Master of Science, The Ohio State University, 2019, Mechanical Engineering

This thesis investigates a metaheuristic optimization method to solve a mission planning problem which requires the solar-powered unmanned ground vehicle (UGV) to frequently visit multiple assigned points in outdoor environment with a desired performance index. The mission planning problem can be decoupled into two parts: a) a decision-making problem of the visiting sequence, which can be formulated as a traveling salesman problem; b) a motion planning problem for the entire mission with complex constraints. The major difficulty of solving this problem comes from the time-varying outdoor environment, the energy constraint, multiple terrain types and mixed-type decision variables. In order to solve the problem effectively and efficiently, a hybrid cascaded heuristic optimization algorithm is developed to generate an optimized motion plan such that the objective function is minimized under constraints. To obtain the environmental information, a solar irradiance map of the operational area is constructed from satellite images at the beginning and an initial path is generated using the proposed algorithm. In the following repeated routes through the operational area, the updated solar irradiance map built from on-board camera images will be applied to provide more accurate environmental information for the re-planned path, such that the UGV could have better performance of the assigned mission. Experiments in outdoor environment were conducted to validate the methods presented in a structured, highly-explored region to achieve energy sustainable mission.

Committee: Ran Dai (Advisor); Wei Zhang (Committee Member) Subjects: Robotics

Master of Science (MS), Ohio University, 2016, Electrical Engineering (Engineering and Technology)

A vehicle-to-vehicle follower design based on RC Unmanned Ground Vehicle (UGV) is presented in this thesis. To achieve the desired performance for two-vehicle leader-follower, a 3DOF path trajectory tracking controller with a close-loop guidance controller are used, which consider both the kinematics and the dynamics characteristics of the vehicle model. In our research, we use a Trajectory Linearization Control (TLC) to achieve the path tracking, and a PID controller to guide the preceding vehicle. To this end, the following objectives have been achieved. First, a 3DOF kinematics and dynamics vehicle model has been built. Second, an Adaptive Cruise Control (ACC) Trajectory Linearizaton Control (ACCTLC) scheme is presented. Third, a Vehicle-to-Vehicle following Trajectory Linearizaiton Control is proposed. MATLAB/SIMULINK simulation testing of 3DOF control algorithm is presented, which verifies the algorithm. Future work include implementing the current controller design by installing those algorithms to the real RC car; as well as adding lane constraint to the current work; and adding obstacle avoidance to develop fully autonomous ground vehicle.

Committee: Michael Braasch (Advisor); Jim Zhu (Committee Co-Chair) Subjects: Electrical Engineering

Master of Science (M.S.), University of Dayton, 2013, Electrical Engineering

We present a 3D reconstruction algorithm designed to support various automation and navigation applications. The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point cloud model of its unknown surroundings. In this thesis, we present the step by step methodology of the development of a reconstruction technique. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. In an improved version of the algorithm, we utilized optical flow features to create an extremely dense representation model. Although dense, this model is hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear super resolution. With this addition, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. Our final contribution of additional preprocessing steps is designed to filter noise points and mismatched features, giving birth to the complete Dense Point-cloud Representation (DPR) technique. We measure the success of DPR by evaluating the visual appea (open full item for complete abstract)

Committee: Asari Vijayan PhD (Committee Chair); Raul Ordonez PhD (Committee Member); Eric Balster PhD (Committee Member) Subjects: Electrical Engineering; Engineering