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Found 10 papers, 3 papers with code
FaSTraP: Fast and Safe Trajectory Planner for Flights in Unknown Environments
The desire of maintaining computational tractability typically leads to optimization problems that do not include the obstacles (collision checks are done on the solutions) or to formulations that use a convex decomposition of the free space and then impose an ad hoc allocation of each interval of the trajectory in a specific polyhedron.
Robotics
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown.
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan.
Robotics
Hierarchical Bayesian Noise Inference for Robust Real-time Probabilistic Object Classification
This paper presents an approach for filtering sequences of object classification probabilities using online modeling of the noise characteristics of the classifier outputs.
Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints
Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems.
Universal Adaptive Control of Nonlinear Systems
This work develops a new direct adaptive control framework that extends the certainty equivalence principle to general nonlinear systems with unmatched model uncertainties.
Adaptive Variants of Optimal Feedback Policies
The stable combination of optimal feedback policies with online learning is studied in a new control-theoretic framework for uncertain nonlinear systems.
Unmatched Control Barrier Functions: Certainty Equivalence Adaptive Safety
This work applies universal adaptive control to control barrier functions to achieve forward invariance of a safe set despite the presence of unmatched parametric uncertainties.
Adaptive Robust Control Contraction Metrics: Transient Bounds in Adaptive Control with Unmatched Uncertainties
This work presents a new sufficient condition for synthesizing nonlinear controllers that yield bounded closed-loop tracking error transients despite the presence of unmatched uncertainties that are concurrently being learned online.
Dynamic Adaptation Gains for Nonlinear Systems with Unmatched Uncertainties
We present a new direct adaptive control approach for nonlinear systems with unmatched and matched uncertainties.
