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Full-Text Articles in Engineering
Reinforcement Learning For Mobile Robot Collision Avoidance In Navigation Tasks, Zilong Jiao
Reinforcement Learning For Mobile Robot Collision Avoidance In Navigation Tasks, Zilong Jiao
Dissertations - ALL
Collision avoidance is fundamental for mobile robot navigation. In general, its solutions include: {\it map-based} and {\it mapless approaches.} In the map-based approach, robots pre-plan collision-free paths based on an environment map and follow their paths during navigation. On the other hand, the mapless approach requires robots to avoid collisions without referencing to an environment map. This thesis first studies the map-based approach for multiple robots to collectively build environment maps. In this study, a robot following a pre-planned path may encounter unexpected obstacles, such as other moving robots and obstacles inaccurately presented on an environment map. This motivates us …
Reinforcement Learning Approach For Inspect/Correct Tasks, Hoda Nasereddin
Reinforcement Learning Approach For Inspect/Correct Tasks, Hoda Nasereddin
LSU Doctoral Dissertations
In this research, we focus on the application of reinforcement learning (RL) in automated agent tasks involving considerable target variability (i.e., characterized by stochastic distributions); in particular, learning of inspect/correct tasks. Examples include automated identification & correction of rivet failures in airplane maintenance procedures, and automated cleaning of surgical instruments in a hospital sterilization processing department. The location of defects and the corrective action to be taken for each varies from task episode. What needs to be learned are optimal stochastic strategies rather than optimization of any one single defect type and location. RL has been widely applied in robotics …
Artificial Intelligence Enabled Distributed Edge Computing For Internet Of Things Applications, Georgios Fragkos
Artificial Intelligence Enabled Distributed Edge Computing For Internet Of Things Applications, Georgios Fragkos
Electrical and Computer Engineering ETDs
Artificial Intelligence (AI) based techniques are typically used to model decision-making in terms of strategies and mechanisms that can conclude to optimal payoffs for a number of interacting entities, often presenting competitive behaviors. In this thesis, an AI-enabled multi-access edge computing (MEC) framework is proposed, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate Internet of Things (IoT) applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes’ communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure …
Socially Aware Network User Mobility Analysis And Novel Approaches On Aerial Mobile Wireless Network Deployment, Ismail Uluturk
Socially Aware Network User Mobility Analysis And Novel Approaches On Aerial Mobile Wireless Network Deployment, Ismail Uluturk
USF Tampa Graduate Theses and Dissertations
Service demand patterns for wireless networks are evolving with the technological developments in areas such as personal computing, unmanned vehicles, and internet-of-things, where increasing mobile service demand is one of the significant challenges introduced. In addition to these new intrinsic dynamics, natural disasters and societal upheaval are also disrupting the conventional patterns of network demand. Situations like damaged infrastructure due to a natural disaster or large numbers of displaced people caused by political strife and social upheaval demand flexible, rapidly deployable network architectures. The increasing demands of next-generation communication services are straining the capabilities of the traditional approach of the …
A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal
A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal
Theses and Dissertations--Electrical and Computer Engineering
Advances in computing power in recent years have facilitated developments in autonomous robotic systems. These robotic systems can be used in prosthetic limbs, wearhouse packaging and sorting, assembly line production, as well as many other applications. Designing these autonomous systems typically requires robotic system and world models (for classical control based strategies) or time consuming and computationally expensive training (for learning based strategies). Often these requirements are difficult to fulfill. There are ways to combine classical control and learning based strategies that can mitigate both requirements. One of these ways is to use a gravity compensated torque control with reinforcement …
Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi
Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi
Honors Theses and Capstones
In this paper, I develop a hierarchical Markov Decision Process (MDP) structure for completing the task of vertical rocket landing. I start by covering the background of this problem, and formally defining its constraints. In order to reduce mistakes while formulating different MDPs, I define and develop the criteria for a standardized MDP definition format. I then decompose the problem into several sub-problems of vertical landing, namely velocity control and vertical stability control. By exploiting MDP coupling and symmetrical properties, I am able to significantly reduce the size of the state space compared to a unified MDP formulation. This paper …
A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan
A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan
Open Access Theses & Dissertations
Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has …