10 citations · 15 across the 13 of their papers we have counts for
13 papers
A Data-Based Architecture for Flight Test without Test Points
D. Isaiah Harp, Joshua Ott, John Alora +1
The justification for the "test point" derives from the test pilot's obligation to reproduce faithfully the pre-specified conditions of some model prediction. Pilot deviation from…
An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments
Ali Agha, Kyohei Otsu, Benjamin Morrell +86
This paper presents an appendix to the original NeBula autonomy solution developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), participating in the DARPA Sub…
Physics-informed Gaussian Processes for Safe Envelope Expansion
D. Isaiah Harp, Joshua Ott, Dylan M. Asmar +2
Flight test analysis often requires predefined test points with arbitrarily tight tolerances, leading to extensive and resource-intensive experimental campaigns. To address this ch…
Informative Input Design for Dynamic Mode Decomposition
Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd
Efficiently estimating system dynamics from data is essential for minimizing data collection costs and improving model performance. This work addresses the challenge of designing f…
Trajectory Optimization for Adaptive Informative Path Planning with Multimodal Sensing
Joshua Ott, Edward Balaban, Mykel Kochenderfer
We consider the problem of an autonomous agent equipped with multiple sensors, each with different sensing precision and energy costs. The agent's goal is to explore the environmen…
Learning-based Methods for Adaptive Informative Path Planning
Marija Popovic, Joshua Ott, Julius Rückin +1
Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments…
Approximate Sequential Optimization for Informative Path Planning
Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd
We consider the problem of finding an informative path through a graph, given initial and terminal nodes and a given maximum path length. We assume that a linear noise corrupted me…
Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs
Muhammad Fadhil Ginting, Sung-Kyun Kim, Oriana Peltzer +4
To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and…
Adaptive Coverage Path Planning for Efficient Exploration of Unknown Environments
Amanda Bouman, Joshua Ott, Sung-Kyun Kim +5
We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked w…
Fast and Scalable Signal Inference for Active Robotic Source Seeking
Christopher E. Denniston, Oriana Peltzer, Joshua Ott +6
In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking…
Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing
Joshua Ott, Edward Balaban, Mykel J. Kochenderfer
Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers the problem of an agent equipped with multiple sensors, each with different sensing accuracy and energ…
Risk-aware Meta-level Decision Making for Exploration Under Uncertainty
Joshua Ott, Sung-Kyun Kim, Amanda Bouman +6
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, loc…
FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget
Oriana Peltzer, Amanda Bouman, Sung-Kyun Kim +8
We present a method for autonomous exploration of large-scale unknown environments under mission time constraints. We start by proposing the Frontloaded Information Gain Orienteeri…