This thesis summarizes an approach for building a trajectory-tracking framework for autonomous robots working in low-speed and controlled space. A modularized robot framework can provide easy access to hardware and software replacement, which can be a tool for validating trajectory-tracking algorithms in controlled laboratory conditions. An introduction to other existing methods for trajectory tracking is presented. These advanced trajectory control methods and studies aim to improve trajectory tracking control for better performance under different environments. This research uses ROS as the middleware for connecting the actuators and computing units. A market-existing global position measurement tool, the UWB system, was selected as the primary localization sensor. A Raspberry Pi and an Arduino Uno are used for high-level and low-level control. The separation of the control units benefits the modularization design of the framework. A robust control approach has also been introduced to prevent the disturbance of uneven terrain to improve the framework's capability to drive arbitrary robot chassis in different testing grounds. During each stage of development, there are offline and online tests for live control tests. The trajectory tracking controller requires a robot kinematic model and tracking control program for better results of controlled behaviour. A custom trajectory control program was made and implemented into the tests. A digital simulation and a physical robot are built to validate the algorithm and the designed framework for performance validation. This framework aims to suit the other scholar's developments and can be used as a testing platform to implement their autonomous driving algorithms or additional sensors. By replacing the control algorithm in the existing trajectory-tracking robotic framework, this autonomous, universal platform may benefit the validation of these algorithms' performance in the field experiment. / Thesis / Master of Applied Science (MASc) / This thesis contains five chapters. Chapter 1 provided the information and background for this research topic regarding the key components, methods, and tools for creating trajectory tracking. Chapter 2 focuses on the existing methods and deep study of tools, equipment and hardware setups for trajectory tracking in simulation and physical setups. The experiments referenced from other studies can benefit the research and development work for the current trajectory tracking development work. The review provides different kinematics models for robot layouts, which impacts the final design of the field experiment robot. Chapter 3 presents the design work process for creating a controller based on the final field experiment robot. This chapter provides steps and considerations while building the control system for a trajectory-tracking robot from scratch. Chapter 4 demonstrates the simulation results and field experiment results. A study of error analysis and repeatability justification can also be found in this chapter. Chapter 5 summarizes the research and development contribution, primary findings, and concerns for identified problems.

The goal of this study is to determine if trust in autonomous vehicles is affected by whether the vehicle is being operated in a closed or open system. A PRQF survey method was used to complete this study. The survey contained items to assess pedestrian behavior, personal innovativeness, and receptivity to autonomous vehicles. Scenario questions were also utilized to determine differences in the trust of automated vehicles in open and closed settings. The results from this study indicated increased pedestrian receptivity scores for the closed system (M=14.11, SD=3.78), compared to the open system (M=13.70, SD=3.90). Average trust scores were also increased for the closed system (M=4.68, SD=1.82) compared to the open system (M=4.56, SD=1.85). These results were used to conclude that trust and receptivity of autonomous vehicles were increased for closed systems.

Design and development of a 1/10 scale prototype of a Full Dimension Continuous Haulage System manufactured by the Long-Airdox Company. The prototype, which will allow development and testing of path-planning and control algorithms for autonomous navigation and operation in underground coal mines, has been completed. The prototype system, though not an identical copy, clones all full-scale model degrees of freedom and functions necessary for navigation. In addition to the physical structure, a microcontroller-based system was developed for providing the necessary low-level motor controls, data gathering and multiple processor communications. High level software running on a laptop PC with the windows operating system is used for analyzing all measurement data, execution of path-planning and control algorithms and issuing the command data. / Master of Science

<div>The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.</div>

There is a significant amount of ongoing research on developing multi-agent algorithms for mobile robots. Moving those algorithms beyond simulation and into the real world requires multi-robot testbeds. However, there is currently no easily accessible source of information for guiding the creation of such a testbed. In this thesis, we describe the process of creating a testbed at Purdue University involving a set of unmanned aerial vehicles (UAVs). We discuss the components of the testbed, including the software that is used to interface with the UAVs. We also describe the challenges that we faced during the setup process, and evaluate the UAV platforms that we are using. Finally, we demonstrate the implementation of a multi-agent task allocation algorithm on our testbed.

Deploying robots in unknown and complex areas for inspection tasks is becoming a real need for various application scenarios. Recently, there has been an increasing interest to develop and use autonomous aerial robots in environments such as urban voids and subterranean mine tunnels, aiming to decrease the human presence in dangerous or inaccessible areas. These areas are characterized by complete darkness and narrow tunnels, where the ground can often be rough and not traversible for mobile vehicles, thus the developments focus on Micro Aerial Vehicles (MAVs). MAVs are mechanically simple and agile platforms that can navigate through cluttered areas and have the potential to perform complex exploration tasks when equipped with proper onboard sensors. One of the key milestones in the development of autonomous robots is self-exploration. The definition of self-exploration according to [7] is "the act of moving through an unknown environment while building a map that can be used for subsequent navigation". By reaching this milestone, robots would be freed from the limitation of requiring already existing maps for navigation. In this thesis, a frontier-based exploration algorithm is established and evaluated to understand how such method could be used to reach the self-exploration milestone. By marking the border between what is known and unknown the method is able to determine the next desired position for the robot to expand the map. The resulting algorithm, together with a path planning method and 3-dimensional mapping framework, the method was tested and examined in simulated environments with different levels of complexity.

No description available.

Self-driving cars used to be a concept of a future society. However, through years of research, testing, and dedication they are becoming a modern day reality. To further expand research and testing capabilities in the field of autonomous vehicles, an Autonomous Vehicle Research Platform (AVRP) can be developed. The purpose of an AVRP is to provide researchers with an autonomous ground vehicle testing platform they can outfit with sensors and equipment to meet their specific research needs. The platform will give researchers the capabilities to test algorithms, new sensors, navigation, new technologies, etc. that they believe would help advance autonomous vehicles. When their testing is done, their equipment can be removed so the next researcher can utilize the platform. The scope of this thesis is to develop the operational specifications for an AVRP that can operate at level 4 autonomy. These specifications include navigation and sensing hardware, such as LIDAR, radar, ultrasonic, cameras, and important specifications that pertain to using each, as well as a review of optimal mounting locations. It will also present benchmarks for computing, design specs for power and communication buses, and modifications for universal mounting racks. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.

No description available.