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An Intelligent Autonomous Driving system based on LIDAR Perception
Mr. M. Navaneetha krishnan a, Mr. K. Jaikumar b
Assistant Professor, Department of ECE, P. A. College of Engineering & Technology, Pollachi a, b [email protected]a, [email protected] b
Abstract: Accuracy in predicting the distance and improving the control of sensor by absorbing the road environment without accidents. We used LIDAR to create a self-driving robot in this paper. For user reference, we use Google Sheets. We use Node MCU to collect sensor distance information in particular. Information should be collected and collated for reference in order to plan and make decisions based on traffic. Our solution has been demonstrated in a self-driving car case study and on a real-life road scene by Udacity. This LIDAR Driving system can also be used in defence purpose, fire extinguishing, etc.
Keywords: Node MCU, Self-Driving Car, LIDAR.
1. Introduction
Over the years and centuries, industries have grown dramatically, with the first being completely powered by a steam engine, followed by the advent of gasoline and diesel, and now it appears that electricity will be a future population. Of course, as technology advances, more efficient and helpful cars can be developed; yet, as our world becomes more fast-paced, the number of accidents has increased. Because drivers are to blame for the majority of accidents, self-driving vehicles could be utilised to take their place. [1]
The proposed study is famous nowadays in the field of autonomous vehicles; in technology, we predict the environment and avoid obstacles by sensing. To detect their surroundings, autonomous cars use sensors such as LIDAR, RADAR, and ultrasonic sensors. By handling driving inputs such as accelerating and braking, this automated vehicle interacts with the data collected for the environment.
There are already self-driving vehicles on the market.
There are already self-driving vehicles on the market, such as automobiles that can park themselves, and researchers are working on ways to drive a vehicle without the need for human intervention.
Internal sensors with external connectivity will be the focus of study in the future. They do, however, necessitate more expensive detecting sensors, such as multi-line lasers. Rather than using high-cost sensors, they look for a low-cost, highly integrated, and lightweight alternative to achieve the same functionality. Small obstacles, regardless of size, shape, or appearance, can also be detected. In This study makes a contribution by proposing a new LIDAR-based environment identification and control approach. LIDAR (Light Detection and Ranging) is a sort of sensor that detects the environment [2]. It keeps a constant eye on its surroundings, and if any barriers are detected, the automobile detects them and navigates around them. At low sites, dual motor electric vehicles equipped with our real-time method enable for environmental perception and control.
2. Robot Part
The autonomous robot consists of Arduino UNO, Arduino IDE, LIDAR, Power supply, DC Motor and Servo motor.
2.1 Arduino UNO
Figure 1: Arduino UNO Board for Robotic Control
UNO is popular open-source microcontroller board using a mega 328p microcontroller. It is used to interface the board with other electronic components and contains set of digital and analogue i/o data pins. It is programmed using the IDE programming language .The code can be uploaded to board via USB connector.By connecting board to laptop this connection can also be utilized to power it. It consists of 6 analog pins and 14 digital pins. It can be programmed with the help of Arduino IDE that supports embedded C, its back-end is constructed using JAVA. Through USB port the code can be uploaded on the board. This port can also be used to power the board by connecting it to a laptop or PC. The operating voltag of Arduino is 5V, DC Current is 20mA, Input voltage is 7 to 20V, Clock frequency is 16MHz and Flash Memory is 32KB.[3]
2.2. Arduino IDE
Arduino is an open-source platform and supported on windows, Linux and MAC OS. Any Arduino board can use this software. Node MCU a microcontroller unit that can also use Arduino IDE.
IDE can also be used with MCU microcontroller chip. The primary code called sketch will generate hexfile which will be transferred and replaced in board. The IDE environment contains two parts:
editor and compiler. This environment supports C and C++ languages.
2.3. LIDAR (Light Detection and Ranging)
The laser in pulsed form is targeted by LIDAR system, which works on track of flight concept and the reflected or scattered pulse is measured using the detector.[4] From measuring the time difference b/w the transmitted and reflected pulse, the distance between the objects can be calculated.
TF MINI LIDAR FEATURES
• It is single directional laser range finder
• Its Maximum Detection Range: 12m
• It Performs Distance measurement at higher levels.
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• It Supports 100Hz sampling resolution, responsive
• Small size and low power consumption
Table 1: TF Mini LIDAR Specifications
Voltage Range 5V
Communication Interface UART / I2C
Working Range 0.1m-12m
Average Power: 0.6W
Refresh Frequency 100Hz
Band 850nm
Size 1.65in x 0.59in x 0.63in / 42mm x 15mm x 16mm
2.4. Power Supply
Power supply is the electrical device that provides electric power. It's also known as rate at which the circuit transfers electrical energy to the load. The primary functions of the power supply is to convert current from a source to voltage, current and frequency in order to power a load (electric power converters). In the event of an electrical fault, it also conducts current limiting for safety reasons, switching off the current. External 5V sensors can be powered using the Vin pin.2.5.
DC Motor
DC motors are a type of rotary Electrical equipment that converts direct current electrical energy into mechanical energy. To change the direction of current periodically, all DC motors employ either an electro mechanical or an electronic mechanism. DC motors are powered by existing direct current lighting power distribution system. The motors speed can be changed by adjusting the supply voltage or the strength of current in the field windings. The DC motor is utilised to drive the conveyor, which transports the thrown debris. Control device, output sensors and feedback systems servo motors are a type of motors that are appropriate for using closed loop control systems, despite the fact that they are not a defined class of motors. It is controllable and the speed can be altered.[5]
3. Working
In this paper we use LIDAR for the alternate for ultrasonic sensor and IR sensor. Due to its high efficiency and high accuracy, we use LIDAR. LIDAR consumes less power compared to other sensors.
Here, we are using AT Mega 298p for controlling, DC motor for rotating purpose. The power supply is given for both motor drivers and Ardino respectively. There is a built-in transformer that provides adequate power to the components.
Node MCU is used for Cloud Storage. The movement of the LIDAR is stored in Cloud for further references. For the use of storage, it will be helpful for the user identifies where there are the obstacles and we can see the vehicle conditions.[6] [7]
The Ardino IDE software platform were used for coding. For the effective working of the robot,
we need effective coding in C language. The working of the LIDAR is used to transmit the laser light from laser beam if there is any obstacle then it, can be received by the receiver. Then the time difference between the transmitter and receiver can be calculated. If the time is minimum, then it finds an obstacle nearby vehicle. The sensor interfaced with the micro controller and the micro controller gets the data about the presence of the obstacles in the path. The four LIDAR sensor are used to sense the obstacles in all sides. For forward motion the front sensor data is monitored if any obstacle is deducted within the range of 30 cm. Then the next priority is given to left side. Then message as
“Front side obstacle deducted” will be sent to spread sheet. The left side sensor data is monitored if there is no presence of obstacle then the rover will turn left and will repeat. If there is presence of obstacle in left side. Then message as “Left side obstacle deducted” will be sent to spread sheet. Next priority will be given to the right side. If there is no presence of obstacle then the rover will turn right.
In case there is presence of obstacle in right side of the rover.[8] Then message as “Front side obstacle deducted” will be sent to spread sheet. Then the rover will check the back sensor for presence of any obstacles currently. If there is no presence of obstacles then rover will turn reverse. If there is a presence of obstacle in back side of the rover then rover will be in ideal. And message as “All side obstacle deducted” will be sent to spread sheet.[9]
4. Monitoring Part 4.1. Node MCU
The node MCU is an microcontroller unit or system on a cup, that includes the ESP8266 Wi-Fi module. Serial communication protocols like as UART and SPT are supported by node MCU, just like they are by Arduino. Using the Arduino IDE, the programme may be written in the node MCU. The node MCU is powered by an on-board micro-USB port. The development board has 17 GPIO pins, as well as RST and FLASH. The output is indicated by an integrated LED that may be programmed by the user.[10]
4.2. Google Sheet
Spreadsheet software is Google Sheets. The distance between the barrier and the LIDAR is determined and the data is saved in the Node MCU.When the user needs the information, the signal is retrieved from the mobile phone by turn on the hotspot.[11] [12] The details are filled in the Google Sheet and the details are taken for the reference.
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Figure 2: Screen Shot of Datas received from LIDAR in Google Sheet 5. Results
The Hardware output of this project constructs a small size moving robot, which detects an obstacle and moves around a given direction. It moves forward for user inputs and detects an obstacle it will automatically change their path for reaching destination.
Figure 3: Autonomous Robot with LIDAR
The robot part is considering as a small range moving vehicle, which also been consider as an autonomous vehicle. It detects an obstacle through the LIDAR sensor and moves forward to reach the destination.
6. Conclusion
We prove a unique strategy for autonomous rover obstacle avoidance that is both efficient and safe by using sensor values, the vehicle used to avoid colliding with any fixed or moving barrier.
Rather than calculating the repulsive field directly from the distance data, it first detects barriers, and it also finds the angle with the least value from the total field function. This has the advantage of requiring no time-consuming processes such as image processing or computer vision processing, and it
can be easily implemented in a real-time system due to the low overhead involved in calculating it.
Thus, this model can be used in defence application for finding the enemies hidden in the borders with out involving humans. This also can be used in fire extinguishing where the humans are not able to enter.
References
1. Capezio, F.; Sgorbissa, A. &Zaccaria, R. (2005), “GPS Based Localization for a Surveillance UGV in Outdoor Areas”, Proceedings of the Fifth International Workshop on Robot Motion and Control (RoMoCo’05), pp. 157- 162, ISBN: 83-7143-266-6, Dymaczewo, Poland.
2. Carnegie, D. A.; Prakash, A.; Chitty, C. & Guy, B. (2004), “A human-like semi-autonomous Mobile Security Robot”, 2nd International Conference on Autonomous Robots and Agents, ISBN:
0-476-00994-4, Palmerston North, New Zealand.
3. Atmega Datasheet: https://ww1.microchip.com/downloads/en/DeviceDoc/Atmel-7810-Automotive - Microcontrollers-ATmega328P_Datasheet.pdf
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732-736, doi: 10.1109/ICACCS.2019.8728312.
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7. A. Gambi, T. Huynh and G. Fraser, (2019) "Automatically Reconstructing Car Crashes from Police Reports for Testing Self-Driving Cars", IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), Montreal, QC, Canada, pp. 290-291, doi:
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8. Z. Rozsa and T. Sziranyi, (2018) "Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor", in IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 8, pp. 2708-2720, doi: 10.1109/TITS.2018.2790264.
9. R. P. Meenaakshi Sundhari and K. Jaikumar, (2020) "IoT assisted Hierarchical Computation Strategic Making (HCSM) and Dynamic Stochastic Optimization Technique (DSOT) for energy optimization in wireless sensor networks for smart city monitoring", Computer Communications, vol.
150, pp. 226-234.
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11. R. A. Light, (2017) “Mosquitto: server and client implementation of the MQTT protocol”, The Journal of Open-Source Software, vol. 2, no. 13, p. 265. Online..
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