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Articles 1 - 12 of 12
Full-Text Articles in Robotics
Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu
Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu
Master's Theses
The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete.
In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic …
Decentralized, Noncooperative Multirobot Path Planning With Sample-Basedplanners, William Le
Decentralized, Noncooperative Multirobot Path Planning With Sample-Basedplanners, William Le
Master's Theses
In this thesis, the viability of decentralized, noncooperative multi-robot path planning algorithms is tested. Three algorithms based on the Batch Informed Trees (BIT*) algorithm are presented. The first of these algorithms combines Optimal Reciprocal Collision Avoidance (ORCA) with BIT*. The second of these algorithms uses BIT* to create a path which the robots then follow using an artificial potential field (APF) method. The final algorithm is a version of BIT* that supports replanning. While none of these algorithms take advantage of sharing information between the robots, the algorithms are able to guide the robots to their desired goals, with the …
Involuntary Signal-Based Grounding Of Civilian Unmanned Aerial Systems (Uas) In Civilian Airspace, Keith Conley
Involuntary Signal-Based Grounding Of Civilian Unmanned Aerial Systems (Uas) In Civilian Airspace, Keith Conley
Master's Theses
This thesis investigates the involuntary signal-based grounding of civilian unmanned aerial systems (UAS) in unauthorized air spaces. The technique proposed here will forcibly land unauthorized UAS in a given area in such a way that the UAS will not be harmed, and the pilot cannot stop the landing. The technique will not involuntarily ground authorized drones which will be determined prior to the landing. Unauthorized airspaces include military bases, university campuses, areas affected by a natural disaster, and stadiums for public events. This thesis proposes an early prototype of a hardware-based signal based involuntary grounding technique to handle the problem …
An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi
An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi
Master's Theses
The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have been widely used for high-order nonlinear system control. This thesis studies the application of sliding mode control …
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Master's Theses
Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …
Corridor Navigation For Monocular Vision Mobile Robots, Matthew James Ng
Corridor Navigation For Monocular Vision Mobile Robots, Matthew James Ng
Master's Theses
Monocular vision robots use a single camera to process information about its environment. By analyzing this scene, the robot can determine the best navigation direction. Many modern approaches to robot hallway navigation involve using a plethora of sensors to detect certain features in the environment. This can be laser range finders, inertial measurement units, motor encoders, and cameras.
By combining all these sensors, there is unused data which could be useful for navigation. To draw back and develop a baseline approach, this thesis explores the reliability and capability of solely using a camera for navigation. The basic navigation structure begins …
Artificial Neural Network-Based Robotic Control, Justin Ng
Artificial Neural Network-Based Robotic Control, Justin Ng
Master's Theses
Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.
Towards Autonomous Localization Of An Underwater Drone, Nathan Sfard
Towards Autonomous Localization Of An Underwater Drone, Nathan Sfard
Master's Theses
Autonomous vehicle navigation is a complex and challenging task. Land and aerial vehicles often use highly accurate GPS sensors to localize themselves in their environments. These sensors are ineffective in underwater environments due to signal attenuation. Autonomous underwater vehicles utilize one or more of the following approaches for successful localization and navigation: inertial/dead-reckoning, acoustic signals, and geophysical data. This thesis examines autonomous localization in a simulated environment for an OpenROV Underwater Drone using a Kalman Filter. This filter performs state estimation for a dead reckoning system exhibiting an additive error in location measurements. We evaluate the accuracy of this Kalman …
A Lidar Based Semi-Autonomous Collision Avoidance System And The Development Of A Hardware-In-The-Loop Simulator To Aid In Algorithm Development And Human Studies, Thomas F. Stevens
A Lidar Based Semi-Autonomous Collision Avoidance System And The Development Of A Hardware-In-The-Loop Simulator To Aid In Algorithm Development And Human Studies, Thomas F. Stevens
Master's Theses
In this paper, the architecture and implementation of an embedded controller for a steering based semi-autonomous collision avoidance system on a 1/10th scale model is presented. In addition, the development of a 2D hardware-in-the-loop simulator with vehicle dynamics based on the bicycle model is described. The semi-autonomous collision avoidance software is fully contained onboard a single-board computer running embedded GNU/Linux. To eliminate any wired tethers that limit the system’s abilities, the driver operates the vehicle at a user-control-station through a wireless Bluetooth interface. The user-control-station is outfitted with a game-controller that provides standard steering wheel and pedal controls along …
Telepresence: Design, Implementation And Study Of An Hmd-Controlled Avatar With A Mechatronic Approach, Darren Michael Chan
Telepresence: Design, Implementation And Study Of An Hmd-Controlled Avatar With A Mechatronic Approach, Darren Michael Chan
Master's Theses
Telepresence describes technologies that allow users to remotely experience the sensation of being present at an event without being physically present. An avatar exists to represent the user whilst in a remote location and is tasked to collect stimuli from its immediate surroundings to be delivered to the user for consumption. With the advent of recent developments in Virtual Reality technology, viz., head-mounted displays (HMDs), new possibilities have been enabled in the field of Telepresence. The main focus of this thesis is to develop a solution for visual Telepresence, where an HMD is used to control the direction of a …
Robust Region Tracking In Multi-Agent Systems Utilizing Sliding Mode Control: Theory And Applications, Mark Bacon
Robust Region Tracking In Multi-Agent Systems Utilizing Sliding Mode Control: Theory And Applications, Mark Bacon
Master's Theses
This thesis presents a methodology to bring controlled agents within a moving region despite agent interaction dynamics, uncertain forces and parameter variation. The logic is derived from traditional Sliding Mode Control theory with an expanded boundary layer which allows position deviation from the region center to specified bounds. As an example of the utility of this control, multiple methods of herding (controlling passive agents by appropriate positioning of controlled agents) are presented.
Multiple Robot Boundary Tracking With Phase And Workload Balancing, Michael Jay Boardman
Multiple Robot Boundary Tracking With Phase And Workload Balancing, Michael Jay Boardman
Master's Theses
This thesis discusses the use of a cooperative multiple robot system as applied to distributed tracking and sampling of a boundary edge. Within this system the boundary edge is partitioned into subsegments, each allocated to a particular robot such that workload is balanced across the robots. Also, to minimize the time between sampling local areas of the boundary edge, it is desirable to minimize the difference between each robot’s progression (i.e. phase) along its allocated sub segment of the edge. The paper introduces a new distributed controller that handles both workload and phase balancing. Simulation results are used to illustrate …