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Journal articles on the topic 'Unmanned ground vehicle'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

Juhari, Khairul Anuar, Mohd Rizal Salleh, Mohd Nazrin Muhamad, and Teruaki Ito. "208 NAVIGATION SYSTEM FOR UNMANNED GROUND VEHICLE." Proceedings of Manufacturing Systems Division Conference 2013 (2013): 53–54. http://dx.doi.org/10.1299/jsmemsd.2013.53.

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2

Hay, A., C. Samson, L. Tuck, and A. Ellery. "Magnetic surveying with an unmanned ground vehicle." Journal of Unmanned Vehicle Systems 6, no. 4 (December 1, 2018): 249–66. http://dx.doi.org/10.1139/juvs-2018-0013.

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With the recent proliferation of unmanned aerial vehicles for geophysical surveying, a novel opportunity exists to develop unmanned ground vehicles in parallel. This contribution features a study to integrate the Husky A200 robotic development platform with a GSMP 35U magnetometer that has recently been developed for the unmanned aerial vehicle market. Methods to identify and reduce the impact of magnetically noisy components on the unmanned ground vehicle platforms are discussed. The noise generated by the platform in laboratory and gentle field conditions, estimated using the fourth difference method for a magnetometer–vehicle separation distance of 121 cm and rotation of the chassis wheels at full speed (1 m/s), is ±1.97 nT. The integrated unmanned ground vehicle was used to conduct two robotic magnetic surveys to map cultural targets and natural variations of the magnetic field. In realistic field conditions, at a full speed of 1 m/s, the unmanned ground vehicle measured total magnetic intensity over a range of 1730 nT at 0.1 m spatial resolution with a productivity of 2651 line metres per hour.
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Peterson, John, Weilin Li, Brian Cesar‐Tondreau, John Bird, Kevin Kochersberger, Wojciech Czaja, and Morgan McLean. "Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search." Journal of Field Robotics 36, no. 4 (March 12, 2019): 818–45. http://dx.doi.org/10.1002/rob.21867.

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4

Typiak, Andrzej, and Michał Gnatowski. "Map Building System for Unmanned Ground Vehicle." Solid State Phenomena 180 (November 2011): 131–36. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.131.

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A typical unmanned and remotely operated vehicle is usually equipped with cameras which give insufficient information about the nearest environment and an operator has difficulties in driving such a vehicle in unknown environment. In this paper, we consider a problem of the vehicle nearest area map building based on additional devices. The vehicle is equipped with SICK LMS lasers, inclinometer and radars. Combining information from the devices allows to build a map which helps an operator to drive the vehicle more efficiently. We tested the system on a few military vehicles and the results show that our system really improves remotely driving.
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5

Biswas, Tanmoy, Arup Kr Goswami, Manas Pal, and Saptarshi Naskar. "SMS Controlled Unmanned Ground Vehicle." International Journal of Computer Sciences and Engineering 6, no. 12 (December 31, 2018): 847–54. http://dx.doi.org/10.26438/ijcse/v6i12.847854.

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6

Fernandez, S. George, K. Vijayakumar, R. Palanisamy, K. Selvakumar, D. Karthikeyan, D. Selvabharathi, S. Vidyasagar, and V. Kalyanasundhram. "Unmanned and autonomous ground vehicle." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4466. http://dx.doi.org/10.11591/ijece.v9i5.pp4466-4472.

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Unmanned and Autonomous Ground Vehicle (UAGV) is a smart vehicle that capable of doing tasks without the need of human operator. The automated vehicle can work during off and on road navigation and also used in military operation such as detecting bombs, border patrol, carrying cargos, search, rescue etc reducing soldier’s exposure to danger, freeing them to perform other duties. This type of vehicle mainly uses sensors to observe the environment and automatically take decisions on its own in unpredictable situation and with unknown information or pass this information to the operator who control the UAGV through various communication when it requires support. This UAGV can send visual feedbacks to the operator at the ground station. An onboard sensor gives the complete environment of the vehicle as signals to the operator.
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Chang, Bao Rong, Hsiu-Fen Tsai, Jyong-Lin Lyu, and Chien-Feng Huang. "Distributed sensing units deploying on group unmanned vehicles." International Journal of Distributed Sensor Networks 17, no. 7 (July 2021): 155014772110368. http://dx.doi.org/10.1177/15501477211036877.

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This study aims to use two unmanned vehicles (aerial vehicles and ground vehicles) to implement multi-machine cooperation to complete the assigned tasks quickly. Unmanned aerial/ground vehicles can call each other to send instant inquiry messages using the proposed cooperative communication protocol to hand over the tasks between them and execute efficient three-dimensional collaborative operations in time. This study has demonstrated integrating unmanned aerial/ground vehicles into a group through the control platform (i.e. App operation interface) that uses the Internet of Things. Therefore, pilots can make decisions and communicate through App for cooperative coordination, allowing a group of unmanned aerial/ground vehicles to complete the tasks flexibly. In addition, the payload attached to unmanned air/ground vehicles can carry out multipurpose monitoring that implements face recognition, gas detection, thermal imaging, and video recording. During the experiment of unmanned aerial vehicle, unmanned aerial vehicle will plan the flight path and record the movement trajectory with global positioning system when it is on duty. As a result, the accuracy of the planned flight path achieved 86.89% on average.
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8

Liu, Qi, Zirui Li, Shihua Yuan, Yuzheng Zhu, and Xueyuan Li. "Review on Vehicle Detection Technology for Unmanned Ground Vehicles." Sensors 21, no. 4 (February 14, 2021): 1354. http://dx.doi.org/10.3390/s21041354.

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.
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9

Bhandari, Renuka, Vijaya Khati, Sangita Yadav, and Moni Kumari. "Wireless Network for Unmanned Ground Vehicle." INTERNATIONAL JOURNAL OF RESEARCH IN ADVANCE ENGINEERING 2, no. 3 (May 11, 2016): 21. http://dx.doi.org/10.26472/ijrae.v2i3.51.

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10

Zhang, Xin, Yan An Zhao, Li Gao, and Dong Hao Hao. "Evaluation Framework and Method of the Intelligent Behaviors of Unmanned Ground Vehicles Based on AHP Scheme." Applied Mechanics and Materials 721 (December 2014): 476–80. http://dx.doi.org/10.4028/www.scientific.net/amm.721.476.

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In order to provide basis and standards to the research on unmanned driving behaviors, a more thorough evaluation system of Intelligent Behavior for Unmanned Ground Vehicles needs to come forward. The intelligent behavior of unmanned ground vehicles in pedestrian crossing scenario is taken as an example in this paper. By using building and analyzing evaluation index system, this paper proposes an evaluation method that can comprehensively expressed the technological performance of unmanned ground vehicles based on Analytic Hierarchy Process (AHP). Compared with traditional methods, this evaluation method takes index weight into sufficient conderations and is more objective. The method properly works out with index weight of specific scenarios that reflects the actual situations. The establishment of comprehensive scoring method objectively and conveniently turns the performances of unmanned ground vehicle into scores, so that the results can be compared and ranked directly. Last but not least, a certain participating vehicle is used for case study. The result proves aforementioned method to be practical, reliable, convenient and logical. It not only evaluates the assessment comprehensively, but also evaluates the index separately to guide researchers to find out the defects of unmanned driving vehicle evaluation indexes and point out ways to improve them.
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11

Liang, Xiao, Guodong Chen, Shirou Zhao, and Yiwei Xiu. "Moving target tracking method for unmanned aerial vehicle/unmanned ground vehicle heterogeneous system based on AprilTags." Measurement and Control 53, no. 3-4 (January 7, 2020): 427–40. http://dx.doi.org/10.1177/0020294019889074.

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Using the characteristics of unmanned aerial vehicle/unmanned ground vehicle, heterogeneous systems can accomplish many complex tasks cooperatively. Moving target tracking is an important basis for the relative positioning and formation maintenance of heterogeneous cooperative systems. This paper first introduces the unmanned aerial vehicle/unmanned ground vehicle collaborative tracking task and heterogeneous system. In order to maintain the original stability of unmanned aerial vehicle, a control method based on SBUS protocol to simulate remote control is proposed. About unmanned ground vehicle with Mecanum wheel, a detailed description of control method is designed. For the problems of real-time performance and occlusion, a tracking scheme based on AprilTag identification is studied. The scheme tracks the Tag target in the case of no occlusion. When occlusion occurs, the scheme tracks the color feature around the Tag. The accuracy of the tracking algorithm and the problem of occlusion are greatly improved. Finally, the scheme is applied to the heterogeneous systems. Simulation and experimental results show that the proposed method is suitable for unmanned aerial vehicle/unmanned ground vehicle heterogeneous system to perform the collaborative tracking task.
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12

Li, Xin, Guang Ming Xiong, Yang Sun, Shao Bin Wu, Jian Wei Gong, Hui Yan Chen, and Li Gao. "Design on Hierarchical Testing System for Unmanned Ground Vehicles." Advanced Materials Research 346 (September 2011): 817–22. http://dx.doi.org/10.4028/www.scientific.net/amr.346.817.

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The test system for technical abilities of unmanned vehicles is gradually developed from the single test to comprehensive test. The pre-established test and evaluation system can promote the development of unmanned ground vehicles. The 2009 Future Challenge: Intelligent Vehicles and Beyond (FC’09) pushed China's unmanned vehicles out of laboratories. This paper proposed to design a more scientific and comprehensive test system for future competitions to better guide and regulate the development of China's unmanned vehicles. According to the design idea of stage by stage and level by level, the hierarchical test content from simple to advanced, from local to overall is designed. Then the hierarchic test environment is established according to the levels of test content. The test method based on multi-platform and multi-sensor is put forward to ensure the accuracy of test results. The testing criterion framework is set up to regulate future unmanned vehicle contests and to assess the unmanned vehicles scientifically and accurately.
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13

Kang, David S., Jamie M. Anderson, and Paul A. DeBitetto. "Draper unmanned vehicle systems." Robotica 18, no. 3 (May 2000): 263–72. http://dx.doi.org/10.1017/s0263574799002246.

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Draper Small Autonomous Aerial Vehicle (DSAAV), MITy and SMART micro-rovers, Companion mini-rover, and Vorticity Control Unmanned Undersea Vehicle (VCUUV) are highlighted. DSAAV demonstrated autonomy with GPS/INS integration and vision processing. The micro- and mini-rovers investigated ground based autonomy with extensive mapping and planning integration. VCUUV is a flexible-hull UUV which propels and maneuvers like a tuna.
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14

Bis, Rachael, Huei Peng, and A. Galip Ulsoy. "Vehicle occupancy space for unmanned ground vehicles with actuation error." International Journal of Vehicle Autonomous Systems 12, no. 2 (2014): 180. http://dx.doi.org/10.1504/ijvas.2014.060115.

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15

Vantsevich, Vladimir V., Jeremy P. Gray, and Dennis Murphy. "Fused Dynamics of Unmanned Ground Vehicle Systems." SAE International Journal of Commercial Vehicles 7, no. 2 (September 30, 2014): 406–13. http://dx.doi.org/10.4271/2014-01-2322.

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16

Deng, Da Wei, and Bao An Li. "Large Unmanned Aerial Vehicle Ground Testing System." Applied Mechanics and Materials 719-720 (January 2015): 1244–47. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.1244.

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The traditional high altitude long endurance UAV ground testing system (referred to as: testing system) not only lacks of universality and scalability, also incapable of handling faculties during flying procedure. In the paper, by using the new generation of automatic test system (New ATS), the flight simulation method, embedded technology and fault diagnosis technology, we are able to expand the testing system from ground to the sky, improving the generality of the system and UAV’s testing ability, also ensure the safety and reliability of the UAV flight at the same time.Keywords: Unmanned aerial vehicle; Automatic test system;Fault diagnosis; Embedded bus
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17

Sung, Changhyun, Minho Son, Minkyu Choi, and Jihun Kim. "Development of Small Unmanned Ground Vehicle Platform." Transaction of the Korean Society of Automotive Engineers 28, no. 2 (February 1, 2020): 117–23. http://dx.doi.org/10.7467/ksae.2020.28.2.117.

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18

Fualdes, Thierry D., and Claude J. Barrouil. "Perception planning for an unmanned ground vehicle." Robotics and Autonomous Systems 11, no. 2 (September 1993): 87–96. http://dx.doi.org/10.1016/0921-8890(93)90012-2.

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19

Lam Loong Man, Christopher Kwet Young, Yogesh Koonjul, and Leckraj Nagowah. "A low cost autonomous unmanned ground vehicle." Future Computing and Informatics Journal 3, no. 2 (December 2018): 304–20. http://dx.doi.org/10.1016/j.fcij.2018.10.001.

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20

Shuang, Xuecheng, and Yan Zhang. "Design of Unmanned Wing-in-ground Effect Vehicle with Tri-fold Main Wing." E3S Web of Conferences 248 (2021): 02062. http://dx.doi.org/10.1051/e3sconf/202124802062.

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This paper proposed an innovative design of unmanned wing-in-ground effect vehicle with tri-fold main wing, aiming at optimizing the main wing of unmanned vehicles based on its current application. The design also takes into account the speed and load in an effort to improve efficiency and broaden the application of the vehicle. In addition, the unmanned GEV is driven by electric power and propelled by the main and auxiliary propellers, which is well supported by the ground effect. The design features energy conservation and emission reduction, which is worthy of promoting in the market.
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21

Kopyt, Antoni, Janusz Narkiewicz, and Paweł Radziszewski. "An unmanned aerial vehicle optimal selection methodology for object tracking." Advances in Mechanical Engineering 10, no. 12 (December 2018): 168781401881408. http://dx.doi.org/10.1177/1687814018814085.

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In this article, an optimal method of unmanned aerial vehicle selection for following a moving ground target is presented. The most suitable unmanned aerial vehicle from an aircraft operating fleet is selected for tracking task. The aircraft choice is done taking regarding the unmanned aerial vehicle distribution in the mission zone, individual performance of fleet platforms, and maneuverability of a ground platform. The unmanned aerial vehicle fleet members’ flying qualities are various, so an optimization process is used to assign an aircraft to follow the ground target. A target trajectory prediction is embedded in tracking algorithm. The simulation results demonstrated the effectiveness of algorithms developed, and in-flight demonstration proved the possibility of realization of computed trajectories by the unmanned aerial vehicle.
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22

Prajwal Shenoy, T., K. Praveen Shenoy, Lukhman Khan, Sabdar Aziz, Sayed Afran, and Kamlesh Kumar. "Design and development of a novel triphibian quadcopter." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.21.11822.

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Multimodal Vehicles can travel in at least two distinctive modes of transportation, be it air and water, land and water or air and land. In this paper, design and development of a novel unmanned multimodal vehicle is presented that can travel in air, land and water, also called a as a triphibian quadcopter which would prove beneficial in rescue operations and operations involving hazardous environments. The vehicle is essentially a mix of unmanned aerial vehicle, unmanned ground vehicle and unmanned surface vehicle and is equipped with mechanisms to travel in land, air and water at the will of the operator. The entire framework of the vehicle is built on the multi rotor configuration. Flight tests were conducted to validate the design.
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23

Strawa, Natalia, Dmitry I. Ignatyev, Argyrios C. Zolotas, and Antonios Tsourdos. "On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics." Electronics 10, no. 2 (January 15, 2021): 187. http://dx.doi.org/10.3390/electronics10020187.

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Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.
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24

Zhao, Taifei, Hua Wang, and Qianwen Ma. "The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772092022. http://dx.doi.org/10.1177/1550147720920220.

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The unmanned aerial vehicle features with high flexibility and easy deployment. It could be used as an air base station and provide fast communication services for the ground users. It plays an important role in some special occasions such as natural disasters, emergency communications and temporary large-scale activities. A single unmanned aerial vehicle equipped with base station has limited range of services, but a multiple unmanned aerial vehicle equipped with base station network can serve a wider range of users. The research goal of unmanned aerial vehicle equipped with base station network coverage control is to maximize the network coverage under the condition of maintaining the service quality. In view of the low dynamic coverage ratio of unmanned aerial vehicle equipped with base station network, this article proposes a relative distance–based unmanned aerial vehicle equipped with base station deployment method. The unmanned aerial vehicle realizes on-demand coverage and maintains a stable network topology under the influence of three relative distances by sensing the uncovered area of the ground, the neighbouring unmanned aerial vehicles and the location of the coverage boundary or obstacles. In addition, the algorithm is also adapted to a variety of scenarios. The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.
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25

Shamsuddin, Putri Nur Farhanah Mohd, Roshahliza M. Ramli, and Muhamad Arifpin Mansor. "Navigation and motion control techniques for surface unmanned vehicle and autonomous ground vehicle: a review." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 1893–904. http://dx.doi.org/10.11591/eei.v10i4.3086.

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An excellent navigation, guidance, and control (NGC) system had a high impact on trajectory tracking and the following scenarios. Both scenarios will include the heading, tangent, and velocity parameters in the computation. However, the control system design problem is not a new issue in the unmanned surface vehicle (USV) and autonomous ground vehivle (AGV) due to this constraint faced by many researchers since early these autonomy developments. Hence, this paper listed and emphasizing the techniques, including techniques implementation, strength, and the algorithm's constraints, a fusion of several techniques implemented for vehicle's stability, a turning ahead, and heading estimation. This paper concerns the similar algorithm used in the USV and AGV. Most of the selected techniques are basic algorithms and have been frequently implemented to control both vehicles' systems. Previous research shows pure pursuit guidance is the most popular technique in AGV to control the degree-of-freedom (DOF) velocity and the dynamic rate (sway, surge, and yaw). Simultaneously, the line of sight (LOS) controller is very compatible with controlling the movement of the USV. In conclusion, the technique's simulation test needs further research that will expose in the actual situation.
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26

Ali, Zain Anwar, and Xinde Li. "Modeling and controlling of quadrotor aerial vehicle equipped with a gripper." Measurement and Control 52, no. 5-6 (April 16, 2019): 577–87. http://dx.doi.org/10.1177/0020294019834040.

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Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle.
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27

Li, Linpei, Xiangming Wen, Zhaoming Lu, and Wenpeng Jing. "An Energy Efficient Design of Computation Offloading Enabled by UAV." Sensors 20, no. 12 (June 13, 2020): 3363. http://dx.doi.org/10.3390/s20123363.

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The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.
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28

Sell, Raivo, and Priit Leomar. "Universal Navigation Algorithm Planning Platform for Unmanned Systems." Solid State Phenomena 164 (June 2010): 405–10. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.405.

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The paper deals with route planning and message exchange platform development for unmanned vehicle systems like Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicle (UAV). Existing solutions for both types of vehicles are discussed and analyzed. Based on existing solution an unified concept is introduced. In this paper we present the study where the universal navigation algorithm planning platform is developed aiming to provide common platform for different unmanned mobile robotic systems. The platform is independent from the application and the target software. The navigation and action planning activity is brought to the abstract layer and specific interfaces are used to produce the target oriented code, describing two different test platforms are presented and co-operation scenarios.
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Väljaots, Eero, Raivo Sell, and Mati Kaeeli. "Motion and Energy Efficiency Parameters of the Unmanned Ground Vehicle." Solid State Phenomena 220-221 (January 2015): 934–39. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.934.

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The paper investigates the data acquisition method and a system of wheeled mobile unmanned ground vehicles (UGV) for characterization and optimization of motion and energy efficiency. This enables to conduct real-time and conditional field tests. The obtained results are used for an advanced methodology framework for robotic design targeted on the development, simulation and testing of vehicle platforms along the entire design process.
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30

Akopov, A. S., N. K. Khachatryan, L. A. Beklaryan, and A. L. Beklaryan. "UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 2. FUZZY CLUSTERING AND SOFTWARE IMPLEMENTATION." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 196 (October 2020): 21–29. http://dx.doi.org/10.14489/vkit.2020.10.pp.021-029.

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This article continues the description of the control system for ground unmanned vehicles as part of the integration of a phenomenological approach to modeling the behavior of agents and methods of fuzzy clustering in order to improve the quality of decisions. As a result, adaptive fuzzy clustering methods provide support for adaptive ground unmanned vehicles control, which minimizes the risks of accidents (emergencies involving ground unmanned vehicles) and maximizes traffic (total output stream) in conditions of heavy traffic. The second part is devoted to the description of the developed fuzzy clustering algorithm, software implementation and experiments. As a result, within the framework of the developed model of ground unmanned vehicles movement, fuzzy clustering methods are used to ensure the procedure for choosing the most preferable (least dense) lane in conditions of heavy traffic and to support continuous information exchange between ground unmanned vehicles. The software implementation of the developed simulation model in the AnyLogic environment was performed and numerical experiments aimed at analyzing scenarios of the development of the road situation with the participation of the ground unmanned vehicles ensemble were carried out. Various behavioral scenarios of the developed ground unmanned vehicles control system were investigated, and agent clustering was performed for each scenario under consideration. As a result of numerical experiments, the effectiveness of using the proposed fuzzy clustering procedure to assess the density of the road flow and adaptive control and maneuvering of the ground unmanned vehicles is confirmed.
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31

Akopov, A. S., N. K. Khachatryan, L. A. Beklaryan, and A. L. Beklaryan. "UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 2. FUZZY CLUSTERING AND SOFTWARE IMPLEMENTATION." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 196 (October 2020): 21–29. http://dx.doi.org/10.14489/vkit.2020.10.pp.021-029.

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This article continues the description of the control system for ground unmanned vehicles as part of the integration of a phenomenological approach to modeling the behavior of agents and methods of fuzzy clustering in order to improve the quality of decisions. As a result, adaptive fuzzy clustering methods provide support for adaptive ground unmanned vehicles control, which minimizes the risks of accidents (emergencies involving ground unmanned vehicles) and maximizes traffic (total output stream) in conditions of heavy traffic. The second part is devoted to the description of the developed fuzzy clustering algorithm, software implementation and experiments. As a result, within the framework of the developed model of ground unmanned vehicles movement, fuzzy clustering methods are used to ensure the procedure for choosing the most preferable (least dense) lane in conditions of heavy traffic and to support continuous information exchange between ground unmanned vehicles. The software implementation of the developed simulation model in the AnyLogic environment was performed and numerical experiments aimed at analyzing scenarios of the development of the road situation with the participation of the ground unmanned vehicles ensemble were carried out. Various behavioral scenarios of the developed ground unmanned vehicles control system were investigated, and agent clustering was performed for each scenario under consideration. As a result of numerical experiments, the effectiveness of using the proposed fuzzy clustering procedure to assess the density of the road flow and adaptive control and maneuvering of the ground unmanned vehicles is confirmed.
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32

Madhavan, R., and E. Messina. "Intelligent unmanned ground vehicle navigation via information evaluation." Advanced Robotics 20, no. 12 (January 2006): 1375–400. http://dx.doi.org/10.1163/156855306778960608.

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Vandapel, Nicolas, Raghavendra Rao Donamukkala, and Martial Hebert. "Unmanned Ground Vehicle Navigation Using Aerial Ladar Data." International Journal of Robotics Research 25, no. 1 (January 2006): 31–51. http://dx.doi.org/10.1177/0278364906061161.

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34

Sun, Yang, Guang Ming Xiong, Hui Yan Chen, Shao Bin Wu, Jian Wei Gong, and Yan Jiang. "A Cost Function-Oriented Quantitative Evaluation Method for Unmanned Ground Vehicles." Advanced Materials Research 301-303 (July 2011): 701–6. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.701.

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Task driven approach is widely used as an evaluation method for intelligent vehicle, however this approach may lead to many teams using the conservative approach to complete the task. Although the competition task can be completed, it has deviated from the goal of technological development actually. A cost function-oriented quantitative evaluation method is proposed in this study. The time to complete each task and the quality of each indicator are considered in the evaluation method. The cost function-oriented quantitative evaluation method guides the intelligent vehicle’s development in the "low-cost index" (i.e. high technology) direction. The evaluation results in the 2010 Future Challenge: Intelligent Vehicles and Beyond (FC’ 2010) competition showed that the proposed method can quantitatively evaluate the overall technical performance and individual technical performance of unmanned vehicles.
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Xu, Fengtong, Tao Hong, Jingcheng Zhao, and Tao Yang. "Detection and identification technology of rotor unmanned aerial vehicles in 5G scene." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985399. http://dx.doi.org/10.1177/1550147719853990.

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In the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based networks. Therefore, a lot of unmanned aerial vehicle “black flying” incidents have occurred. UAVs are a kind of “low, slow and small” artificial targets, which face enormous challenges in detecting, identifying, and managing them. In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. For a one-rotor unmanned aerial vehicle, the radar echo data are a single-component sinusoidal frequency modulation signal. The echo signal is conjugated first and then is subjected to a short-time Fourier transform, while the micro-Doppler has a double effect. For a multi-rotor unmanned aerial vehicle, the radar echo data are a multi-component sinusoidal frequency modulation signal, the k-order Bessel function base and the signal are used for integral projection processing, which better identifies the micro-Doppler characteristics such as the number of rotors or the rotational speed of each rotor. The noise interference is added to verify that the algorithm has better robustness. The micro-Doppler characteristics of rotor unmanned aerial vehicles are extracted by the above algorithm, and the data sets are built to train the model. Finally, the classification of unmanned aerial vehicle is realized, and the classification results are given. The research in this article provides an effective solution to solve the problem of detecting and identifying unmanned aerial vehicle by 5G millimeter wave radar in the Internet of Things, which has high practical application value.
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Cantieri, Alvaro, Matheus Ferraz, Guido Szekir, Marco Antônio Teixeira, José Lima, André Schneider Oliveira, and Marco Aurélio Wehrmeister. "Cooperative UAV–UGV Autonomous Power Pylon Inspection: An Investigation of Cooperative Outdoor Vehicle Positioning Architecture." Sensors 20, no. 21 (November 9, 2020): 6384. http://dx.doi.org/10.3390/s20216384.

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Realizing autonomous inspection, such as that of power distribution lines, through unmanned aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy and safety of the operation; however, many technical problems, such as those pertaining to the precise positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed strategy, each vehicle provides its position information to the other, which ensures a safe inspection process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating the feasibility of the proposed approach.
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Kim, Mingeuk, Seungjin Yoo, Ji-chul Kim, and Geun-Ho Lee. "Development of Electric Unmanned Ground Vehicle for Aerial-Ground Cooperative Missions." Transactions of the Korean Society of Mechanical Engineers - A 43, no. 11 (November 30, 2019): 829–36. http://dx.doi.org/10.3795/ksme-a.2019.43.11.829.

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Lazna, Tomas, Petr Gabrlik, Tomas Jilek, and Ludek Zalud. "Cooperation between an unmanned aerial vehicle and an unmanned ground vehicle in highly accurate localization of gamma radiation hotspots." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141775078. http://dx.doi.org/10.1177/1729881417750787.

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This article discusses the highly autonomous robotic search and localization of radiation sources in outdoor environments. The cooperation between a human operator, an unmanned aerial vehicle, and an unmanned ground vehicle is used to render the given mission highly effective, in accordance with the idea that the search for potential radiation sources should be fast, precise, and reliable. Each of the components assumes its own role in the mission; the unmanned aerial vehicle (in our case, a multirotor) is responsible for fast data acquisition to create an accurate orthophoto and terrain map of the zone of interest. Aerial imagery is georeferenced directly, using an onboard sensor system, and no ground markers are required. The unmanned aerial vehicle can also perform rough radiation measurement, if necessary. Since the map contains three-dimensional information about the environment, algorithms to compute the spatial gradient, which represents the rideability, can be designed. Based on the primary aerial map, the human operator defines the area of interest to be examined by the applied unmanned ground vehicle carrying highly sensitive gamma-radiation probe/probes. As the actual survey typically embodies the most time-consuming problem within the mission, major emphasis is put on optimizing the unmanned ground vehicle trajectory planning; however, the dual-probe (differential) approach to facilitate directional sensitivity also finds use in the given context. The unmanned ground vehicle path planning from the pre-mission position to the center of the area of interest is carried out in the automated mode, similarly to the previously mentioned steps. Although the human operator remains indispensable, most of the tasks are performed autonomously, thus substantially reducing the load on the operator to enable them to focus on other actions during the search mission. Although gamma radiation is used as the demonstrator, most of the proposed algorithms and tasks are applicable on a markedly wider basis, including, for example, chemical, biological, radiological, and nuclear missions and environmental measurement tasks.
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NGUYEN, DuyHinh, Xiqian WU, Daisuke IWAKURA, and Kenzo NONAMI. "2C11 Autonomous control and Simultaneous Localization and Mapping (SLAM) of Unmanned Ground Vehicle." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _2C11–1_—_2C11–9_. http://dx.doi.org/10.1299/jsmemovic.2010._2c11-1_.

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Armendariz, Saul, Victor Becerra, and Nils Bausch. "Bio-inspired Autonomous Visual Vertical and Horizontal Control of a Quadrotor Unmanned Aerial Vehicle." Electronics 8, no. 2 (February 5, 2019): 184. http://dx.doi.org/10.3390/electronics8020184.

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Near-ground manoeuvres, such as landing, are key elements in unmanned aerial vehicle navigation. Traditionally, these manoeuvres have been done using external reference frames to measure or estimate the velocity and the height of the vehicle. Complex near-ground manoeuvres are performed by flying animals with ease. These animals perform these complex manoeuvres by exclusively using the information from their vision and vestibular system. In this paper, we use the Tau theory, a visual strategy that, is believed, is used by many animals to approach objects, as a solution for relative ground distance control for unmanned vehicles. In this paper, it is shown how this approach can be used to perform near-ground manoeuvres in a vertical and horizontal manner on a moving target without the knowledge of height and velocity of either the vehicle or the target. The proposed system is tested with simulations. Here, it is shown that, using the proposed methods, the vehicle is able to perform landing on a moving target, and also they enable the user to choose the dynamic characteristics of the approach.
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Oh, Hyondong, Seungkeun Kim, Hyo-Sang Shin, Antonios Tsourdos, and Brian A. White. "Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles." International Journal of Systems Science 45, no. 12 (March 4, 2013): 2499–514. http://dx.doi.org/10.1080/00207721.2013.772677.

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42

Fabris, Eduardo Jose, Vicenzo Abichequer Sangalli, Leonardo Pavanatto Soares, and Márcio Sarroglia Pinho. "Immersive telepresence on the operation of unmanned vehicles." International Journal of Advanced Robotic Systems 18, no. 1 (January 1, 2021): 172988142097854. http://dx.doi.org/10.1177/1729881420978544.

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Unmanned ground vehicles are usually deployed in situations, where it is too dangerous or not feasible to have an operator onboard. One challenge faced when such vehicles are teleoperated is maintaining a high situational awareness, due to aspects such as limitation of cameras, characteristics of network transmission, and the lack of other sensory information, such as sounds and vibrations. Situation awareness refers to the understanding of the information, events, and actions that will impact the execution and the objectives of the tasks at the current and near future of the operation of the vehicle. This work investigates how the simultaneous use of immersive telepresence and mixed reality could impact the situation awareness of the operator and the navigation performance. A user study was performed to compare our proposed approach with a traditional unmanned vehicle control station. Quantitative data obtained from the vehicle’s behavior and the situation awareness global assessment technique were used to analyze such impacts. Results provide evidence that our approach is relevant when the task requires a detailed observation of the surroundings, leading to higher situation awareness and navigation performance.
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43

Akopov, A. S., N. K. Khachatryan, L. A. Beklaryan, and A. L. Beklaryan. "UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 1. VEHICLE MOVEMENT MODEL." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 3–12. http://dx.doi.org/10.14489/vkit.2020.09.pp.003-012.

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A control system for ground unmanned vehicles is presented, using fuzzy clustering methods for making decisions at an individual level. A new approach to the management of ground unmanned vehicles has been developed, taking into account the state of vehicles in a dense traffic, in particular, the presence of road accidents, the appearance of traffic congestion (high density clusters), etc. An important advantage of this approach is the description of the rules for the interaction of various agents with each other and the external environment within the framework of the final decision-making system of individual agents without the need for a complex computational procedure for identifying the potentials of various forces of the system as a whole. In particular, such rules can be described using systems of differential equations with a variable structure, taking into account all the variety of possible interactions and collisions (potential collisions) between different (moving or stationary) objects. A key feature of the proposed model is the use of the concept of the radius of the agent’s personal space, which explains the effects of turbulence and crush. In this case, the radius of the agent’s personal space is a function of the density of vehicles. As a result, a model of unmanned vehicle movement is developed.
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44

Akopov, A. S., N. K. Khachatryan, L. A. Beklaryan, and A. L. Beklaryan. "UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 1. VEHICLE MOVEMENT MODEL." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 3–12. http://dx.doi.org/10.14489/vkit.2020.09.pp.003-012.

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A control system for ground unmanned vehicles is presented, using fuzzy clustering methods for making decisions at an individual level. A new approach to the management of ground unmanned vehicles has been developed, taking into account the state of vehicles in a dense traffic, in particular, the presence of road accidents, the appearance of traffic congestion (high density clusters), etc. An important advantage of this approach is the description of the rules for the interaction of various agents with each other and the external environment within the framework of the final decision-making system of individual agents without the need for a complex computational procedure for identifying the potentials of various forces of the system as a whole. In particular, such rules can be described using systems of differential equations with a variable structure, taking into account all the variety of possible interactions and collisions (potential collisions) between different (moving or stationary) objects. A key feature of the proposed model is the use of the concept of the radius of the agent’s personal space, which explains the effects of turbulence and crush. In this case, the radius of the agent’s personal space is a function of the density of vehicles. As a result, a model of unmanned vehicle movement is developed.
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45

Soni, Aakash, and Huosheng Hu. "Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey." Robotics 7, no. 4 (November 1, 2018): 67. http://dx.doi.org/10.3390/robotics7040067.

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Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.
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Chadwick, Roger A., and Skye Pazuchanics. "Spatial Disorientation in Remote Ground Vehicle Operations: Target Localization Errors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (October 2007): 161–65. http://dx.doi.org/10.1177/154193120705100404.

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Operating remote unmanned ground vehicles (UGVs) poses the risk of operator spatial disorientation. While the position of the vehicle can be tracked and displayed on a global map, operators face the difficult task of integrating object (target) information from context limited ground views with a global map view. Studies indicate this can be a challenging task. An analysis of specific object location errors is presented.
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Yamauchi, Genki, Ken Akiyama, and Keiji Nagatani. "Development of Volcano Observation System That Consists of Unmanned Aerial Vehicle and Tele-operated Unmanned Ground Vehicle." Journal of the Robotics Society of Japan 34, no. 3 (2016): 220–25. http://dx.doi.org/10.7210/jrsj.34.220.

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48

Liu, Sheng, Jingxiang Yu, Zhenghao Ke, Fengji Dai, and Yibin Chen. "Aerial–ground collaborative 3D reconstruction for fast pile volume estimation with unexplored surroundings." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091994. http://dx.doi.org/10.1177/1729881420919948.

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Fast and accurate pile volume estimation is a very important basic problem in mining, waste sorting, and waste disposing industry. Nevertheless, for rapid changing or badly conditioned piles like stockpiles or landfills, conventional approaches involving massive measurements may not be applicable. To solve these problems, in this work, by utilizing unmanned aerial vehicles and unmanned ground vehicles equipped with a camera, we propose a collaborative framework to estimate volumes of free-formed piles accurately in short time. Compensating aerial- and ground views enable the reconstruction of piles with steep sides that is hard to be observed by single unmanned aerial vehicle. With the help of red-green-blue image sequences captured by unmanned aerial vehicles, we are able to distinguish piles from the ground in reconstructed point clouds and automatically eliminate concave on the ground while estimating pile volume. In the experiments, we compared our method to state-of-the-art dense reconstruction photogrammetry approaches. The results show that our approach for pile volume estimation has proved its feasibility for industrial use and its availability to free-formed piles on a different scale, providing high-accuracy estimation results in short time.
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Jain, Abhinandan, Calvin Kuo, Paramsothy Jayakumar, and Jonathan Cameron. "Constraint Embedding for Vehicle Suspension Dynamics." Archive of Mechanical Engineering 63, no. 2 (June 1, 2016): 193–213. http://dx.doi.org/10.1515/meceng-2016-0011.

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Abstract The goal of this research is to achieve close to real-time dynamics performance for allowing auto-pilot in-the-loop testing of unmanned ground vehicles (UGV) for urban as well as off-road scenarios. The overall vehicle dynamics performance is governed by the multibody dynamics model for the vehicle, the wheel/terrain interaction dynamics and the onboard control system. The topic of this paper is the development of computationally efficient and accurate dynamics model for ground vehicles with complex suspension dynamics. A challenge is that typical vehicle suspensions involve closed-chain loops which require expensive DAE integration techniques. In this paper, we illustrate the use the alternative constraint embedding technique to reduce the cost and improve the accuracy of the dynamics model for the vehicle.
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Palacios, Filiberto Muñoz, Eduardo Steed Espinoza Quesada, Guillaume Sanahuja, Sergio Salazar, Octavio Garcia Salazar, and Luis Rodolfo Garcia Carrillo. "Test bed for applications of heterogeneous unmanned vehicles." International Journal of Advanced Robotic Systems 14, no. 1 (January 1, 2017): 172988141668711. http://dx.doi.org/10.1177/1729881416687111.

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This article addresses the development and implementation of a test bed for applications of heterogeneous unmanned vehicle systems. The test bed consists of unmanned aerial vehicles (Parrot AR.Drones versions 1 or 2, Parrot SA, Paris, France, and Bebop Drones 1.0 and 2.0, Parrot SA, Paris, France), ground vehicles (WowWee Rovio, WowWee Group Limited, Hong Kong, China), and the motion capture systems VICON and OptiTrack. Such test bed allows the user to choose between two different options of development environments, to perform aerial and ground vehicles applications. On the one hand, it is possible to select an environment based on the VICON system and LabVIEW (National Instruments) or robotics operating system platforms, which make use the Parrot AR.Drone software development kit or the Bebop_autonomy Driver to communicate with the unmanned vehicles. On the other hand, it is possible to employ a platform that uses the OptiTrack system and that allows users to develop their own applications, replacing AR.Drone’s original firmware with original code. We have developed four experimental setups to illustrate the use of the Parrot software development kit, the Bebop Driver (AutonomyLab, Simon Fraser University, British Columbia, Canada), and the original firmware replacement for performing a strategy that involves both ground and aerial vehicle tracking. Finally, in order to illustrate the effectiveness of the developed test bed for the implementation of advanced controllers, we present experimental results of the implementation of three consensus algorithms: static, adaptive, and neural network, in order to accomplish that a team of multiagents systems move together to track a target.
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