Unmanned Vehicles Systems; Unmanned Vehicle Tactical Memorandum (TM 3-22-5-SW): Report of Findings and Recommendation

The acquisition, presentation and management of autonomous driving decision-making knowledge of unmanned vehicles are the key and difficult issues in the autonomous driving decision-making system of unmanned vehicles. This paper presents a knowledge model, which includes problem description layer and problem-solving knowledge layer. The automatic driving decision knowledge base of unmanned vehicle is composed of a set of knowledge models. Knowledge model supports knowledge representation and reasoning. Based on the WEB visualization knowledge modeling tool and visualization knowledge service tool, we construct the decision-making knowledge base management system for autonomous driving of unmanned vehicles and then construct the autonomous driving decision-making system of unmanned vehicles. The reasoning example shows that the knowledge base management system can effectively improve the knowledge acquisition, representation and maintenance efficiency of autonomous driving decision-making system, which is of great significance in enhancing the intelligence level of autonomous driving decision-making system.

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Vehicles for Open-Pit Mining with Smart Scheduling System for Transportation Based on 5G

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i5.1490 ◽

2021 ◽

Vol 12 (5) ◽

pp. 827-835

Author(s):

Mohanad F Jwaid, Husam K Salih Juboori

Keyword(s):

Artificial Intelligence ◽

Unmanned Vehicles ◽

Open Pit Mine ◽

Open Pit ◽

Open Pit Mining ◽

Transport Costs ◽

Time Data ◽

Nsga Ii ◽

Scheduling System ◽

Unmanned Vehicle

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancements coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. In addition to four other models we are proposing a decomposition-based restricted genetic dominance (DBCDP-NSGA-II) algorithm, which retains viable and non-facilitating solutions in small areas in order to improve the convergence, distribution and diversity of traditional high-dimensional multi-objective fast-dominated genetic sorting Algorithms (NSGA-II).

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UNMANNED VEHICLE OBSTACLE DETECTION AND AVOIDANCE USING DANGER ZONE APPROACH

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2013-0041 ◽

2013 ◽

Vol 37 (3) ◽

pp. 529-538 ◽

Cited By ~ 4

Author(s):

Ta-Chung Wang ◽

Tz-Jian Lin

Keyword(s):

Obstacle Avoidance ◽

Relative Velocity ◽

Unmanned Vehicles ◽

Obstacle Detection ◽

Servo Motor ◽

Danger Zone ◽

Unmanned Vehicle ◽

Algebraic Sets ◽

Time Period ◽

Distance Data

This paper proposes an obstacle avoidance algorithm for unmanned vehicles in unknown environment. The vehicle uses an ultrasonic sensor and a servo motor which rotates from 0 to 180 degrees to obtain the distance data, and the profile of the obstacle. In this avoidance algorithm we will use the danger zone concept to judge whether the obstacle will cause a possible collision. The danger zone concept surrounds the vehicle through the intersection of semi-algebraic sets. These semi-algebraic sets use the relative velocity of the obstacle to calculate the area in which obstacles will collide with the vehicle within a pre-specified time period. Combining the profile of the boundary of the obstacle with the danger zone concept, a method for determining the safe maneuvers to avoid collisions is also provided.

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UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 1. VEHICLE MOVEMENT MODEL

Vestnik komp iuternykh i informatsionnykh tekhnologii ◽

10.14489/vkit.2020.09.pp.003-012 ◽

2020 ◽

pp. 3-12

Author(s):

A. S. Akopov ◽

N. K. Khachatryan ◽

L. A. Beklaryan ◽

A. L. Beklaryan

Keyword(s):

Control System ◽

Fuzzy Clustering ◽

Personal Space ◽

Computational Procedure ◽

Unmanned Vehicles ◽

Final Decision ◽

Clustering Methods ◽

Movement Model ◽

Unmanned Vehicle ◽

Vehicle Movement

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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Unmanned vehicle use: legal responsibility

Courier of the Kutafin Moscow State Law University ◽

10.17803/2311-5998.2021.82.6.155-161 ◽

2021 ◽

pp. 155-161

Author(s):

A. O. Ananenko

Keyword(s):

Detailed Analysis ◽

Russian Federation ◽

Unmanned Vehicles ◽

The State ◽

Legal Responsibility ◽

Technical Device ◽

Unmanned Vehicle ◽

The Third ◽

The Russian Federation ◽

Transport Complex

The article discusses the main directions of development and improvement of regulatory legal acts regulating the use of unmanned vehicles in the Russian Federation, the legal responsibility of the manufacturer, owner, operator of such a technical device.It is concluded that the priority of the digitalization of the transport complex is the widespread use of BPTS, mainly so far in the field of road and air transport. Examples of pilotless vehicles that have been in trial operation for several years are given. The experiment, which is currently being carried out, will be completed on the 2nd of 2022 and a decision will be made on the ways of developing unmanned vehicles.It is concluded that the priority of the digitalization of the transport complex is the widespread use of BPTS, mainly so far in the field of road and air transport. Examples of pilotless vehicles that have been in trial operation for several years are given. The experiment, which is currently being carried out, will be completed on the 2nd of 2022 and a decision will be made on the ways of developing unmanned vehicles.The article provides a detailed analysis of three bills regulating the creation and use of unmanned vehicles. Two of them have already been included in the State Duma of the Russian Federation in 2019 and 2020. and rejected, the third was prepared by the Ministry of Transport in 2021 and submitted for discussion.

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Beacon-Based Hybrid Routing Protocol for Large-Scale Unmanned Vehicle Ad Hoc Network

Electronics ◽

10.3390/electronics10243129 ◽

2021 ◽

Vol 10 (24) ◽

pp. 3129

Author(s):

Weiwei Mu ◽

Guang Li ◽

Yulin Ma ◽

Rendong Wang ◽

Yanbo Li ◽

...

Keyword(s):

Routing Protocol ◽

Large Scale ◽

Unmanned Vehicles ◽

Maintenance Cost ◽

Communication Link ◽

Unmanned Vehicle ◽

Hybrid Routing ◽

Communication Links ◽

Hybrid Routing Protocol ◽

Vehicle Network

In this paper, we designed a beacon-based hybrid routing protocol to adapt to the new forms of intelligent warfare, accelerate the application of unmanned vehicles in the military field, and solve the problems such as high maintenance cost, path failure, and repeated routing pathfinding in large-scale unmanned vehicle network communications for new battlefields. This protocol used the periodic broadcast pulses initiated by the beacon nodes to provide synchronization and routing to the network and established a spanning tree through which the nodes communicated with each other. An NS3 platform was used to build a dynamic simulation environment of service data to evaluate the network performance. The results showed that when it was used in a range of 5 ~ 35 communication links, the beacon-based routing protocol’s PDR was approximately 10% higher than that of AODV routing protocol. At 5 ~ 50 communication links, the result was approximately 20% higher than the DSDV routing protocol. The routing load was not related to the number of nodes and communication link data and the protocol had better performance than traditional AODV and DSDV routing protocol, which reduced the cost of the routing protocol and effectively improved the stability and reliability of the network. The protocol we designed is more suitable for the scenarios of large-scale unmanned vehicle network communication in the future AI battlefield.

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Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Sensors ◽

10.3390/s19071600 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1600 ◽

Cited By ~ 1

Author(s):

Zheng Yao ◽

Sentang Wu ◽

Yongming Wen

Keyword(s):

Internet Of Things ◽

Social Cognitive ◽

Simulation Experiment ◽

Unmanned Vehicles ◽

The Internet ◽

Unmanned Vehicle ◽

Aerial Vehicle ◽

Multi Agent ◽

The Internet Of Things ◽

Cognitive Optimization

Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.

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Acceleration Anomaly Detection Method for Sensor Axle Box of Unmanned Vehicle

International Journal of Vehicle Structures and Systems ◽

10.4273/ijvss.13.4.10 ◽

2021 ◽

Vol 13 (4) ◽

Author(s):

Ling Yu ◽

Lei Wang

Keyword(s):

Time Window ◽

Unmanned Vehicles ◽

Unmanned Vehicle ◽

Signal Conversion ◽

Acceleration Signal ◽

Capacitive Accelerometer ◽

Digitally Programmable ◽

Track Condition ◽

Statistical Analysis Method ◽

Programmable Gain

Detecting the anomaly acceleration of the sensor’s axle box of unmanned vehicles is very important for judging the wear condition of vehicle track and evaluating the state of the track. A capacitive accelerometer is connected with acquisition equipment to collect the information of train axle box’s acceleration change when the vehicle is running; instrument amplifier AD8250 with a digitally programmable gain is selected as system signal conversion chip to realize acceleration signal conversion; sliding variance of axle box’s acceleration of the unmanned vehicle is calculated based on sliding variance statistical analysis method, which is confirmed by time window and distance window. Fixing the width of a sliding window according to the response statistics caused by the line excitation link, the acceleration sliding variance is compared with the standard one to determine whether the acceleration is in an anomaly state. The test results show that the anomaly acceleration of the sensor axle box of the unmanned vehicle detected by the proposed method is consistent with the actual results, which provides a reliable basis for vehicle track condition assessment.

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Intelligent Detection Method for Roll Stability of Unmanned Vehicle based on Fuzzy Control

International Journal of Vehicle Structures and Systems ◽

10.4273/ijvss.13.4.06 ◽

2021 ◽

Vol 13 (4) ◽

Author(s):

Jun Chen

Keyword(s):

Control System ◽

Fuzzy Control ◽

Detection Method ◽

Lower Layer ◽

Unmanned Vehicles ◽

Roll Angle ◽

Detection Accuracy ◽

Unmanned Vehicle ◽

Roll Control ◽

Intelligent Detection

When the unmanned vehicle is disturbed by the outside world or carries out dangerous actions such as steering and continuous lane changing, the yaw stability of the unmanned vehicle decreases and the dangerous situation such as rollover is easy to occur. In this paper, the intelligent detection method for roll stability of unmanned vehicles based on fuzzy control is studied. The roll control system of the unmanned vehicle based on a double-layer control strategy is designed. The roll stability of the unmanned vehicle is controlled by an upper-layer fuzzy controller and lower-layer differential braking control. The dynamic model and tire model are built in MATLAB/Simulink to restore the running characteristics of unmanned vehicles. Based on the operation characteristics, the roll stability of the unmanned vehicle’s roll control system based on fuzzy control is tested from three aspects: steady-state response, roll stability and dynamic stability coefficient. The experimental results show that the transverse load’s transfer rate of the proposed method is reduced by more than 0.2% compared with the contrast method, the yaw angular velocity, centroid’s roll angle and roll angle measured under the two working conditions are closer to the actual values, which shows that the method has better control effect and detection accuracy.

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CONTROL SYSTEM STRUCTURE AND LOGIC FOR UNMANNED VEHICLES

Izvestiya of Samara Scientific Center of the Russian Academy of Sciences ◽

10.37313/1990-5378-2021-23-4-96-102 ◽

2021 ◽

Vol 23 (4) ◽

pp. 96-102

Author(s):

D.E. Chickrin ◽

Keyword(s):

Control System ◽

Control Unit ◽

Vehicle Control ◽

Unmanned Vehicles ◽

System Structure ◽

Unmanned Vehicle ◽

Control Subsystem

The article presents the author developed requirements for the key subsystem of the unmanned vehicle: the control subsystem, with the details of the unmanned vehicle control modes; proposals are given for the implementation of a three-level unmanned vehicle control unit, according to the concept of a "modular crate" proposed by the author.

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