Optimization of waterspace management and scheduling for multiple unmanned vehicles

2020 ◽  
pp. 303-327
Keyword(s):  
Unmanned Vehicles

Innovative Systems for Human Supervisory Control of Unmanned Vehicles

2012 ◽  
Author(s):  
Andrew S. Clare ◽  
Jason C. Ryan ◽  
Kimberly F. Jackson ◽  
M. L. Cummings
Keyword(s):  
Supervisory Control ◽  
Unmanned Vehicles ◽  
Human Supervisory Control

STRUCTURAL GUIDELINES FOR THE MODERNIZATION OF AGRICULTURAL MACHINERY ENTERPRISES: CHARACTERISTICS OF THE CURRENT DEVELOPMENT MODEL

2020 ◽  
Vol 1 (3) ◽  
pp. 55-62
Author(s):  
N. А. LEBEDEV ◽  
Keyword(s):  
Development Strategy ◽  
Machine Building ◽  
Unmanned Vehicles ◽  
Development Model ◽  
Current Development ◽  
Agricultural Machinery ◽  
Building Products ◽  
The Development Strategy ◽  
New Development

The article analyzes the structural guidelines for the modernization of the domestic agricultural machinery industry, which are formed in the aspect of a new development model; some types of machine-building products that are innovative in nature. Separate tasks of digitalization for the development of production of unmanned vehicles are considered. It is concluded that in order to seriously promote the structural modernization of agricultural machinery enterprises, it is necessary to give priority to the development strategy of enterprises, which should be based on clear development guidelines for the long-term period.

Using a Natural Language and Gesture Interface for Unmanned Vehicles

2000 ◽  
Author(s):  
Dennis Perzanowski ◽  
Alan C. Schultz ◽  
William Adams ◽  
Elaine Marsh
Keyword(s):  
Natural Language ◽  
Unmanned Vehicles

scholarly journals Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

2021 ◽  
Vol 11 (13) ◽  
pp. 5963
Author(s):  
Phuc Thanh-Thien Nguyen ◽  
Shao-Wei Yan ◽  
Jia-Fu Liao ◽  
Chung-Hsien Kuo
Keyword(s):  
Robot Navigation ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Mobile Robot Navigation ◽  
Selection Strategy ◽  
Localization And Mapping ◽  
Pure Pursuit ◽  
Stable Performance ◽  
Large Curve ◽  
Moving Speed

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

Sharding-Enabled Blockchain for Software-Defined Internet of Unmanned Vehicles in the Battlefield

IEEE Network ◽  
2021 ◽  
Vol 35 (1) ◽  
pp. 101-107
Author(s):  
Bimal Ghimire ◽  
Danda B. Rawat ◽  
Chunmei Liu ◽  
Jiang Li
Keyword(s):  
Unmanned Vehicles

scholarly journals Observation and analysis of diving beetle movements while swimming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Debo Qi ◽  
Chengchun Zhang ◽  
Jingwei He ◽  
Yongli Yue ◽  
Jing Wang ◽  
...  
Keyword(s):  
Swimming Speed ◽  
High Speed ◽  
Kinematic Model ◽  
Movement Pattern ◽  
Unmanned Vehicles ◽  
Power Stroke ◽  
Cross Sectional ◽  
Motion Data ◽  
The Cross ◽  
Diving Beetle

AbstractThe fast swimming speed, flexible cornering, and high propulsion efficiency of diving beetles are primarily achieved by their two powerful hind legs. Unlike other aquatic organisms, such as turtle, jellyfish, fish and frog et al., the diving beetle could complete retreating motion without turning around, and the turning radius is small for this kind of propulsion mode. However, most bionic vehicles have not contained these advantages, the study about this propulsion method is useful for the design of bionic robots. In this paper, the swimming videos of the diving beetle, including forwarding, turning and retreating, were captured by two synchronized high-speed cameras, and were analyzed via SIMI Motion. The analysis results revealed that the swimming speed initially increased quickly to a maximum at 60% of the power stroke, and then decreased. During the power stroke, the diving beetle stretched its tibias and tarsi, the bristles on both sides of which were shaped like paddles, to maximize the cross-sectional areas against the water to achieve the maximum thrust. During the recovery stroke, the diving beetle rotated its tarsi and folded the bristles to minimize the cross-sectional areas to reduce the drag force. For one turning motion (turn right about 90 degrees), it takes only one motion cycle for the diving beetle to complete it. During the retreating motion, the average acceleration was close to 9.8 m/s2 in the first 25 ms. Finally, based on the diving beetle's hind-leg movement pattern, a kinematic model was constructed, and according to this model and the motion data of the joint angles, the motion trajectories of the hind legs were obtained by using MATLAB. Since the advantages of this propulsion method, it may become a new bionic propulsion method, and the motion data and kinematic model of the hind legs will be helpful in the design of bionic underwater unmanned vehicles.

scholarly journals Genetic Improvement of Routing Protocols for Delay Tolerant Networks

2021 ◽  
Vol 1 (1) ◽  
pp. 1-37
Author(s):  
Michela Lorandi ◽  
Leonardo Lucio Custode ◽  
Giovanni Iacca
Keyword(s):  
Routing Protocols ◽  
Genetic Improvement ◽  
Ad Hoc ◽  
Delay Tolerant Networks ◽  
Unmanned Vehicles ◽  
Test Cases ◽  
Urban Networks ◽  
Delivery Probability ◽  
Apply Genetic ◽  
Delay Tolerant

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes’ mobility. In these contexts, routing is NP-hard and is usually solved by heuristic “store and forward” replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.

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