Presents a nonlinear actuator-aware safety filter using high-relative-degree collision cone exponential CBFs and a backup CBF on 3DGS, claiming 47% less jerk and 2.25x faster runtime than prior 3DGS filters.
Champion-level drone racing using deep reinforce- ment learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
aerial-autonomy-stack is a ROS2-based open-source framework that supports faster-than-real-time simulation of complete perception-to-action drone autonomy pipelines while remaining agnostic to PX4 and ArduPilot autopilots.
citing papers explorer
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FastBridge: Closing the Model-Based Realization Gap in Safety Filters on 3D Gaussian Splatting for Fast Quadrotor Flight
Presents a nonlinear actuator-aware safety filter using high-relative-degree collision cone exponential CBFs and a backup CBF on 3DGS, claiming 47% less jerk and 2.25x faster runtime than prior 3DGS filters.
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Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
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aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones
aerial-autonomy-stack is a ROS2-based open-source framework that supports faster-than-real-time simulation of complete perception-to-action drone autonomy pipelines while remaining agnostic to PX4 and ArduPilot autopilots.