There is growing commercial interest in the use of unmanned aerial vehicles (UAVs) in urban environments, specifically for package delivery applications. However, the size, complexity and sheer numbers of expected UAVs makes conventional air traffic management that relies on human air traffic controllers infeasible. To enable UAVs to safely and...
While robotic systems may have once been relegated to structured environments and automation style tasks, in recent years these boundaries have begun to erode. As robots begin to operate in largely unstructured environments, it becomes more difficult for them to effectively interpret their surroundings. As sensor technology improves, the amount...
This thesis presents a decentralized communication planning algorithm for cooperative terrain-based navigation (dec-TBN) with autonomous underwater vehicles. The proposed algorithm uses forward simulation to approximate the value of communicating at each time step. The simulations are used to build a directed acyclic graph that can be searched to provide a...
We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
Emerging applications for robotic data collection include ocean monitoring, emergency response and urban search and rescue. At the core of these applications is a robot's ability to make informed decisions on incomplete data. This dissertation addresses this problem by developing novel techniques for modeling and estimating structured environments using deep...
Deep learning has recently revolutionized robot perception in many canonical robotic applications, such as autonomous driving. However, a similar transformation has yet to occur in more harsh environments including underwater and underground. This is due in part to the difficulty in deploying robots in these environments, which lack large real...
Multi-robot systems are versatile and extremely capable of exploration tasks in complex environments. Increasingly sophisticated planners, which incorporate new features of a multi-robot system, are necessary for the operation of the systems. Marsupial robots are multi-robot systems consisting of a carrier robot (e.g., a ground vehicle), which is highly capable...
Human-robot teams are invaluable for mapping unknown environments, exploring difficult-to-reach areas, and manipulating inaccessible equipment. However, guiding autonomous robots requires dealing with these dynamic domains while synthesizing a significant amount of data and balancing competing objectives. Current mission planning methods often involve manually specifying low-level parameters of the mission, such...
Motion planning is a cornerstone of autonomous robots, enabling robots to safely and efficiently perform tasks such as package delivery, infrastructure inspection, and manipulation. However, as the field of robotics matures, robotic systems are being developed that (1) are challenging to analytically model, (2) require computationally expensive model-based controllers, and...
Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This thesis proposes a method using Model Predictive Control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a Linear Wave Theory (LWT) solver to approximate the component...
In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when...
Tensegrity structures are composed of pure compressional elements that are connected via a network of pure tensional elements. The concept of tensegrity promises numerous advantages to the field of robotics. Tensegrity robots are, however, notoriously difficult to control due to their oscillatory nature and nonlinear interaction between the components. Multiagent...
As robotic systems become increasingly prevalent in our lives (e.g., by harvesting food, assisting with disaster response and defense, and transporting persons and our goods around the world) there is a growing need to ensure that they can cooperate to achieve their intended goals. Robotic cooperation in the real-world is...
Unmanned aerial vehicle (UAV) technology has grown out of traditional research and military applications and has captivated the commercial and consumer markets, showing the ability to perform a spectrum of autonomous functions. This technology has the capability of saving lives in search and rescue, fighting wildfires in environmental monitoring, and...
Performing autonomous robotic tasks in the field, such as ocean monitoring and aerial surveillance, requires planning and executing paths in dynamic environments. In these uncertain and changing environments, it is not uncommon to see a large difference between the path planned by the robotic vehicle and the path that the...
The effectiveness of robot autonomy is governed by the ability to make decisions based on online sensor measurements and a prior belief of the environment. Uncertainty in the environment introduces challenges to robotic decision making. This thesis address two key robot decision making problems: exploration and navigation. The robotic exploration...
Information gathering tasks, such as terrestrial search and rescue, aerial inspection, and marine monitoring, require robotic unmanned systems to make decisions on how to travel within an environment to maximize or minimize a path-dependent information objective function. The distribution of information throughout the environment is the result of various processes,...