Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments. / Master of Science / Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.

abstract: In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP). In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners. / Dissertation/Thesis / Masters Thesis Computer Science 2019

As the world is evolving very fast with the developments of new technologies and softwares in design and manufacturing, business organizations and manufacturing industries will always be adapting to the new technologies and softwares for increasing the cost and time efficiency in the development of products. So, this thesis focuses on a comparative study between two Dassault Systems softwares in which, one is mostly used CAD software by industries for a long time, and one is the latest developments in the CAD softwares with satisfying business requirements. For this comparison study, the two methods called design automation and robot simulation are used in the development of modular fixtures platforms used in automobile manufacturing industries. In the first method, the design and assembly of modular fixtures platform are done which holds the automotive car sheet pillars together. With a single mouse click, the complete design and assembly of the modular fixtures can be done using automation. In the second method, the spot-welding manufacturing operation is done to join the car sheet pillars together to produce the B-pillar of the Body in white (BIW) for the automobile, with the help of a welding gun connected to ABB robot arm, using automation in robot simulation. This work takes place in CATIA V5 and 3D Experience, and the final results obtained in both the software are compared and discussed in the results part of this report. Automation in CAD has been one of the advanced developments that happened in the 21st century through which most of the engineering knowledge and intent can be captured and reutilized. CATIA V5 & 3D Experience Automation is done using two programming languages called VB (Visual Basics) and VB.net.