activity
20162024
most citedDifferentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety Filter

10 citations · 15 across the 17 of their papers we have counts for

collaborators

17 papers

cs.RO2024

Time-Optimal Planning for Long-Range Quadrotor Flights: An Automatic Optimal Synthesis Approach

Chao Qin, Jingxiang Chen, Yifan Lin +3

Time-critical tasks such as drone racing typically cover large operation areas. However, it is difficult and computationally intensive for current time-optimal motion planners to a…

cs.RO2024

Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments

Jingxing Qian, Siqi Zhou, Nicholas Jianrui Ren +2

Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guar…

eess.SY2024

Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters

Lukas Brunke, Siqi Zhou, Mingxuan Che +1

Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement le…

cs.RO2023

AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space

Vivek K. Adajania, Siqi Zhou, Arun Kumar Singh +1

Quadrotor motion planning in complex environments leverage the concept of safe flight corridor (SFC) to facilitate static obstacle avoidance. Typically, SFCs are constructed throug…

cs.RO2023

Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon

Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig

Learning-based controllers have demonstrated superior performance compared to classical controllers in various tasks. However, providing safety guarantees is not trivial. Safety, t…

cs.CV2023

Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors

Ziwei Liao, Jun Yang, Jingxing Qian +2

3D object-level mapping is a fundamental problem in robotics, which is especially challenging when object CAD models are unavailable during inference. In this work, we propose a fr…