In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

Most 3D scanners are heavy, bulky and costly. These are the major factors that make them irrelevant to be attached to a drone for autonomous navigation. With modern technologies, it is possible to design a simple 3D scanner for autonomous navigation. The objective of this study is to design a cost effective 3D indoor mapping system using a 2D light detection and ranging (LiDAR) sensor for a drone. This simple 3D scanner is realised using a LiDAR sensor together with two servo motors to create the azimuth and elevation axes. An Arduino Uno is used as the interface between the scanner and computer for the real-time communication via serial port. In addition, an open source Point-Cloud Tool software is used to test and view the 3D scanner data. To study the accuracy and efficiency of the system, the LiDAR sensor data from the scanner is obtained in real-time in point-cloud form. The experimental results proved that the proposed system can perform the 2D and 3D scans with tolerable performance.

Crop management is one of the key aspects in precision agriculture. On-site crop management includes a scheduled prediction and prescription of fertilization and pesticide application on specific areas of the field. A prediction of the tree health is much needed in order to decide a suitable prescription for the plant. An autonomous vehicle, equipped with at least one LiDAR (light detection and ranging) sensor, could be used not only for detecting and mapping its surrounding, but to also help evaluate tree health for early distress detection. Currently, farmers have to rely on eyesight to identify trees or plants in distress. Big-scale plantations depend on costly scheduled airborne monitoring routines, which also relies on human vision by scouring through hours of aerial video footage. Both techniques have similar weaknesses in terms of the time it takes to detect a tree in distress and the accuracy of the detection using human vision. The objective of this research is to propose a technique in evaluating tree health using a simple LiDAR sensor that is commonly used in autonomous vehicles. In order to achieve this objective, an evaluation of the different intensity characteristics of tree leaves versus fruits was carried out, both in the lab and in the field. This study has chosen oil palm trees as its subject, as the problem of health monitoring in oil palm plantations is evident. A LiDAR with a 905 nm near-infrared (NIR) laser is used to scan both individual healthy leaves in the lab and different oil palm trees in the field. Since a LiDAR sensor is normally used for ranging, a systematic process was proposed to capture the reflected intensity value of the laser beam that was transmitted to the object. This whole system can be realized using a LiDAR sensor, servo motors, and an Arduino board. Processing software was used to test and store the captured information from the sensor. Later, MATLAB was used to plot the intensity map of the leaves and oil palm tree, classify the range intensity into histograms, and calculate the leaf area index (LAI) for the oil palm trees. From the experimental results, it is found that the reflectance intensity of the leaves shows consistent range values between 155-160 magnitude both in the lab and in the field. From there, three different trees with different number of leaves were scanned and evaluated based on their LAI values. The health of the tree is then predicted, where a healthy tree is estimated to have a higher LAI value. The resulting LAI value is found to correlate with the evaluation using eyesight. This proves that although using a single-wavelength NIR laser beam provided by the LiDAR sensor, as compared to multiple wavelengths of a spectrometer, the difference between the oil palm leaves, fruits and the background noise could be determined. In the future, where multi-wavelength laser LiDAR sensors could be possible, more materials could be characterized. In conclusion, detection, mapping, and materials characterization could be done by an autonomous vehicle utilizing a LiDAR sensor, where tree health could be predicted for crop care management.

The current practice in determining oil palm fresh fruit bunches (FFB) ripeness is by its colour which could be inaccurate. This study investigates the classification of oil palm FFB ripeness using Raman spectroscopy. A feature extraction model is developed based on the different organic compositions that contribute to the ripeness classification. Samples are collected according to the Malaysian Palm Oil Board (MPOB) standards which are unripe, underripe, ripe, overripe, and rotten. Different characteristics of the Raman shift were detected which represent the material composition for each sample. The Raman intensity of the oil palm fruit increases from unripe to ripe before decreasing to rotten due to the carotenoid content in the fruit. In conclusion, Raman spectroscopy is a suitable technique to observe the changes in the composition of oil palm fruit classified by its ripeness. Â

The Unmanned Aerial Vehicles (UAV) has recently gained popularity in numerous industries due to its unique capabilities. Multicopters are increasingly replacing conventional helicopters and fixed wing planes as the most commonly manufactured Micro Aerial Vehicles, due to their reasonable price. Unlike the long flight operations of fixed wing planes, multicopters can only be operated for short durations. The performance of multicopter depend on the characteristic of the motor that had being used. Adding further payloads to multicopters will deteriorate the performance in terms of stability and flight time. The objective of this paper is to study the characteristics of brushless direct current motor for the purpose of multicopter design. The experiments to determine the characteristics of brushless direct current motor are divided into two parts. The first part of the experiment is the measurement of lift force, power consumption, and current drain under various loads. The next part of the experiment is the measurement of the propeller RPM. All experiments were conducted using the same propeller, motor and Electronic Speed Controller (ESC) setup. The data collected from the experiments is used to generate five characteristics graphs, which consist of propeller rotation versus throttle, power versus propeller rotation, lift versus throttle, current versus throttle and finally power versus throttle. Besides that, the graph of flight time vs throttle input was estimated. All the data was used to build a reference table to estimate the minimum take-off throttle input and flight time for various multicopter configurations. In conclusion, the reference table and the graphs can be useful in aiding multicopter designers in designing an octocopter.

Controlling a UAV in an emergency situation is a challenging task and it requires a highly skilled person. With modern technologies it is possible to design a UAV which can be controlled by anyone regardless of their skills. The objective of this study is to develop an intelligent quadrotor flight controller for autonomous navigation to aid humans. The intelligent flight controller is realized by adding additional IMU sensors to control more degree of freedoms (DOF) together with real time data communication. The design comprises of electronic circuits for navigation and telemetry developed from scratch with the help of CAD software. An open source flight controller software AeroQuad is used to test the quadrotor dynamics. The sensors data from quadrotor is obtained in real time to study the stability and dynamics of the system. The data collected shows behavior of the quadrotor when hovering and maneuvering.