[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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302 T. Czapla and J. Wrona FLIR and daylight camera for target acquisition. Surveillance system is mounted on a mast. The vehicle is equipped with series hybrid electric drive system with power station containing 60 kW diesel engine charging lithium-ion batteries and electric propulsion motors. Vehicles operation modes could vary from teleoperated to autonomous. Technical data are summarized in Table 5. Table 5. Technical data of Crusher vehicle Weight 5987 kg Length x Width x Height 5.1 x 2.59 x 1.52 m Engine power 60 kW Max speed 42 km/h Endurance Gradient/side slope 40°/- Vertical step >1,22 m Trench >2.03 m Fording Mode of operation Tele-operated, autonomous 3.6 Gladiator Gladiator is a wheeled multipurpose unmanned ground vehicle currently developed at Carnegie Mellon University (National Robotics Engineering Center, Gladiator). It is designated to perform combat, logistic, mine clearing and reconnaissance missions. Platform is supposed to accomplish its missions with using various sensors: daylight and infrared cameras, laser rangefinder, acoustic sensors and GPS. Vehicle will be able to use M240G 7.63 machine gun and M249 5.56 mm squad automatic weapon for direct fire. As a transport platform it will carry weapons, ammunition, supplies and wounded personnel. Technical data of the Gladiator vehicle are shown in Table 7. Table 6. Technical data of Gladiator vehicle Weight Length x Width x Height Engine power Max speed Endurance Gradient/side slope Vertical step Trench Fording Mode of operation 1270 kg 2.03 x 1.29 x 1.52 m 33 km/h 24 h including 8 h travel and 16 h stationary surveillance Tele-operated, autonomous
Technology Development of Military Applications of Unmanned Ground Vehicles 303 3.7 MDARS Mobile Detection Assessment Response System (MDARS) was developed by General Dynamics Robotic Systems. The platform remains in service with the U.S. Department of Energy and the U.S. Army. MDARS can be tele-operated and work in autonomous mode, up to 16 vehicles can be controlled by a single operator. The vehicle is equipped with all-around obstacle avoiding system (General Dynamics Robotic Systems). Technical data of the vehicle are provided in Table 7. Fig. 9. Unmanned ground vehicles: a) ARTS, b) Crusher (sources: U.S. Army, Hightech- Edge) Table 7. Technical data of MDARS vehicle Weight Length x Width x Height Engine power Max speed Endurance Gradient/side slope Vertical step Trench Fording Mode of operation 1360 kg 2.92 x 1.64 x 2.62 m 32.1 km/h 16 h Tele-operated, autonomous 3.8 MULE Multifunctional Utility/Logistics & Equipment Armed Robotic Vehicle - Assault (Light) (MULE ARV-A(L)) is a multipurpose UGV currently developed by Lockheed Martin Missiles and Fire Control Systems (Global Security). The MULE complies with Future Combat System and has three variants: transport version (MULE-T), an Armed Robotic Vehicle - Assault (Light) (MULE ARV-A(L)), and a counter-mine vehicle (MULE-C). Since the FCS program was canceled in 2009, only the MULE ARV-A(L) is under development. Vehicle has in-hub mounted propulsion electric motors on each of six wheels. MULE can be equipped with an
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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16 A. Babiarz et al. the camera ser
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18 A. Babiarz et al. a) b) Fig. 11.
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20 A. Babiarz et al. patrol point i
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Recognition and Location of Objects
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48 P. Demski et al. Fig. 1. Automat
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52 P. Demski et al. 4 Automatic Tar
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54 P. Demski et al. implementing su
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Object Tracking for Rapid Camera Mo
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Object Tracking in a Picture during
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94 Construction of Image Acquisitio
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96 G. Bieszczad et al. effectivenes
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98 G. Bieszczad et al. such a way t
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100 G. Bieszczad et al. Table 1. No
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114 G. Bieszczad et al. [11] Krupi
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116 D. Bereska et al. Presented in
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118 D. Bereska et al. Fig. 2. Image
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120 D. Bereska et al. Fig. 4. Ortho
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Omnidirectional Video Acquisition D
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Part III Design of Vision Based Con
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Vision System for Group of Mobile R
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A Distributed Control Group of Mobi
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178 D. Bereska et al. The aim of th
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180 D. Bereska et al. 3 Control Alg
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182 D. Bereska et al. Fig. 5. Schem
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184 D. Bereska et al. 4.1 Mechanica
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186 D. Bereska et al. 4.1.3 Open-Lo
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188 D. Bereska et al. Fig. 16. Comp
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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Prototyping the Autonomous Flight A
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Feature Extraction and HMM-Based Cl
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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