Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
Browse
2 results
Search Results
Item Outdoor Localization and Path Planning for Repositioning a Self-Driving Electric Scooter(2023) Poojari, Srijal Shekhar; Paley, Derek; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The long-term goal of this research is to develop self-driving e-scooter technology to increase sustainability, accessibility, and equity in urban mobility. Toward this goal, in this work, we design and implement a self-driving e-scooter with the ability to safely travel along a pre-planned route using automated, onboard control without a rider. We also construct an autonomous driving framework by synthesizing open-source robotics software libraries with custom-designed modules specific to an e-scooter, including path planning and state estimation. The hardware and software development steps along with design choices and pitfalls are documented. Results of real-world autonomous navigation of our retrofitted e-scooter, along with the effectiveness of our localization methods are presented.Item SALAM: A SCALABLE ANCHOR-FREE LOCALIZATION ALGORITHM FOR WIRELESS SENSOR NETWORKS(2006-04-26) Youssef, Adel Amin Abdel Azim; Agrawala, Ashok K; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we present SALAM, a scalable anchor-free protocol for localization in wireless sensor networks. SALAM can determine the positions of sensor nodes without any infrastructure support. We assume that each node has the capability to estimate distances to its corresponding neighbors, that are within its transmission range. SALAM allows to trade the accuracy of the estimated position against node transmission range and/or computational power. The application layer can choose from a whole range of different options, to estimate the sensor node's positions with different accuracy while conserving battery power. Scalability is achieved by dividing the network into overlapping multi-hop clusters each with its own cluster head node. Each cluster head is responsible for building a local relative map corresponding to its cluster using intra-cluster node's range measurements. To obtain the global relative topology of the network, the cluster head nodes collaboratively combine their local maps using simple matrix transformations. In order for two cluster heads to perform a matrix transformation, there must be at least three boundary nodes that belongs to both clusters (i.e. the two clusters are overlapping with degree 3). We formulate the overlapping multi-hop clustering problem and present a randomized distributed heuristic algorithm for solving the problem. We evaluate the performance of the proposed algorithm through analytical analysis and simulation. A major problem with multi-hop relative location estimation is the error accumulated in the node position as it becomes multi-hop away from the cluster head node. We analyze different sources of error and develop techniques to avoid these errors. We also show how the local coordinate system (LCS) affects the accuracy and propose different heuristics to select the LCS. Simulation results show that SALAM achieves precise localization of sensors. We show that our approach is scalable in terms of communication overhead regardless of the network size. In addition, we capture the impact of different parameters on the accuracy of the estimated node's positions. The results also show that SALAM is able to achieve accuracy better than the current ad-hoc localization algorithms.