Delicar: A smart deep learning based self driving product delivery car in perspective of Bangladesh

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.publishedVersio

Locomotion system for ground mobile robots in uneven and unstructured environments

One of the technology domains with the greatest growth rates nowadays is service robots. The extensive use of ground mobile robots in environments that are unstructured or structured for humans is a promising challenge for the coming years, even though Automated Guided Vehicles (AGV) moving on flat and compact grounds are already commercially available and widely utilized to move components and products inside indoor industrial buildings. Agriculture, planetary exploration, military operations, demining, intervention in case of terrorist attacks, surveillance, and reconnaissance in hazardous conditions are important application domains. Due to the fact that it integrates the disciplines of locomotion, vision, cognition, and navigation, the design of a ground mobile robot is extremely interdisciplinary. In terms of mechanics, ground mobile robots, with the exception of those designed for particular surroundings and surfaces (such as slithering or sticky robots), can move on wheels (W), legs (L), tracks (T), or hybrids of these concepts (LW, LT, WT, LWT). In terms of maximum speed, obstacle crossing ability, step/stair climbing ability, slope climbing ability, walking capability on soft terrain, walking capability on uneven terrain, energy efficiency, mechanical complexity, control complexity, and technology readiness, a systematic comparison of these locomotion systems is provided in [1]. Based on the above-mentioned classification, in this thesis, we first introduce a small-scale hybrid locomotion robot for surveillance and inspection, WheTLHLoc, with two tracks, two revolving legs, two active wheels, and two passive omni wheels. The robot can move in several different ways, including using wheels on the flat, compact ground,[1] tracks on soft, yielding terrain, and a combination of tracks, legs, and wheels to navigate obstacles. In particular, static stability and non-slipping characteristics are considered while analyzing the process of climbing steps and stairs. The experimental test on the first prototype has proven the planned climbing maneuver’s efficacy and the WheTLHLoc robot's operational flexibility. Later we present another development of WheTLHLoc and introduce WheTLHLoc 2.0 with newly designed legs, enabling the robot to deal with bigger obstacles. Subsequently, a single-track bio-inspired ground mobile robot's conceptual and embodiment designs are presented. This robot is called SnakeTrack. It is designed for surveillance and inspection activities in unstructured environments with constrained areas. The vertebral column has two end modules and a variable number of vertebrae linked by compliant joints, and the surrounding track is its essential component. Four motors drive the robot: two control the track motion and two regulate the lateral flexion of the vertebral column for steering. The compliant joints enable limited passive torsion and retroflection of the vertebral column, which the robot can use to adapt to uneven terrain and increase traction. Eventually, the new version of SnakeTrack, called 'Porcospino', is introduced with the aim of allowing the robot to move in a wider variety of terrains. The novelty of this thesis lies in the development and presentation of three novel designs of small-scale mobile robots for surveillance and inspection in unstructured environments, and they employ hybrid locomotion systems that allow them to traverse a variety of terrains, including soft, yielding terrain and high obstacles. This thesis contributes to the field of mobile robotics by introducing new design concepts for hybrid locomotion systems that enable robots to navigate challenging environments. The robots presented in this thesis employ modular designs that allow their lengths to be adapted to suit specific tasks, and they are capable of restoring their correct position after falling over, making them highly adaptable and versatile. Furthermore, this thesis presents a detailed analysis of the robots' capabilities, including their step-climbing and motion planning abilities. In this thesis we also discuss possible refinements for the robots' designs to improve their performance and reliability. Overall, this thesis's contributions lie in the design and development of innovative mobile robots that address the challenges of surveillance and inspection in unstructured environments, and the analysis and evaluation of these robots' capabilities. The research presented in this thesis provides a foundation for further work in this field, and it may be of interest to researchers and practitioners in the areas of robotics, automation, and inspection. As a general note, the first robot, WheTLHLoc, is a hybrid locomotion robot capable of combining tracked locomotion on soft terrains, wheeled locomotion on flat and compact grounds, and high obstacle crossing capability. The second robot, SnakeTrack, is a small-size mono-track robot with a modular structure composed of a vertebral column and a single peripherical track revolving around it. The third robot, Porcospino, is an evolution of SnakeTrack and includes flexible spines on the track modules for improved traction on uneven but firm terrains, and refinements of the shape of the track guidance system. This thesis provides detailed descriptions of the design and prototyping of these robots and presents analytical and experimental results to verify their capabilities

Influence of Turning on Military Vehicle Induced Rut Formation

Rut formation can severely influence soil conditions and vegetation, and reduce vehicle mobility. Vehicle operations can affect rut formation. Ruts formed in straight vehicle paths are different than when the vehicle turns. This research is mainly to investigate the effects of vehicle turning maneuvers on soil rut formation, including field tests, lab tests, and model development. Field tests were conducted at Yuma Training Center, Fort Riley and Fort Lewis on wheeled and tracked military vehicles. In field tests, rut depth, rut width and rut index were used as the main indicators to quantify a rut. A Vehicle Tracking System was mounted onto each vehicle to utilize the Global Positioning System. The vehicles were operated in spiral patterns to get constantly decreasing turning radius. The Vehicle Terrain Interaction terrain mechanics model was chosen to modify to predict rut formation during vehicle turning operations on yielding soils. In the modified VTI model, the resultant force on a single wheel is a dynamic variable correlated with the vehicle’s weight, velocity, and turning radius. In addition, lab tests were conduced on a tire and a track shoe in sand. Lateral forces and lateral displacements were applied under constant normal forces. The tire was pulled laterally and the track shoe was pulled back and forth to represent actual movement during vehicle turning. Results indicate that (1) rut depth, rut width and rut index increase with the decrease of TR, especially when TR is less than 20 meters; (2) vehicle parameters and soil parameters are statistically significant to affect rut formation; (3) the modified VTI model is able to predict rut formation when turning, with an improved R square of 0.43; (4) in lab tests, the final sinkage caused by the lateral force or displacement is 3 to 5 times the static sinkage; (5) rut depths increase from 65% to 548% of the initial rut depths under the effects of the combination of the multi-pass and turning maneuvers after multiple passes. This dissertation is a collection of five individual papers. More detailed description of test procedures and conclusions are found in these papers

Data fusion for unsupervised video object detection, tracking and geo-positioning

In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements

Modeling of Terrain Impact Caused by Off-road Vehicles

Terrain impact models were developed for both wheeled vehicles and tracked vehicles based on the analysis of vehicle dynamics, soil mechanics, and geometric relationships between vehicle parameters and the disturbed width. The terrain impact models, including both disturbed width models and impact severity models, were developed separately for tracked vehicles and wheeled vehicles. The disturbed width models of both vehicle types were primarily based on the geometric relationship between vehicle contact width and vehicle dynamic parameters. For both vehicle types, the impact severity was defined as the ratio between soil shear stress and soil shear strength. The impact severity model of wheeled vehicles was based on the balance between the centrifugal force of the vehicle and the soil shearing force that was related to vehicle dynamic parameters. For tracked vehicles, the soil shear stress was primarily de- rived from the lateral displacement of the tracks, not the centrifugal force, thus the impact severity model of tracked vehicles was based on the relationship between soil shear stress and soil lateral displacement caused by the lateral movement of the tracks. Field tests of both wheeled vehicles and tracked vehicles were conducted at different test sites with different soil types and soil strength. The wheeled vehicles included a High Mobility Multipurpose Wheeled Vehicle (HMMWV), and a Light Armored Vehicle (LAV). The tracked vehicles included an M1A1 tank, an M577 armored personal carrier (APC), and an M548 cargo carrier. The field test data supported the prediction of terrain impact models. The average per- centage errors of the disturbed width model of the LAV and the HMMWV were 19.5 % and 8.6 %, respectively. The average percentage errors of the impact severity model for the LAV were 48.5 % and 34.2 % for the high-speed (9.6 m/s) test and low-speed (5.4 m/s) test, respectively. The average percentage errors of the disturbed width model for the M1A1, M577, and the M548 were 10.0 %, 27.3 %, and 8.5 %, respectively. The average percent- age errors of the impact severity model of the M1A1 and M577 were 25.0 % and 21.4 %, respectively

Vision-Aided Navigation for GPS-Denied Environments Using Landmark Feature Identification

In recent years, unmanned autonomous vehicles have been used in diverse applications because of their multifaceted capabilities. In most cases, the navigation systems for these vehicles are dependent on Global Positioning System (GPS) technology. Many applications of interest, however, entail operations in environments in which GPS is intermittent or completely denied. These applications include operations in complex urban or indoor environments as well as missions in adversarial environments where GPS might be denied using jamming technology. This thesis investigate the development of vision-aided navigation algorithms that utilize processed images from a monocular camera as an alternative to GPS. The vision-aided navigation approach explored in this thesis entails defining a set of inertial landmarks, the locations of which are known within the environment, and employing image processing algorithms to detect these landmarks in image frames collected from an onboard monocular camera. These vision-based landmark measurements effectively serve as surrogate GPS measurements that can be incorporated into a navigation filter. Several image processing algorithms were considered for landmark detection and this thesis focuses in particular on two approaches: the continuous adaptive mean shift (CAMSHIFT) algorithm and the adaptable compressive (ADCOM) tracking algorithm. These algorithms are discussed in detail and applied for the detection and tracking of landmarks in monocular camera images. Navigation filters are then designed that employ sensor fusion of accelerometer and rate gyro data from an inertial measurement unit (IMU) with vision-based measurements of the centroids of one or more landmarks in the scene. These filters are tested in simulated navigation scenarios subject to varying levels of sensor and measurement noise and varying number of landmarks. Finally, conclusions and recommendations are provided regarding the implementation of this vision-aided navigation approach for autonomous vehicle navigation systems

Vegetative Recovery of Military Vehicle Impacts at Fort Lewis, WA

Vehicles driven off-road damage the soil and vegetation on the terrain, which can cause soil erosion and degradation of the landscape. This type of damage occurs on military installations due to training. Military training lands must be managed in an attempt to minimize the overall impacts of training on the terrain. The Army Training and Testing Area Carrying Capacity (ATTACC) is a model used by the U.S. Army to manage their training lands. Methods of determining the impacts produced by a vehicle and subsequent vegetative recovery have been used at Fort Lewis, WA for the Light Armored Vehicle (LAV). The LAV is an eight-wheeled vehicle with a maximum curb weight of approximately 14,000 kg. In June of 2003, the vehicle was operated in spiral patterns (five high-speed and five low-speed), and the impacts of the vehicle were assessed at this time. Measurements were taken at 13-20 points along each of the 10 spirals. The impact measurements taken at each point were disturbed width and impact severity. The impacts were reassessed after six months and one year to determine recovery from the initial damage. Different types of impacts (imprint, scrape, combination, and pile) were determined based on the characteristics of the damage produced. The recovery of these different impact types was also assessed. The study site at Fort Lewis was found to have an overall vegetative recovery of 43% after one year, but the different impact types varied in the amount of recovery. Imprint impact types had an almost complete recovery of 74%, while the scrape and combination showed little recovery (11% and 22%, respectively) after one year. The pile also showed a high recovery of 54%. Areas where the vehicle was operated at low speeds showed high recovery (78%). Recovery was much lower (29%) for areas where the vehicle was operated at high speeds. The damage produced was higher and recovery lower when the vehicle was turning sharply. The data produced by this study will be useful in managing the training with LAVs at Fort Lewis by implementation into the ATTACC model. Further study must be done to determine when these impacts would be fully recovered from the damage. The results found in this study are only applicable to the LAV and Fort Lewis. Other vehicles produce different impacts, and other locations have different climates, soils, and vegetation types that would respond differently to vehicle impacts. The methods used in this study can be utilized at other locations and with different vehicles to provide applications to more sites and a wider variety of vehicles