Another hint that rescue robotics represents a ideal playground for PBL scenarios in ..... providing global positioning to the surface vehicle inspecting underwater.
Developing a PBL-based Rescue Robotics Course Frank Hees, Sabina Jeschke RWTH Aachen University ZLW/IMA Dennewartstr. 27 52068 Aachen, Germany +49 241 80-911-50
{hees,sabina.jeschke}@zlw-ima.rwth-aachen.de Nicole Natho, Olivier Pfeiffer Technische Universität Berlin, MuLF Straße des 17. Juni 136 10623 Berlin, Germany +49 30 314 24 603
{natho, pfeiffer}@math.tu-berlin.de ABSTRACT Problem-based learning (PBL) denotes self-determined learning and learning through discovery, activity-based education, interdisciplinary education, and self-assessment. The participants in problem based learning courses learn to analyze a subject or a problem with minimal guidance by their teacher or rather their facilitator of learning. Students find and use the suitable sources of information by themselves, and finally, compare, select and convert the results. The essential highlight of the PBL approach is the examination of authentic (real life) and complex subjects. The origin of the PBL lies in application-based technical engineering subjects and later in medical education. Robotics education is perfectly suited for the application of PBL-scenarios as robotics combines a multitude of technological disciplines (ranging from computer sciences, software engineering, artificial intelligence, electrical engineering up to technology design) and its ubiquitous popularity with a variety soft skills (team skills, complex problem-solving strategies, etc.), required in the development process. The popularity of robots can be easily deduced from the large number of robotic heroes in literature and movies. Thus, robotics is ideally suited as a model project-oriented course of combining communication skills, development of strategies to solve complex interdisciplinary challenges, and different concepts of soft- and hardware engineering. Among the wide range of robotics applications, one field of particular importance is the field of “Rescue Robots”. Here, robots are developed that operate in catastrophe-scenarios, e.g. earthquakes or fires. Based on the data obtained from their various sensors (video cameras, infrared sensors, laser scanner and gas sensors), these robots have to manage their tasks autonomously in catastrophe-based scenarios. This comprises detection, rescue, and aid for victims should the situation arise. In order to fulfill these complex tasks, development of basic skills such as exact movements on unstable bedrock, field mapping, positioning and communication in weakly structured environments is necessary. Besides the construction of preferably allterrain and robust robots, the improvement of innovative analysis procedures for complex sensor data is another focus of development. In addition, conception and realization of novel man-machine-interfaces come to the fore in order to support the operators of robots with their exhausting control tasks.
Beyond that, robotics is increasing the number of female students in the natural sciences and engineering. It has the potential of attracting girls and young females at their respective levels education by illustrating their own potential in a playful experimental setting. Independent design and construction of robots demonstrates the importance of creativity and social relevance, giving young women more confidence in their technical and scientific skills, facts affecting young women’s choice of degree. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. First Kuwait Conf. on E-Services and E-Systems, Nov 17-19, 2009 Copyright 2009 ACM 978-1-60558-797-4…$5.00. IMA/ZLW & IfU – RWTH Aachen University Institute of Information Management in Mechanical Engineering IMA
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Head of Institute: Univ.-Prof. Dr. rer. nat. Sabina Jeschke 1st Deputy: apl.-Prof. Dr. habil. Ingrid Isenhardt 2nd Deputy: Dr. rer. nat. Frank Hees Senior Advisor: Univ.-Prof. Dr.-Ing. em. Klaus Henning
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Integrated in the “RoboCup”, the “Rescue-Robot League” clarifies the intensified orientation of the “RoboCup initiative” on real life applications. Another hint that rescue robotics represents a ideal playground for PBL scenarios in academic education.
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Categories and Subject Descriptors K.3.1 [Computing Milieux]: Computer Uses in Education. General Terms Management, Design, Security, Human Factors. Keywords PBL, Robotics, Academic Education
1. INTRODUCTION The deployment of robots in disaster has received increasing attention in robotics research since the first suggestions in the aftermath of the Hanji-Awaji earthquake in Kobe, 1995, and the bombing of the Murrah federal building in Oklahoma City in the same year [1]. Since then, robots have actually participated in 7 disaster, starting with the attack on the World Trade Center, New York City, USA in 2001 [2]. While the success of the robots varied from scenario to scenario, due mostly to unforeseen challenges in locomotion and control of the specific systems, they proved that robots should and will play a growing role in future search and rescue (SAR) operations. As there are strong indications of global warming and the subsequent climate change increasing the chances of natural disasters [3, 4], the scenario of disaster response and relief has a strong social impact and relevance. Public awareness of the problem is high, providing excellent motivation for using such scenarios as the basis of problems in a PBL course. At the same time, the requirements placed on robotic systems deployed in such scenarios are both unique and technologically demanding. As a result, SAR robots are ideally suited to demonstrating the complexity of robot design, construction and control in a PBL course. The simple problem of navigating a robot within a collapsed structure alone leads to topics ranging from robust locomotive systems that will not get damaged on the extremely rough and uneven surfaces, cybernetics for precise control of the movement under such conditions, control as wireless remote control by a human operator may be blocked through steel components within the structure itself while fiber-optics cables might snatch and tear as the system moves through tight confines. In addition to the wide range of robotic-related topics that may be introduced in the course of these scenarios, the unpredictable and ever-changing situations faced in rescue operations are ideally suited to train problem solving and decision finding skills.
1.1
Tasks in Search and Rescue
Reconnaissance and Mapping: Disasters, both man-made (industrial accidents, attacks) and natural can drastically alter the layout of even well known areas or change the existing infrastructure and access options. Reconnaissance and mapping is meant to provide general information about the situation and a general geographical and topological layout of the stricken area, usually sacrificing minute detail in favor of quick overall coverage.
Search: In difference to reconnaissance, search is meant to provide detailed information about less accessible and often far smaller areas (collapsed buildings, landslides, flooded structures) for specific purposes, such as localizing persons, objects or specific situations
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Head of Institute: Univ.-Prof. Dr. rer. nat. Sabina Jeschke 1st Deputy: apl.-Prof. Dr. habil. Ingrid Isenhardt 2nd Deputy: Dr. rer. nat. Frank Hees Senior Advisor: Univ.-Prof. Dr.-Ing. em. Klaus Henning
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The complexity and cost of a more flexible general purpose design result in SAR robots usually being designed to fulfill one or several specific task [5]:
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Structural Inspection: Most rescue operations require direct human intervention at some point. To ensure their safety close up sensor-data of the structural integrity of a damaged or collapsed structure is required before further access by rescue teams, both robots and humans, is possible. This task places high demands on sensor capabilities as well as maneuverability within the tight and confined spaces that can be expected.
Rubble and obstacle removal: Access to buried persons or hazardous situations often requires the removal of obstacles or the stabilization of badly damaged areas that resulted from the disaster. The manipulation of these objects requires more raw power than finesse combined with smallest possible size and foot print.
In situ medical assessment and intervention: Experience from the Oklahoma City bombing has shown the need of providing quick medical access to persons trapped within collapsed structures [6], even before human rescuers can safely reach them. Robots can be used to reach these persons and provide diagnostic sensor data, a direct, usually verbal, channel of communication between victim and medical personal and transportation capacities for medical supplies or even life support.
Medically sensitive extrication and evacuation of casualties: Certain hazardous situations, such as the hot spots of radiological, chemical or biological disasters require the evacuation of victims without the possibility of more direct human intervention or beyond the capacity of humans operating in bulky and heavy protective gear.
Mobile beacon or repeater: Robots can be used to form ad-hoc sensor and communication networks to enable or extend the range of all data traffic between the inside and outside of a disaster area.
On-Site surrogate for off-site specialists: Robots can be used to provide remote sensing, communication and manipulative capabilities to off-site specialists in support of on-site teams. The robot “acts as the body” for a remote controller, enabling the quick contribution of additional specialists, even in several different locations at once. Each of these tasks can be the basis of another task in the list, leading to a natural succession of problems ideally suited to a PBL course with subsequent tasks often requiring refinement in the skills and knowledge necessary. Reconnaissance and mapping might lead to the discovery of buildings destroyed in a landslide, requiring the search for survivors within these buildings. In consequence, the problem of best suited motive systems progresses, as reconnaissance can be performed from the air or surface, with relatively low demands on control and robustness of the drive system, while search will require entering into confined, rugged and hard to predict terrain, posing more challenging requirements. Search might result in the demand for structural inspection as a victim might be trapped in an unsound area of a structure and so on.
2. RELATED WORK Under Socrates it was familiar that a priori ignoramus gains access to gradual solutions of complex problems by starting from questions. In nowadays’ knowledge and information society, this skill is of essential interest. To educate and therefore to improve this skill, a new style of teaching was developed in the seventies by Howard Barrows et al [7] called IMA/ZLW & IfU – RWTH Aachen University Institute of Information Management in Mechanical Engineering IMA
Center for Learning and Knowledge Management ZLW Assoc. Institute for Management Cybernetics e.V. IfU
Head of Institute: Univ.-Prof. Dr. rer. nat. Sabina Jeschke 1st Deputy: apl.-Prof. Dr. habil. Ingrid Isenhardt 2nd Deputy: Dr. rer. nat. Frank Hees Senior Advisor: Univ.-Prof. Dr.-Ing. em. Klaus Henning
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(such as hazards). While speed is less of an issue, thoroughness and a minimal foot print to prevent additional damage may be essential. .
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“Problem-based learning (PBL)”. He designed a problem-oriented curriculum for medical students on the basis of the ideas of David Boud [8] and John Dewey [9], and this idea is used in several technical disciplines of today. The objective of PBL is the improvement of skills of acting by using problems that were designed to be real-world questions for a motivating workflow, and to impart social, technical and methodical skills. A further important characteristic is the basic attitude towards learning: learner and educator are equivalent persons regarding technical knowledge and behavioral role. According to Barrows [11] a PBL curriculum has the following characteristics:
student-centered education,
teachers are instructors,
small student groups,
problems form the organizing focus and stimulus for learning,
problems are a vehicle for the development of problem-solving skills.
new information is acquired through self-directed learning.
Figure 1. Instructional Design, modified from [10]
professional qualifications (expertise),
methods and/or media qualifications (methods for the search of approaches),
social skills (social behavior in learning environments and teams),
personal qualifications (development of the personality),
problem solving qualifications and decision-making and responsibility. In regard to Barrows [13] the essential educational aims are designed according to the explicit competencies of the students:
1. Acquisition of well thought-out knowledge regarding the problems. IMA/ZLW & IfU – RWTH Aachen University Institute of Information Management in Mechanical Engineering IMA
Center for Learning and Knowledge Management ZLW Assoc. Institute for Management Cybernetics e.V. IfU
Head of Institute: Univ.-Prof. Dr. rer. nat. Sabina Jeschke 1st Deputy: apl.-Prof. Dr. habil. Ingrid Isenhardt 2nd Deputy: Dr. rer. nat. Frank Hees Senior Advisor: Univ.-Prof. Dr.-Ing. em. Klaus Henning
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A problem-based curriculum is designed to develop the ability of solving problems. Operationalization of learning targets takes place via key skills, and not as in traditional teaching methods in form of knowledge snippets. According to Weber [12], the following key skills are important in a PBL curriculum:
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2. Improvement of problem solving strategies (reasoning) for real world questions 3. Improvement of skills of self-directed learning and team work. 4. Enhancing students’ motivation. Real-world problems are designed by progressive asking. In addition, students should be solving problems as self-governing as possible. The instructor is a work flow and not a knowledge transfer moderator. Such a procedure for multifaceted problems such is difficult to realize. Therefore Meril [14, 15] suggests integrating the elements of “instructional design” (cf. figure 1). In this way, complex problems are reconstructed by sub problems that are subsequently moderated according to PBL. The IITS at the University of Stuttgart offers a robotics courses for students of all fields, not limited to engineering, called “Robinson Mixed” [16]. The course design emphasizes supervising and mentoring students from non-technological fields and early semesters. Project and team work are an integral part of the concept. Completing the course enables the students not only to design, construct and program autonomous robots, but also qualifies them to teach basic robotics to high school level students. The students have the option to participate in instructor courses licensed by the Fraunhofer Institute as part of the Roberta [17] program. Aim of the course is improving the inter-disciplinarity of students in non-technological, particularly their interest in the technological fields. The course is offered as a regular, elective module credited with ECTS points. It has been held for the first time in the spring of 2009 and has received a positive resonance from the participating students.
3. OUTLINE OF THE COURSE
Locomotion: which motive system is best suited for the environment to be expected and the task to be fulfilled? The decision will be influenced by considerations ranging from speed and size of the final system to ruggedness and range. Each task will typically require a different compromise between these parameters. Reconnaissance capability might be best provided by aerial robots (UAV) or unmanned surface vehicles (USV, basically unmanned boats) as large areas have to be covered and the robot’s payload is nothing but a sensor suite, while search might require small serpentine platforms to navigate narrow spaces and rubble removal might be best served by a large, powerful bulldozer-like tracked unit.
Manipulation: Several tasks like rubble removal, evacuation of casualties, or in-situ medical intervention might require manipulative capabilities of the robot to place sensors or move objects. The control of such a manipulator will lead to the problem of direct and inverse
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Head of Institute: Univ.-Prof. Dr. rer. nat. Sabina Jeschke 1st Deputy: apl.-Prof. Dr. habil. Ingrid Isenhardt 2nd Deputy: Dr. rer. nat. Frank Hees Senior Advisor: Univ.-Prof. Dr.-Ing. em. Klaus Henning
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The students will be given the description of a disaster scenario such as a landslide, an area hit by a hurricane or earthquake, an industrial accident or a destroyed building. They will be asked to devise a plan of actions to safely evacuate the survivors of the disaster. This will lead to the identifying the task of reconnaissance and mapping as the starting point of the operation. The students are supposed to design and, if possible build, a robot or a team of robots to fulfill that task, with the scenario providing a natural transition and motivation to the next task and problem. Restricting ourselves to robotics, each task will usually have to address, amongst others, the following topics, under the specific restrictions and requirements of the task at hand:
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Sensing: SAR operations are likely to involve the full spectrum of sensors available in robotics. Visual systems such as high resolution cameras will provide a general overview of the area or visual data from the inside of a building. Carbon-dioxide sensors can help in locating trapped persons, while a GPS can be used to help localize the robot (outside of screening structures) for high resolution mapping, with acceleration sensors or gyroscopes providing inertial navigation capabilities inside structures. Acoustical and seismic sensors are able to detect shifts in the rubble caused by buried victims or unstable structures.
Single-Robot Control: Many existing SAR robots are remote controlled, either through wireless communication or a fiber-optic cable, doubling as a safety tether to retrieve the robot if it gets stuck. The human operator can provide far more complex and advanced decisions and reactions than most autonomous systems possess. However, direct control is not always possible, particularly in badly damaged collapsed structures where steel static components disrupt wireless data exchange while rugged surface and tight confines would tear a cable. Even under direct control of a human, cybernetic control systems can be used to fulfill sub-tasks such as moving in a straight line over very uneven surfaces with strongly varying traction [18]. Very simple behavior-based autonomous robots can fulfill very complex tasks without outside supervision if operating as a swarm [19], making full autonomy possible under certain circumstances. How autonomous can/should the robot be?
Robot teams and team control: Cooperating teams of robots can provide capabilities beyond those of the single members. A UAV-USV team was used to inspect the damage of seawalls and bridges at Marco Island, Florida following the hurricane Wilma [20], with the aerial vehicle providing global positioning to the surface vehicle inspecting underwater damage. Teams of robots, especially heterogeneous teams, require control and coordination. Depending on the task and the capabilities of the robots, centralized, hierarchical or decentralized control architectures are best suited. The tasks to be solved have to be allocated and might require communication between the robots.
Communication and data exchange: Tasks like search or medical assessment might require direct data transfer between robot and human operators, or between robots. The students will have to decide what data has to be exchanged, if real-time capabilities are necessary (direct control commands or remote access to the camera on the robot for remote navigation) or if asynchronous communication is sufficient (still images taken of a large area in reconnaissance). The course can be easily extended beyond the scope of robotics, adding problems from logistics and crisis management.
4. TOPICS TO BE COVERED IN THE PROJECT In addition to the more generic aspects of robot design, each of the tasks defined above addresses a number of relevant topics within the field of robotics. The tasks defined in section 1.1 have a certain hierarchical order, adding new sub-fields of robotics and expanding in complexity those topics already touched upon in the previous tasks (cf. figure 2). As such, their order supports the natural work flow of the PBL-course as a whole. Reconnaissance requires far less complex sensor suits than structural inspection or medical assessment but can already be used to introduce the more basic topic of sensing and IMA/ZLW & IfU – RWTH Aachen University Institute of Information Management in Mechanical Engineering IMA
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kinematics. Design decisions have to be based on the requirements, as delicate operations or brute force tasks require very different systems.
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estimation. The control architecture for searching is less complex than that for human surrogate and remote control. This progression of tasks and the refinement of knowledge and skills to fulfill them keeps the learners' motivation and interest up, while providing comprehensive coverage of all sub fields where desired. The following will provide two selected examples from the list of tasks defined in Chapter 1.1.
Figure 2: Topics and Tasks
4.1
Reconnaissance and Mapping
Requires:
Motive systems: (relatively compared to search) fast, but less reliable motive systems to cover large areas. These would typically include aerial robots and unmanned surface vehicles (boats).
Sensors: Sensor suits for this task range from mapping radar to visual and IR-systems. Sensing and estimation (creating an internal representation of the surroundings based on the available imperfect and often noisy sensor data) play a major role in this. This will introduce a number of methods and theory of statistic data analysis (e.g. Kalman-Filter [21])
Additional theory: Reconnaissance and mapping is closely related to the task of “Simultaneous Localization and Mapping” (SLAM) [22, 23], the ability of a robot to build up an internal representation of the world, the map, and localizing itself within this world, while exploring it.
4.2
On-Site Surrogates for off-site specialists
Motive systems: Since this group of tasks will usually involve direct, close deployment within the actual disaster area, the motive systems will be very robust in design, typically tracked, legged or serpentine systems. Uneven and slippery surfaces and lack of external localization require precise motion control while complex pathways caused by blocked passages require motion planning [24] in support of the human operator.
Manipulation: Many remote controlled tasks require some sort of manipulative capability of the robot, typically a multi-jointed arm. Control of this arm requires knowledge of motion control [24] and an understanding of kinematics [25].
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Requires:
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Remote Control: Humans often require some sort of feedback from the robot to operate the manipulators in particular effectively and efficiently [26]. At the same time, a balance has to be struck between autonomy of the robot to reduce the work load of the operator and ensure safe operation independent of the control input, and the direct control of the operator over the robot's behavior, leading to the theory of robotic control architectures [27].
5. SUMMARY AND CONCLUSIONS Rescue scenarios provide a well-suited background for a PBL project in robotics. The obvious social relevance enhances the motivation of students, while the technical challenges serve as natural entry points into any sub-fields of robotics. At the same time, the single tasks associated with rescue scenarios can be sufficiently focused to prevent overloading the students, providing a high degree of flexibility. The course described fits well into the existing framework of interdisciplinary robotics courses already taught at the IITS at the University of Stuttgart, Germany, in the “Robinson” programme.
6. REFERENCES [1] A. Davids, “Urban search and rescue robots: from tragedy to technology”, Intell. Syst. IEEE 17 (2), 81-83, pp. 1541–1647 (2002). [2] R.R. Murphy: “Trial by fire”, IEEE Robot. Autom. Mag. 11(3), pp. 50–61 (2004). [3] pp. 5-18, Overseas Development Institute, (2009). [4] Intergovernmental Panel on Climate Change, 4th Assessment Report, (2007). [5] R.R. Murphy, S. Tadokoro, D. Nardi, A. Jacoff, P. Fiorini, H. Choset, A.M. Erkman, “Search and rescue robotics, Springer handbook of robotics”, Springer, Berlin/Heidelberg (2008). [6] J.A. Barbera, C. DeAtley, A.G. Macintyre: “Medical aspects of urban search and rescue”, Fire Eng. 148, pp. 88–92 (1995). [7] H.S. Barrows, R.M. Tamblyn, “Problem-based learning: an approach to medical education”, Springer Publishing Company, New York (1980). [8] D. Boud, G. Feletti, “The challenge of problem-based-learning”, Routledge, 2. Ed. (1998). [9] J. Dewey, “Democracy and education”, Free Press, Original from The Macmillan Company (1916, 1941, 1997). [10] J.J.G. van Merriënboer, Th. Bastiaens and B. Hoogveld, “Instructional design for integrated e-learning”, in W. Jochems, J. van Merriënboer and R. Koper (Eds.), “Integrated e-Learning” London, UK: Kogan Page, p.15 (2004). [11] H.S. Barrows, “Problem-based learning in medicine and beyond: a brief overview”, in: L.G. Wilkerson, H. Wim, “Bringing problem-based learning to higher education: theory and practice.”, Jossey-Bass Publishers, San Francisco (1996).
[13] H.S. Barrows, “Problem-based learning applied to medical education.”, Rev. 1994 Ed. Springfield, Southern Illinois University School of Medicine (2000). [14] M.D. Merrill, “A pebble-in-the-pond model for instructional design”, Performance Improvement 41 (7): 39–44. doi:10.1002/pfi.4140410709 (2002). [15] M.D. Merrill, “A task centered instructional strategy”, Journal of Research on Technology in Education, 40(1), pp. 33–50 (2007). [16] S. Jeschke, L. Knipping, N. Natho, U. Vollmer, M. Wilke „The “Robinson” Programme: Interdisciplinary Education based on Robotics Curricula”, in X. Du, E. de Graaff and A. Kolmos (Eds.) “Research on PBL Practice in Engineering Education”, Sense Publishers, Rotterdam, pp. 185–198 (2009). [17] St. Augustin Fraunhofer-Institut für Autonome intelligente Systeme AIS, “Roberta - Anleitung zur Schulung von RobertaKursleiterinnen und Kursleitern”, vol. 5, IRB Verlag (2006). [18] C. Canudas de Wit, “Theory of robot control”, Springer, London (1996).
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Head of Institute: Univ.-Prof. Dr. rer. nat. Sabina Jeschke 1st Deputy: apl.-Prof. Dr. habil. Ingrid Isenhardt 2nd Deputy: Dr. rer. nat. Frank Hees Senior Advisor: Univ.-Prof. Dr.-Ing. em. Klaus Henning
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[12] A. Weber, “Problem-Based Learning. Ein Handbuch für die Ausbildung auf der Sekundarstufe II und der Tertiärstufe“, h.e.p, Verlag Bern (2004).
Page 9 [19] C.R. Kube, H. Zhang, “Collective robotics: From social insects to robots”, Adapt. Behav. 2(2), pp. 189–219 (1993). [20] R. Murphy, E. Steimle, C. Cullins, K. Pratt, C. Griffin, “Cooperative damage inspection with unmanned surface vehicle and micro aerial vehicle at hurricane Wilma”, IEEE/RSJ International Conference on Intelligent Robots and Systems (video proceedings), Beijing, IEEE Press (2006). [21] D. Simon, “Optimal state estimation: kalman, H infinity and non-linear approaches”, Wiley, New York (2006). [22] H. Durrant-Whyte, T. Bailey, “Simultaneous localization and mapping: part I”, IEEE Robot. Autom. Mag., pp. 99–108 (2006). [23] T. Bailey, H. Durrant-Whyte, “Simultaneous localization and mapping: part II”, IEEE Robot. Autom. Mag. 2006, pp. 108–117 (2006). [24] C. Canudas de Wit, B. Siciliano, G. Bastin, “Theory of robot control”, Springer, London (1996). [25] L. Sciavicco, B. Siciliano, “Modelling and control of robot manipulator”, McGraw-Hill, New York (1996). [26] M.J. Massimo, T.B. Sheridan, “Sensory substitution for force feedback in teleoperation”, Presence Teleoper. Virtual Environ. 2(4), pp. 344–352 (1993). [27] G. Hirzinger, B. Brunner, J. Dietrich, J. Heindl, “ROTEX – The first remotely controlled robot in space”, Proc. IEEE Int. Conf. Robot. Autom., Vol 3, San Diego, pp. 2604–2611 (1994). [28] R.R. Murphy, “Marsupial robots in urban search and rescue”, IEEE Intell. Systems 15(2), pp. 14–19, (2000).
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[29] Gonzalez-Banos, H.H., Hsu, D., Latombe, J.C., Motion Planning: Recent Developments, Autonomous Mobile Robots: Sensing, Control, Decision-Making and Applications, ed. Ge, S.S., lewis, F.L., CRC, Bota Racon (2006).
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