented by introducing haptic feedback devices, whose task is to notify the human worker about the currently planned robot's trajectory and changes in its status. In order to verify the effectiveness of the developed human-machine interface in the conditions of a shared collaborative workspace, a user study was designed and conducted among 16 participants, whose objective was to accurately recognise the goal position of the robot during its movement. Data collected during the experiment included both objective and subjective parameters. Statistically significant results of the experiment indicated that all the participants could improve their task completion time by over 45% and generally were more subjectively satisfied when completing the task with equipped haptic feedback devices. The results also suggest the usefulness of the developed notification system since it improved users' awareness about the motion plan of the robot.Web of Science2111art. no. 367

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