UGVs

The present decade has seen an upsurge in the research on the applications of autonomous vehicles and drones to present innovative and sustainable solutions for traditional transportation and logistical challenges. Similarly, in this study, we propose using autonomous cars and drones to resolve conventional logistics and transport challenges faced by international humanitarian organizations (IHOs) during a relief operation. We do so by identifying, shortlisting, and elaborating critical success factors or key transport and logistics challenges from the existing humanitarian literature and present a conceptual model to mitigate these challenges by integrating unmanned ground (UGVs) and aerial vehicles (UAVs) in the humanitarian supply chain. To understand how this novel idea of using UGVs and UAVs could help IHOs, we drafted three research questions, first focusing on the identification of existing challenges, second concentrating on remediation of these challenges, and the third to understand realization timeline for UGVs and UAVs. This lead to the development of a semi-structured, open-ended questionnaire to record the respondents' perspectives on the existing challenges and their potential solutions. We gathered data form, ten interviewees, with substantial experience in the humanitarian sector from six IHOs stationed in Pakistan and Austria. In light of the feedback for the second research question, we present a conceptual model of integrating UAVs and UGVs in the relief chain. The results of the study indicate that technological advancement in mobility withholds the potential to mitigate the existing challenges faced by IHOs. However, IHOs tend to be reluctant in adapting UGVs compared to UAVs. The results also indicate that the adaptation of these technologies is subject to their technical maturity, and there are no significant differences in opinions found between the IHOs from Pakistan and Austria.

The onset of Unmanned Ground Vehicles (UGVs) date back to World War II. These full autonomous robots or remote-controlled robots provide many services for military purposes. The deployment of UGVs in battlefield keeps the soldiers safe from harm, navigates the target points and works as a path tracker. As time goes by, researchers are encouraged to apply UGVs in other domains as in the industrial, road services and the urban domains. The autonomy of UGVs comes from the collective sensory resources and the manipulators that are used to perform specialized tasks. This paper presents the design of a fully autonomous vehicle called " E500 " , which has been implemented to compete in the 22nd Intelligent Ground Vehicle Competition (IGVC), held at Oakland University, Rochester, Michigan in June 2014. The E500's body and chassis are custom made. Its power plant is based on two scooter electric motors that are driven through Pulse Width Modulation (PWM). It receives the information from a camera, few ultrasonic range finding sensors and global positioning system (GPS) receiver. The unmanned vehicle also incorporates vision and navigation systems. They are implemented to meet the design requirements of the IGVC competition. The E500's vision system acquires the images through a Microsoft camera then processes them on an onboard laptop. The vehicle was able to extract the features of the road, detecting the white lines and the position of obstacles then figure out the best path to avoid collisions. A navigation algorithm has been developed to achieve high accuracy up to 10 cm using a Samsung mobile phone running android. The algorithms were tested in a green area with two white lines and some obstacles distributed in a random way.

Dynamic vehicle detection is an important module for Autonomous Land Vehicle (ALV) navigation in outdoor environments. In this paper, we present a novel dynamic vehicle detection algorithm based on the likelihood field model for an ALV equipped with a Velodyne LIDAR. An improved 2D virtual scan is utilized to detect the dynamic objects with the scan differencing operation. For every dynamic object, a vehicle is fitted with the likelihood field model, and the motion evidence and motion consistence of the fitted vehicle are exploited to classify the dynamic object into the vehicle or not. The performance of the algorithm is validated on the data collected by our ALV in various environments.

The dissertation portrays the development of the Unmanned Ground Vehicle (UGV) that is capable of navigating through complex environments. The objectives of the project were accomplished where Robot Operating Systems (ROS) packages have been employed into the Nexus robot and programming microcontroller which is connected to external devices such as sensors and motor controller to be integrated with ROS.  An investigation was conducted to explore existing approaches and hardware devices to attain suitable methods that can be applied in the robot design. The project involves using Simultaneous Localisation And  Mapping (SLAM) to map and localise the landscape and the  Navigation_Stack was used to provide a target to reach, plans a path while avoiding obstacles. In addition, the Nexus robot applied a PD controller with the aim of controlling and regulating the amount of voltage going to the motors and reducing the positional error caused by the difference in speed of the motors. Nexus robot was able to partially control and regulate the voltage going to motors and maintained the positional error. However, during the testing phase, the laser finder was damaged which led to using Gazebo to simulate the robot. The robot in the simulation was able to navigate and map terrain while avoiding obstacles. Some challenges were faced regarding the control system and movement of the Nexus robot; several improvements are suggested.