Abstract: Controlling and monitoring of unmannedground vehicles (UGV) based on wireless communications is an upcoming research area. The control and implementation of the UGV s make use of wirelessnetwork techniques to set up a long range, high speed, noise immune and reliable connection with the remote base station. This paper proposes to select the most optimum path loss model after evaluating many models using simulation software like MATLAB. Further the most suitable network topology and modulation scheme is chosen according to the scenario, amount of traffic and whether the communication is LOS or NLOS .The design of UGV includes sensors, network devices, micro controller, mechanical and electrical design. . These types of vehicles can be used in multiple operations as search and rescue.
Terrestrial: In this type of sensor networks, hundreds to thousands of sensors deployed randomly or pre-deployed on a given area. This type of WSNs is mainly used in the field of environmental monitoring and presents a challenge to the sustainability of the network in terms of management of energy [Ado Adamou ABBA ARI , Abdelhak GUEROUI , Nabila LABRAOUI and Blaise Omer YENKE, 2015].
design and movement, and mobility . It can be used as a relay to enhance coverage, capacity, and energy efficiency in wireless communication. On the other hand, using them as base stations is also an emerging topic. Drone base station, which is known as unmanned aerial vehicle base station (UAV-BS) is a new form of application of drones which is so far used in the military for reconnaissance purposes. Here the name of UAV-BS denotes all different types of base stations that are mounted on a UAV or drone or flying platform which has the ability to change their altitude, avoid obstacles and establish the line-of-sight (LoS) between users. UAV-BS can be a cost-effective and efficient solution for the challenging scenarios discussed earlier such as natural disasters, unplanned power outages, etc. [6–8]. For example, after the hurricane Maria, AT&T had deployed UAV based cell on the wing (COW) to provide long-term evolution (LTE) network coverage for voice, data and text services to their users around a 40-square mile area in Puerto Rico . Nokia and EE are providing their UAV-BS services in Scotland since 2016 to manage the LTE coverage gap during the disaster periods . Ericsson has started to provide on-demand based coverage for special events like music festivals via UAV-BS . In addition, UAV-BS will be more beneficial in high-density urban areas where it is impossible to deploy a cellular base station due to land scarcity and high installation and maintenance costs. Therefore, UAV-BS can be used as a cost-effective solution for telecommunication service provides.
Topology-based data aggregation protocol has been designed to improve data aggregation efficiency of WSNs. This scheme constructs the matrix by incorporating the topology information of nodes in Wireless sensor network by assigning the weight vectors to each node helps to minimize the error rate. TADA is combined with balanced minimum spanning tree (BMST), to minimize the cost of updating the matrix when the scaling of nodes takes place. The effect of link transmission failure is not considered. This scheme performs well in minimizing error rate during matrix construction and improves energy efficiency. The generalized model for traversing the UAV network according to the transmission and topology information has been presented. The multi-path traversals will helps to achieve better coverage, throughput and data transfer in minimal time .TADA performs better in terms of energy consumption because of the smaller weight vector used in it. It proves that the increase in sparsity increases the error rates in RW and ICS whereas TADA provides a negligible error rate even though the sparsity increases. The storage requirement decrease in TADA. A novel energy-efficient algorithm to achieve high system-wide energy efficiency is presented. Incorporation of Genetic Algorithm (GA) to obtain an optimal solution for large-scale WSN applications along with minimal computation time is proposed. To solve high computing complexity, a GA-based optimization protocol is developed to arrive at the best optimal solution. There are three steps in the proposed algorithm. Construct the topology of the network and calculate the route for data mule by selecting the cluster head of each cluster by implementing genetic algorithm. After determining the route data mule traversed the designated path and gathering data from each cluster. This scheme achieves lesser data update period and consumes lesser energy consumption in WSNs. A nature inspired optimization technique known as Particle Swarm Optimization (PSO) is used to find the best topology which will reduce the energy consumption, error rate observed in bits, and travel time of the UAV. The PSO scheme outperforms LEACH in energy consumption
The WSN, UAV and gas sensing systems studied in this research are a response to challenges and limitations of WSNs and UAVs in the field of gas sensing and energy availability. The successful integration of a small solar powered aircraft equipped with a gas sensing system and networked with solar powered ground nodes proves the possibility of 3D monitoring of pollutant gases. The electric powered aircraft allowed the use of sensitive instruments and the execution of circular trajectories without self- contamination.
The robot is fitted with a RFID reader to detect the RFID tag. When the robot detects any human presence, it will check for the RF ID tag to identify whether the person detected is our soldier or an enemy and accordingly the information is sent to the control station. The distance of the detected obstacle is determined using the range finder. The robot is also embedded with a wireless camera which will capture the live video and transmit it to the control station. With the help of this camera we can capture the image and track the movement of the enemy in border area. The poisonous gases and the presence of magnetic field in the border areas are also checked using suitable sensors
Unmannedground vehicles could be navigated by using intelligent control methods.  proposed an neural network for tracking the path of an unmannedvehicle. The input provided to the neural network are velocity, friction coefficient, hope radius, output is velocity difference. For steering control prevention control method is adapted. Different velocity, turning radius and ground surfaces can be adapted by this neural network method. A multi-step neural network was proposed for controlling steering of wheeled mobile robots having complex mathematical model. Neural networks have reduced learning capacity for learning maximum overshoot, overshoot time, steady steering angle and speed. In order to control steering of wheeled mobile robots an GA fuzzy neural network is used. Firstly, a neural network model of mobile robot is developed. The best control parameters are found by using fuzzy neural network and GA method. GA fuzzy neural networks are used for combined speed and direction control of wheeled mobile robots. To achieve this first a controller based on fuzzy neural network is built, then to find best control parameters optimum GA algorithm is used.
This divides the field into parallel regions of two types, depending on whether they have sensors, which are in range of a MULE or not. Zhao et al. introduced a message ferrying scheme which uses a mobile node called a ferry to provide communication between nodes in a highly partitioned ad hoc network . The ferry is a special node with increased resources including renewable power, large memory and processing capabilities, and is used to transport messages between nodes, which otherwise might not have a multi-hop path between them. An extension of the ferry scheme was introduced in . They determine that the mobility of the ferry can be task oriented, where its route is determined for non-messaging reasons, such as a campus bus, or itcan be message-oriented, where ferry mobility is specifically designed to improve messaging performance. In addition, the model was extended to multiple ferries with emphasis on designing ferry routes in .
The aim of this mode is to enable operation of unmannedgroundvehicle using inputs which could vary from a simple computer keyboard to other self-designed input devices. The commands are sent over to the UGV remotely using wireless communication technologies such as ZigBee or internet, while it transfers live video feedback to the user. ZigBee is a wireless technology designed to connect simple high-tech devices for useful purposes.
All sensor modules are integrated with a microcontroller. The microcontroller collect all the data and then process the data and send necessary command to the motor driver circuit which further drives the motor of the UGV for its locomotion. During the manual operation the controller will send the command to the UGV, through a wireless communication using a PC. The wireless communication is done by using two wireless trans-receiver XBee modules; one is connected to PC where as other one is connected to the robot.
the path close to it. Due to the physical size of the vehicle it might be necessary to loop around itself for some time in order to reach its destination point. This will result in oscillations and thus, increased error. Since high look-ahead distances are expected to also increase the error, an optimal point within the range of variation may exist. According to our data, this point could be close to the value of 0.2m and therefore assuming a quadratic relation between this parameter and the error output may be worth- investigated. We included all the above effects in a regression table and run multiple analyses, dropping each time the least significant factor. We concluded in the following model, which seams to prove our assumption, as the D 2 max term is one of the most
UGV is built to undertake missions like border patrol, surveillance controlled by human soldiers (manual). The vehicle is controlled by a human operator and live video is fed back to the base station. A person from a remote place can comfortably control the motion of the robot wirelessly. This defense system of ours has a control unit (to control mobility). This robot is equipped with camera for surveillance in both day and night time. The UGV will be controlled by the controller interfaced with display for live feed. The vehicle has GPS to track the path. A RADAR system is used to detect any sudden movements around its environment. Additionally, metal detector is used to detect land mines and trace a safe path for the soldiers. In addition, wireless controlled drone is set up with the UGV which will be helpful in places where the UGV can’t reach.
In the area of robot path planning, various routing algorithms using classic approaches such as cell decomposition, potential field, sampling-based method, and sub- goal network have been proposed. For example, the applications of robot path planning based on cell decomposition can be found in the literature (Rosell, 2005; Šeda, 2007). A potential field method, inspired by the concept of electrical charges, was used to guide the robot to move toward the target while pushing away from the obstacles by assigning repulsive and attractive forces to the obstacles and the goal, respectively (Cosio and Castaneda, 2004). Sampling-based motion planning (SBP) algorithms create the paths by randomly adding points instead of evaluating all possible solutions. Two possible SBPs, probabilistic road-map and rapidly exploring random trees have been investigated (Lee et al., 2014). The sub-goal network utilizes a list of reachable configurations from the starting point to a goal point while avoiding all obstacles to identify the path for robot motion. This method has been used in a motion planner for humanoid robots (Candido et al., 2008) and for deploying the vision system and IR sensors (Singh et al., 2011; Liu et al., 2010).
UUVs were created to help people to fulfil their needs fast and with minimum risk for their lives. The first advanced UUV was created in the 1970’s by the navy and used to recover practice torpedoes and mines. They were also created by offshore oil and gas industries in order to assist in the development of offshore oil fields later on, when the new offshore development exceeded the reach of human divers. Nowadays you can find UUVs designed for different purposes, such as science, education, military and for hobby as well. In this project the main goal is to explain the theoretical, but also the practical aspects of UUV in order to understand how a small scale economical UUV works. In the following chapters step by step instructions will be given in order explain how to make a fully operated UUV, which is able to manoeuvre in any direction the water. The manoeuvring of the robot is achieved with the help of four brushless motors. The UUV has two parts:- A moving vehicle and a stationary floating buoy. The vehicle has an on board camera for monitoring and recording the underwater life. Also it has a robotic arm and in-housed sensors. Finally, the robot is tethered with the buoy’s side via an Ethernet cable. From there to the surface via wi-fi module. The controls for motion are given via Bluetooth module. Underwater vehicles, which can work at depths beyond the reach of scuba divers, give us the opportunity to explore and fill the
A four-channel Remote Control (RC) which is used to model aircraft can be modified to control multiple DC motors and other devices on Unmanned Guided Vehicle (UGV). Remote control used in this paper is HobbyKing brand that works at frequency 2.4 GHz. Eight bit microcontroller module is added to give command reference value to the transmitter module of the remote control that will be sent to the receiver module. On the transceiver module, two channels are used as an identifier (address and command), while the other two channels are used for transfer hexadecimal data (0-F). A PWM data received by the receiver module is read by the microcontroller module using the counter method utilizing the 16-bit timer. The data is sent to the DAC module to be converted into analogue voltage 0-5 Volt which is then manipulated by a signal amplifier to moving the DC motors or control various other devices on the UGV. The algorithm is implemented into the microcontroller module using the C programming language. Test results show that the counter method can be used for reading a PWM signal with absolute error less than 1%, so that the UGV can be controlled well via remote control.
Independent articulated mechanisms for use in the suspension system can have different degrees of freedom. In Figure 2, four examples of these mechanisms are displayed. The articulated mechanism (a) has three degrees of freedom, and due to its slippage, its components are required to enhance the rigidity of the assembly. The articulated mechanism (b) has the ability to change the length of the main suspension arm. The articulated mechanism (c) is another type of mechanism (b) having a deviation angle α relative to the main axis of suspension. The other type of hinge mechanism will be in the form (d), which will allow the suspension arm to be circular along the way, and will make it easier and more robust and more resistant to previous mechanisms and is more suitable for high-speed applications such as vehicles. Seidla  examines a four-wheeled car with an articulated suspension mechanism and points out that without an articulated suspension system there will be no possibility of passing through high-altitude obstacles, because after passing the front wheels on the obstacle, the load will be transferred to rear wheels, and front wheels do not have enough traction to raise the rear wheels. It should be noted that the rear tires are able to provide thrust to the separation point, and then the front tires must provide the thrust necessary to raise the vehicle.
DOI: 10.4236/ica.2018.94011 148 Intelligent Control and Automation equipped with sensing elements and have been utilized to navigate within the disastrous areas for searching and surveying  . Whether manually tele op- erated or autonomously driven, various types of robots such as unmannedground vehicles (UGV), unmanned aerial vehicles (UAV) and unmanned sur- face vehicles (USV), have been used in urban search and rescue missions   . Furthermore, first responders’ teams have used robots to drop emergency kits at victims who are drowning in sea or trapped within hazardous areas such as mine collapses or under the rubbles after earth quakes  .
The aim of our gesture control mode is to enable gesture functioning of the unmannedgroundvehicle (UGV) using Leap Motion without base station assistance. To accomplish this operation, hand gesture commands need to be acquired using inertial measurement unit (IMU) and then be transferred wirelessly using Zigbee technology. Other than Leap Motion concept in robotics, rest are from the literature [17, 18, 19, 20, 25]. Sathiyanarayanan et al., [21, 22, 25] successfully built a robot which could be controlled by hand gestures using hand gloves. In this paper, the hand gloves/MYO armband are replaced by Leap Motion device and the efficiency will be tested for practical implications.
Hardware-in-the-loop (HIL) simulation has widely used among automotive industry, traffic control and many other industry. HIL test became new trends in embedded system testing since they provide the advantageous factor for the one who use it, the factor such as cost, safety, duration, and feasibility which is to be sought for most industries. Not to miss researcher on robotics and unmannedvehicle area they already use this type of simulation testing too. Like [4,9] that use HIL test for testing unmanned air vehicle (UAV). Generally in HIL simulation test, a real environment condition did not applied during the
The UAV was chosen according to the size of study area, flight time and expected output of the survey. The used platform was a DJI Phantom 4 Pro, equipped with a RGB camera with focal length 8.8 mm, CMOS sensors 13.2 x 8.8 mm, pixel size of 2.4 m. The flights were planned using the open source software Mission Planner that connects the platform to the ground station. This tool was also used to set all the parameters of the flight plan, configuration of the flight (nadir images in North-South direction), height of the flight (33 m to have a Ground Sample Distance, GSD, of 8 mm) and the overlap between different images (80% longitudinal and 60% transverse overlap).